System

Abstract

A system is provided.SOLUTION: A means for acquiring educational course data from an Ministry of Education, Culture, Sports, Science and Technology or other educational institutions, a means for summarizing the acquired educational course data using a natural language process technology, a means for automatically generating a slide material based on the summarized educational course data, a means for transmitting the generated slide material to an expert and receiving feedback, a means for correcting the slide material based on the feedback of the expert, and a means for uploading the class recorded by the selected teacher to an online platform; The system includes a means for archive distribution, a means for creating test questions based on learning contents in a generation AI and also providing answers, a means for a user to transmit questions through an educational platform, and a means for generating answers to the transmitted questions in real time by using a AI model.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] The traditional education system faces many challenges, including uniform education, students who end up skipping school, and teacher shortages. In particular, students who skip school are unable to attend classes, and providing a high-quality curriculum requires a great deal of effort and time. It is also difficult to create tests and respond quickly to student questions. There is a need to solve these challenges and provide a more flexible and efficient education system. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a means for acquiring curriculum data and summarizing it using natural language processing technology, a means for automatically generating slide decks based on the summarized curriculum data, a means for sending the slide decks to experts and receiving feedback, a means for revising the slide decks based on the experts' feedback, a means for selected teachers to upload recorded lessons to an online platform and distribute them as an archive, a means for creating test questions based on the learning content using a generative AI and providing answers, a means for users to send questions through an educational platform, and a means for generating answers to the sent questions in real time using an AI model.

[0006] This system not only improves the quality of education, but also effectively addresses the issues of students not attending school and teacher shortages by enabling learning to take place anywhere and at any time. It also includes a means to automatically add graphs and charts to slide decks, and a means to generate and provide users with links to archived lesson videos, providing a more inclusive and accessible educational environment.

[0007] "Curriculum Data" means data that includes the content of the curriculum, learning objectives, and records of learning activities offered by an educational institution.

[0008] "Natural language processing technology" is a technology that enables computers to understand, generate, and summarize human language.

[0009] A summary is a concise summary of the main points or concepts from a longer piece of text or data.

[0010] "Slide materials" are presentation-style documents used to visually present educational content.

[0011] An "expert" is someone who has advanced knowledge and experience in a particular field and is a recognized expert in that field.

[0012] "Feedback" refers to comments such as opinions, evaluations, and areas for improvement regarding the information or materials provided.

[0013] An "online platform" is a web-based system for providing and accessing content and services over the Internet.

[0014] "Archive distribution" is a method of storing recorded lessons and content online so that they can be accessed later.

[0015] "Generative AI" is an artificial intelligence technology that uses algorithms to automatically generate content such as test questions and answers.

[0016] A "question" is an inquiry submitted by a user to resolve any doubts or uncertainties they may have regarding the content of their study.

[0017] "Generating answers in real time" means using artificial intelligence to create answers to users' questions in order to respond immediately.

[0018] A "link" is a URL or identifier that provides access to online content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0027] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0040] The present invention relates to a system for digitizing educational curriculum data and providing high-quality educational content. The program processing of this system is explained below in natural language.

[0041] The server's role begins by obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions and summarizing it using natural language processing technology. Specifically, the server sends an API request to download curriculum data, analyzes it using natural language processing algorithms, extracts key concepts and learning objectives, and generates a summary text. For example, the curriculum section on "Solving Linear Equations" might be summarized as "Basic Solutions and Applications of Linear Equations."

[0042] The server then automatically generates slides based on the summarized curriculum. Predefined slide templates are used, and the summary data is inserted into each template. Graphs and charts are also automatically generated based on the numerical data and added to the slides. For example, a slide containing step-by-step instructions and charts for solving a linear equation can be generated.

[0043] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step does not provide enough concrete examples," and the slides are then modified based on that feedback.

[0044] In addition, selected teachers will record educational content and upload the video files to the server, which will then archive the video files on the online platform for users to access at any time. For example, a video lesson on solving linear equations will be uploaded and made available for users to watch.

[0045] Test questions based on the learning content are automatically created by the server using a generation AI. The questions also include answers, which users can use as practice questions. As a specific example, the problem "Solve the following linear equation" and its answer are generated.

[0046] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0047] In this way, the system of the present invention efficiently digitizes curriculum data and makes it possible to provide high-quality educational content, which not only effectively addresses the issues of students not attending school and teacher shortages, but also enables learning regardless of location or time.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending API requests to download the data or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[0051] Step 2:

[0052] The server then uses natural language processing techniques to summarize the acquired curriculum data. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. The summarized data is then stored back in the database.

[0053] Step 3:

[0054] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded, and the summary content is inserted in the appropriate positions. Graphs and charts are also generated and added to the slides as needed.

[0055] Step 4:

[0056] The server sends the generated slides to the expert via the terminal, who reviews the slides and provides feedback. This feedback is then sent from the terminal to the server.

[0057] Step 5:

[0058] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved and the server proceeds to the next step.

[0059] Step 6:

[0060] Selected teachers record educational content and upload the video files to a server via their devices, where they are saved in a specific format.

[0061] Step 7:

[0062] The server archives the uploaded video files on an online platform, and a link is generated and saved for users to access at any time.

[0063] Step 8:

[0064] The server uses AI to generate test questions based on the learning content, and answers are automatically generated for the generated test questions and stored in a database.

[0065] Step 9:

[0066] Users submit questions through the educational platform, which are entered in text format and sent to the server.

[0067] Step 10:

[0068] The server receives questions sent by users and uses AI models to generate answers in real time, which are then returned to the users.

[0069] The above are the specific processing steps performed by the server, the terminal, and the user.

[0070] Example 1

[0071] 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."

[0072] In today's educational environment, improving the quality of educational content requires efficient digitization of educational data and the effective creation of teaching materials based on that data. However, manually processing large amounts of educational data and creating appropriate teaching materials is a major challenge for teachers and educational institutions, requiring time and effort. Furthermore, there is a need for efficient means to provide high-quality education regardless of location or time, especially for students who are absent from school due to teacher shortages.

[0073] 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.

[0074] In this invention, the server includes: means for acquiring educational data from educational institutions; means for summarizing the acquired educational data using natural language processing technology; means for automatically generating slides based on the summarized educational data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as archives; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; and means for generating answers to the submitted questions in real time using an AI model. This enables efficient digitization of educational data and the provision of high-quality educational content.

[0075] An "educational institution" is any public or private organization that provides education-related data or information.

[0076] "Educational Data" means information or data related to education, such as curriculum, learning objectives, teaching materials, and test questions.

[0077] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0078] "Slide materials" are electronic presentation materials for visually presenting educational content.

[0079] An "expert" is someone who has advanced knowledge and skills in a particular field.

[0080] "Feedback" is the act of providing opinions or evaluations of information or services provided.

[0081] An "online platform" is a basic system for providing and sharing various services and content via the Internet.

[0082] "Archive distribution" refers to the saving and distribution of recorded content so that it can be viewed at a later date.

[0083] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[0084] A "test item" is a question or problem used to assess a learner's understanding or knowledge.

[0085] An "answer" is a correct response or interpretation to a test question or question.

[0086] An "education platform" is an online system that provides education-related services and content in a centralized manner.

[0087] An "AI model" is a complex computational model that combines artificial intelligence algorithms and analytical methods.

[0088] "Real time" refers to a short period of time in which a response is returned immediately after the user does something.

[0089] This invention is a system for digitizing educational data and providing high-quality educational content. This system automates the process of acquiring educational data from educational institutions and summarizing, processing, and distributing it.

[0090] At the heart of the system is a server, which performs multiple functions. The server has a means of acquiring educational data from educational institutions. This means, for example, downloading the educational data using an API request. The server then summarizes the acquired educational data using natural language processing techniques. To do this, it uses a generative AI model to extract and concisely summarize the important information in the educational data.

[0091] For example, the server might send the following prompt to the generative AI model:

[0092] Summarize the curriculum data and extract key concepts and learning objectives. As a specific example, summarize how to solve linear equations.

[0093] The server then automatically generates slides based on the summarized educational data. During this process, the summary data is inserted into a predefined slide template, and graphs and diagrams are automatically generated based on the numerical data. The slides are then sent to experts via the device, who review and provide feedback. As a concrete example, consider a scenario in which a mathematics teacher gives feedback such as, "This solution step does not provide enough concrete examples."

[0094] The server receives feedback, modifies the slides, and sends them back to the experts, improving the quality of the materials provided. Additionally, selected instructors record educational content and upload the video files from their devices to the server. The server then archives the video files on an online platform, making them accessible to users at any time.

[0095] The server also uses a generative AI model to automatically generate test questions based on the learning content. Examples of prompts sent by the server include:

[0096] Create test questions based on the following learning: Provide specific questions and solutions related to solving linear equations.

[0097] Users can submit questions through the educational platform, and the server generates answers in real time using AI models. For example, if a user asks, "I don't really know how to solve this linear equation," the server will instantly provide an explanation and sample answer.

[0098] This will enable the efficient digitization of educational data and the provision of high-quality educational content, and will also strengthen educational support for students who are experiencing teacher shortages or who are not attending school.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] Acquisition of educational data

[0102] The server obtains educational data from the educational institution.

[0103] Specific operation: The server sends an API request to download educational data.

[0104] Input: API request (e.g., GET / education_curriculum)

[0105] Output: Retrieved educational data (e.g., detailed educational course information)

[0106] Step 2:

[0107] Education Data Summary

[0108] The educational data acquired by the server is summarized using natural language processing technology.

[0109] Specific operation: The server sends prompts to the generative AI model to extract key concepts and learning objectives.

[0110] Input: Captured educational data, prompt (e.g., "Please summarize the educational course data and extract key concepts and learning objectives.")

[0111] Output: Summary text (e.g. "Basic methods for solving linear equations and examples of their applications")

[0112] Step 3:

[0113] Automatic generation of slides

[0114] The server automatically generates slide materials based on the summarized educational data.

[0115] What it does: Inserts summary data into predefined slide templates and automatically generates graphs and charts.

[0116] Input: Summary text, slide template

[0117] Output: Generated slide deck (e.g., slide on solving linear equations)

[0118] Step 4:

[0119] Slide deck review and feedback

[0120] The generated slide materials are sent to the expert via the terminal.

[0121] Concrete action: An expert reviews a slide deck and sends feedback from the device to the server. For example, a math teacher requests the addition of a concrete example.

[0122] Input: Generated slide deck, expert feedback

[0123] Output: Slide deck with feedback

[0124] Step 5:

[0125] Editing slide materials

[0126] The server modifies the slide deck based on the expert feedback.

[0127] Specific operations: Modify templates and generate additional graphs and charts.

[0128] Input: Slide deck with feedback

[0129] Output: Revised slide deck

[0130] Step 6:

[0131] Recording and archiving educational content

[0132] Selected teachers use the devices to record educational content and upload the video files to the server.

[0133] What it does: Teachers record videos on their devices and upload them to a server, which then archives the videos on an online platform.

[0134] Input: Recorded lesson video

[0135] Output: Video archived on an online platform

[0136] Step 7:

[0137] Automatic generation of test questions

[0138] The server automatically creates test questions based on the learning content using a generative AI model.

[0139] Specific operation: The server sends a prompt to the generative AI model, which generates test questions and their answers.

[0140] Input: Study content, prompt (e.g. "Please create test questions based on the following study content")

[0141] Output: Generated test questions and their answers

[0142] Step 8:

[0143] User questions and real-time answers

[0144] A user submits a question through the education platform.

[0145] How it works: The user enters a question and presses the submit button. The server then uses the AI ​​model to analyze the question and generate an answer in real time.

[0146] Input: User question, prompt (e.g., "I'm not sure how to solve this linear equation.")

[0147] Output: Generated answers (e.g. explanations and sample answers)

[0148] Each of the above steps will enable the efficient digitization of educational data and the provision of high-quality educational content.

[0149] (Application example 1)

[0150] 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."

[0151] In modern education, efficient provision of high-quality educational content and deepening student understanding requires the organization of vast amounts of educational data and its presentation in an easy-to-understand manner. However, traditional methods require time-consuming manual creation of materials and feedback, making real-time support difficult. Furthermore, a lack of universally accessible educational platforms means that learning environments remain dependent on specific educational institutions. To solve these problems, an educational support system that utilizes advanced automation technology and the Internet is needed.

[0152] 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.

[0153] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as an archive; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means installed on a smartphone or smart glasses for delivering educational content and providing feedback. This allows for the efficient provision of high-quality educational content, rapid reflection of feedback, and the advancement of learning regardless of location or time.

[0154] "Curriculum data" refers to information stored in digital format that contains the contents of curricula and lesson plans established by educational institutions.

[0155] "Natural language processing technology" refers to technology that processes human language using computers, and includes systems that understand and generate text.

[0156] "Slide materials" are presentation materials consisting of a series of slides used to visually explain educational content.

[0157] An "expert" is a person who has specialized knowledge and experience in a particular field.

[0158] "Feedback" means evaluation and suggestions for improvement provided based on information and results received.

[0159] An "online platform" is a system that serves as the foundation for providing services and content via the Internet.

[0160] "Archive distribution" refers to storing recorded content so that users can access and view it later.

[0161] "Generative AI" is a system that automatically generates text and questions using artificial intelligence technology.

[0162] An "AI model" is an artificial intelligence algorithm and structure used to learn from data and perform specific tasks.

[0163] A "smartphone" is a mobile phone that has advanced information processing capabilities and is capable of running applications.

[0164] "Smart glasses" are wearable devices that have the ability to display information within the field of view and realize augmented reality.

[0165] "Educational content" refers to digital learning materials such as textbooks, videos, and workbooks used to provide education to learners.

[0166] The present invention relates to a system for distributing and providing feedback on educational content. Specific embodiments of this system will be described below.

[0167] System Configuration

[0168] The system consists of a server, terminals (smartphones or smart glasses), and users. The server is responsible for acquiring curriculum data from educational institutions and processing and distributing it. Meanwhile, the terminals function as an interface for users to receive and study educational content.

[0169] Hardware and Software

[0170] The server will be equipped with hardware with high-speed data processing capabilities and the following software:

[0171] Natural Language Processing Technology: Hugging Face transformers library

[0172] Data acquisition: requests library (Python)

[0173] Slide generation: python-pptx library

[0174] Question answering system: Hugging Face transformers library and pre-trained models (e.g., distilbert-base-cased-distilled-squad)

[0175] The smartphones and smart glasses used as terminals also have a corresponding application installed, which has the function of receiving and displaying educational content.

[0176] Overview of program processing

[0177] Acquisition and summary of curriculum data

[0178] First, the server retrieves curriculum data from educational institutions using an API. This data is then summarized using natural language processing techniques to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" is summarized as "Basic Solutions and Applications of Linear Equations."

[0179] Automatic generation of slides

[0180] The system then automatically generates slide decks based on the summarized data, using predefined slide templates to populate them with the appropriate summary data, graphs, and charts—for example, slides containing step-by-step instructions and charts for solving linear equations.

[0181] Feedback and Corrections

[0182] The generated slides are sent to experts via the terminal. The server improves the slides by receiving feedback from the experts. For example, if a math teacher gives feedback such as "This solution step does not include enough concrete examples," the slides are revised based on that feedback.

[0183] Distribution and archiving of lesson videos

[0184] In addition, videos of lessons recorded by selected instructors will be uploaded to the server and distributed through the online platform. Users can access these videos at any time. For example, a lesson video on solving linear equations will be uploaded and made available for users to watch.

[0185] Automatic generation of test questions

[0186] Test questions based on the learning content are automatically created using generative AI. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer will be generated.

[0187] Real-time question answering

[0188] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0189] Examples of prompt statements

[0190] Examples of specific prompts include the following:

[0191] Example of input prompt:

[0192] Summarize what you learned about "Solving Linear Equations."

[0193] Example of generated results:

[0194] This article summarizes the basic methods and applications of solving linear equations. A linear equation is an equation that is calculated only once for one variable... (summary below)

[0195] The above is an embodiment of the present invention, which makes it possible to efficiently provide high-quality educational content, quickly incorporate feedback, and advance learning without being restricted by location or time.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The server retrieves curriculum data from the educational institution. The server uses an API to request and receive the required data, while verifying the accuracy and completeness of the data. The input is data from the educational institution's API endpoint, and the output is the retrieved curriculum data.

[0199] Step 2:

[0200] The server summarizes the acquired curriculum data using natural language processing techniques. The server uses natural language processing tools such as Hugging Face's transformers library to extract key concepts and learning objectives. The input is the acquired curriculum data, and the output is summarized text data.

[0201] Step 3:

[0202] The server automatically generates slide presentations based on the summarized data. It uses the python-pptx library to insert summarized data, graphs, and figures into predefined slide templates. The input is the summarized text data and data visualization information, and the output is the generated slide presentation file.

[0203] Step 4:

[0204] The server sends the generated slides to the terminal and requests the expert to review them. The expert reviews the slides via the terminal and provides feedback. The input is the generated slides, and the output is the feedback from the expert.

[0205] Step 5:

[0206] The server receives the expert's feedback and modifies the slide deck based on it. The server adjusts the slide template and content according to the expert's opinions and suggestions. The input is the expert's feedback and the original slide deck, and the output is the modified slide deck.

[0207] Step 6:

[0208] Selected faculty members record their classes and upload the video files to a server, which stores them in a digital archive and distributes them through an online platform. The input is the recorded class video, and the output is the archived video file and its distribution link.

[0209] Step 7:

[0210] The server generates test questions based on the learning content. It uses a generative AI model to automatically create formatted test questions and their answers. The input is the learning content and summary data, and the output is the generated test questions and their answers.

[0211] Step 8:

[0212] Users submit questions through the educational platform. The submitted questions are received by the server, which uses an AI model to analyze the questions and generate answers in real time. The input is the user's question, and the output is the generated answer.

[0213] Step 9:

[0214] The server sends the generated answer to the user through the educational platform. The terminal displays the answer to the user in real time and provides necessary educational support. The input is the generated answer, and the output is the answer content provided to the user.

[0215] The above are the specific processing steps of the system for implementing the invention. In this way, high-quality educational content and rapid feedback are provided, helping learners to deepen their understanding.

[0216] 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.

[0217] This invention relates to a system for digitizing educational curriculum data and providing high-quality educational content, and further optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions. Below, the program processing of this system is explained in natural language.

[0218] The server's role is to obtain curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. The data is downloaded by sending an API request or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[0219] The server then summarizes the acquired curriculum data using natural language processing techniques. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored back in the database.

[0220] The server then automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted in the appropriate locations. Graphs and charts are also generated and added to the slides as needed. For example, a slide containing step-by-step instructions and charts for solving linear equations can be generated.

[0221] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step lacks concrete examples," and the slides are then modified based on that feedback.

[0222] Next, the selected teachers record the educational content and upload the video files to the server via their devices. The server then archives the video files on an online platform so that users can access them at any time. For example, a video lesson on solving linear equations can be uploaded and made available for users to watch.

[0223] The server automatically generates test questions based on the learning content using AI generation. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer can be generated.

[0224] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0225] Furthermore, an emotion engine is built into the system to recognize the user's emotions. The server analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. For example, if the user is having difficulty understanding something, the emotion engine will use that information to generate and provide additional explanations or encouraging messages.

[0226] Based on the emotional data, the server can individually adjust the progress of the learning curriculum and provide the user with an optimal learning environment. This increases motivation and enables efficient learning. The emotional engine automatically generates messages to increase the user's motivation, providing personalized support according to the learner's emotional state.

[0227] With the above configuration, the present invention can improve the quality of education and effectively address the issues of students not attending school and teacher shortages. Furthermore, by recognizing users' emotions and optimizing the learning experience, it is possible to provide a more fulfilling educational environment.

[0228] The processing flow will be explained below.

[0229] Step 1:

[0230] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending an API request to download the data or by importing a periodically updated CSV file from a defined location. The obtained data is then stored in a database.

[0231] Step 2:

[0232] The server summarizes the acquired curriculum data using natural language processing techniques. Topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored in a database.

[0233] Step 3:

[0234] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted into the appropriate slides. Furthermore, the server generates necessary graphs and charts based on the numerical data and incorporates them into the slides. For example, it generates slides that include step-by-step instructions on how to solve a linear equation and accompanying graphs to visually complement the explanation.

[0235] Step 4:

[0236] The generated slides are sent to the expert via the terminal, which then sends them to the expert's email address or via a dedicated review system. The slides are then sent via email or uploaded for review on a dedicated platform.

[0237] Step 5:

[0238] Experts review the slides and provide feedback, which is sent back to the system via the device. For example, a math teacher might say, "This solution step doesn't provide enough concrete examples." That feedback is then sent to the server via the device.

[0239] Step 6:

[0240] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved in the database and proceed to the next step.

[0241] Step 7:

[0242] Selected teachers record educational content. The recorded lesson videos are uploaded to the server via their devices. The video files are saved in a specific format (e.g., MP4).

[0243] Step 8:

[0244] The server archives the uploaded video files on the online platform, and a link is generated and saved for users to access at any time. For example, a video lesson on solving linear equations can be uploaded and viewed by users.

[0245] Step 9:

[0246] The server automatically generates test questions based on the learning content using AI. The generated test questions also include the answers, which are saved in the database. For example, a question such as "Solve the following linear equation" and its answer are generated.

[0247] Step 10:

[0248] A user submits a question through the educational platform. The question is entered in text format and sent to the server. For example, a user submits a question such as, "I don't really understand how to solve this linear equation."

[0249] Step 11:

[0250] The server receives questions sent by users and uses an AI model to generate answers in real time. The generated answers are then returned to the users. For example, the AI ​​returns sample answers and detailed explanations in real time.

[0251] Step 12:

[0252] The server uses an emotion engine that recognizes the user's emotions to analyze the user's learning status in real time, for example, by detecting and analyzing the user's facial expressions and tone of voice through a camera or microphone.

[0253] Step 13:

[0254] The server adjusts the progress of the learning curriculum individually based on the emotion data. If the user has difficulty understanding, it provides additional explanations and supplementary materials. For example, if the user is confused, it provides additional lecture videos with supplementary explanations.

[0255] Step 14:

[0256] Based on the emotional data generated by the emotion engine, a motivational message is automatically generated and sent to the user. For example, a message such as "You're doing great! You're almost at the next stage" can be generated and sent to the user.

[0257] The above are the specific processing steps performed by the server, the terminal, and the user.

[0258] Example 2

[0259] 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."

[0260] In modern education, effectively providing high-quality educational content is a key challenge. However, traditional educational systems lack the means to efficiently digitize curriculum data, summarize, create materials, incorporate feedback, distribute video lectures, generate test questions, respond to questions in real time, and recognize and provide feedback to learners. As a result, the quality of education declines and learners' motivation decreases. Furthermore, it is difficult to provide a data-based, personalized learning curriculum.

[0261] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0262] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating presentation materials based on the summarized curriculum data; means for sending the generated presentation materials to experts and receiving feedback; means for revising the presentation materials based on the expert feedback; means for uploading lectures recorded by selected teachers to an online platform and distributing them as archives; means for creating test questions based on learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; means for providing feedback and learning materials according to the user's learning status using an emotion engine that recognizes the user's emotions; and means for individually adjusting the learning curriculum based on the emotion data. This makes it possible to efficiently and effectively provide educational content and create a personalized learning environment.

[0263] "Curriculum Data" refers to information about the curriculum provided by an educational institution, including, specifically, subjects, learning objectives, topics, learning procedures, etc.

[0264] "Natural language processing technology" refers to the technology of processing human language using a computer, and specifically includes text summarization, topic modeling, text classification, etc.

[0265] "Presentation materials" are materials that summarize the learning content in a visually easy-to-understand manner, and are generally in the form of slides or visual aids.

[0266] An "expert" is someone who has deep knowledge and experience in a particular field, and typically refers to educators and academics.

[0267] An "online platform" is a platform for providing services and content via the Internet, and in the education field it includes the distribution of video lessons and learning management systems.

[0268] "Generative AI" refers to artificial intelligence that generates text and images, specifically natural language generation and image generation.

[0269] An "AI model" is an artificial intelligence framework that uses machine learning or deep learning to perform specific tasks, such as classification, prediction, and generation.

[0270] An "emotion engine" is a system for analyzing a user's emotional state in real time, specifically identifying emotions using facial expression and voice recognition technology.

[0271] An "educational platform" is a web application that manages and provides education-related services and content in an integrated manner, and serves as the foundation for users to access and use learning content.

[0272] A "learning curriculum" is a plan or schedule that defines the progress of learning, and specifically includes the learning content, learning goals, and time allocation for each subject.

[0273] The present invention is a system for digitizing educational curriculum data and providing high-quality educational content, and in particular, optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions.

[0274] System Overview

[0275] This system consists of three elements: a server, a terminal, and a user. The server plays the main role of acquiring, processing, storing, summarizing, and generating data, while the terminal acts as data input and user interface. The user is a consumer of educational content, learning and providing feedback.

[0276] Hardware and Software Configuration

[0277] Server: A high-performance server is used for data processing and storage. MySQL is used as the database, and BERT and T5 models are used for natural language processing. Google Slides API is used to generate presentation materials, and Slack API is used to collect feedback.

[0278] Devices: Computers, tablets, and smartphones are used as interfaces between users and the server. Web applications are used for video recording and uploading.

[0279] Users: Consume content through the educational platform and use Microsoft Azure's Emotion API for emotion recognition technology.

[0280] Program processing

[0281] Data Acquisition and Summarization

[0282] The server acquires curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and educational institutions through API requests and CSV file imports. The acquired data is checked for consistency and stored in a database.

[0283] The server uses natural language processing techniques (BERT topic model and T5 summarization model) to summarize the curriculum data and extract key concepts and learning objectives.

[0284] Example: Summarize the section "Solving Linear Equations" as "Basic Solutions of Linear Equations and Applications."

[0285] Automatic generation of presentation materials

[0286] The server uses the summary data to generate presentation materials via the Google Slides API, and if necessary, creates graphs and charts using the D3.js library and adds them to the materials.

[0287] For example: A slide includes "Step-by-step instructions on solving linear equations and corresponding graphs."

[0288] Feedback and Corrections

[0289] The generated presentation materials are sent to experts via the terminal, and feedback is collected using the Slack API, and the materials are updated based on the feedback using an automatic correction script.

[0290] For example, if the feedback is that the solution steps do not contain enough concrete examples, insert additional concrete examples.

[0291] Recording and distributing educational content

[0292] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then compress the videos, upload them to the platform using the YouTube API, and generate an access link.

[0293] Example: A "Lesson video on solving linear equations" is uploaded and made available for users to watch.

[0294] Automatic generation of test questions

[0295] The server uses generative AI (e.g., GPT) to automatically generate test questions and answers based on the learning content.

[0296] Example: The problem is "Solve the following linear equation \(2x + 3 = 7\)" and the answer is "\( x = 2 \)".

[0297] User questions and AI answers

[0298] Users submit questions through the educational platform, and the server uses an AI model to analyze the questions and generate answers in real time.

[0299] Example: In response to the prompt "I don't really know how to solve a linear equation," provide detailed solution steps along with concrete examples.

[0300] Emotion Recognition and Feedback

[0301] The server uses an emotion engine to analyze emotions from the user's facial expressions and voice in real time while they are learning, and provides feedback and additional learning materials.

[0302] For example, if the user shows signs of confusion, the emotion engine can generate and provide additional explanations or encouraging messages.

[0303] Individualized learning curriculum

[0304] The server adjusts the learning curriculum based on the emotion data, providing additional review materials to users who are falling behind, and generating encouraging messages from the emotion engine to motivate users.

[0305] The present invention makes it possible to provide educational content efficiently and effectively and to create a personalized learning environment.

[0306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0307] Step 1:

[0308] Acquisition of curriculum data

[0309] The server sends a request to the API endpoint provided by the educational institution to obtain the latest curriculum data. The data is received as a CSV file or API response.

[0310] Input: API request or CSV file path

[0311] Output: Curriculum data (JSON or CSV format)

[0312] Specific operation: The server sends a request to the API as a scheduled task every morning at 8:00 to receive new data, which is then stored in the database after undergoing data integrity checks.

[0313] Step 2:

[0314] Data Summarization

[0315] The server summarizes the acquired educational curriculum data using natural language processing techniques, such as the BERT topic model and the T5 summarization model.

[0316] Input: Curriculum data

[0317] Output: Summarized curriculum data

[0318] What it does: The server uses newly saved data as triggers to run NLP algorithms and summarize key concepts and learning objectives, for example summarizing the "Solving Linear Equations" section as "Basic Solutions and Applications of Linear Equations."

[0319] Step 3:

[0320] Automatic generation of presentation materials

[0321] The server automatically generates presentation materials using the Google Slides API based on the summarized curriculum data.

[0322] Input: summarized curriculum data

[0323] Output: Generated presentation materials (Google Slides format)

[0324] What happens: The server loads the template slides, inserts the summary data into the appropriate slides, and optionally creates graphs and charts using the D3.js library and adds them to the slides.

[0325] Step 4:

[0326] Sending and getting feedback

[0327] The server sends the generated presentation materials to the experts, receives feedback, and posts the material link to a dedicated channel using the Slack API.

[0328] Input: Generated presentation materials

[0329] Output: Expert feedback

[0330] How it works: The server automatically posts a link to the document in a Slack channel, and experts can comment on it in Google Slides. The comments are sent to the server in real time.

[0331] Step 5:

[0332] Modifying materials based on feedback

[0333] The server then modifies the presentation materials based on the received feedback, and executes an automatic correction script depending on the feedback.

[0334] Input: Expert feedback

[0335] Output: Revised presentation

[0336] Specific actions: For example, if the feedback is that "the solution steps do not contain enough concrete examples," the server executes a correction script to add concrete examples and updates the materials.

[0337] Step 6:

[0338] Recording and distributing educational content

[0339] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then upload the videos to an online platform for distribution and archiving.

[0340] Input: Recorded lesson video

[0341] Output: Link to a video of the lesson that can be distributed via an online platform

[0342] How it works: Teachers record videos using a web application and submit them through a dedicated upload form. The server compresses the video and uploads it using the YouTube API. A link is automatically generated and users can access it.

[0343] Step 7:

[0344] Automatic generation of test questions

[0345] The server uses a generative AI model (e.g., GPT) to automatically generate test questions based on the learning content, including the answers.

[0346] Input: Curriculum data

[0347] Output: Test questions and their answers

[0348] How it works: The server uses a generative AI model to generate test questions based on the summary data. For example, it generates the question "Solve the following linear equation \(2x + 3 = 7\)" and the answer "\(x = 2\)."

[0349] Step 8:

[0350] User questions and AI answers

[0351] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time.

[0352] Input: User question

[0353] Output: Real-time answers by AI

[0354] Specific operation: When a user inputs a question into the educational platform, the server immediately analyzes the question and generates an appropriate answer. Prompt sentence: For example, in response to "I don't really understand how to solve a linear equation," the server responds with a concrete example and steps to solve the equation.

[0355] Step 9:

[0356] Emotion Recognition and Feedback

[0357] The server recognizes the user's emotions from their facial expressions and voice, and provides feedback and learning materials based on that information, using Microsoft Azure's Emotion API.

[0358] Input: User's facial expression data, voice data

[0359] Output: Feedback and learning materials based on emotion recognition

[0360] Specific operation: The server analyzes the user's emotional data in real time and generates additional explanations or encouraging messages if the user has difficulty understanding. For example, if a confused expression is recognized, the system will provide a more detailed explanation.

[0361] Step 10:

[0362] Individualized learning curriculum

[0363] The server adjusts the learning curriculum based on the user's emotional data, providing review materials and encouraging messages according to the user's progress and level of understanding.

[0364] Input: Emotion data, learning progress data

[0365] Output: personalized learning curriculum and encouragement messages

[0366] Specific operation: The server uses an emotion engine to analyze the user's motivation, and if progress is slow, it provides more review materials and generates and provides appropriate encouraging messages.

[0367] This allows the system to efficiently and effectively deliver educational content and personalize and optimize the user's learning experience.

[0368] (Application example 2)

[0369] 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."

[0370] Traditional educational systems face many challenges in maintaining the quality of educational content and managing learner motivation. It is particularly difficult to efficiently process large volumes of curriculum data and create high-quality teaching materials. It is also difficult to grasp learners' emotions and comprehension levels in real time and provide appropriate feedback based on that information. These challenges often result in a decline in learning motivation and a drop in learning efficiency.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) or other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected educators to an online platform and archiving them; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means for individually adjusting the learning curriculum using an emotion engine that recognizes the user's emotions. This enables improved quality of educational content and management of learner motivation.

[0372] "Ministry of Education, Culture, Sports, Science and Technology and other educational institutions" refers to the government ministries and agencies responsible for the administration of education, culture, sports, science and technology in Japan and other educational institutions.

[0373] "Curriculum data" refers to official curriculum and teaching plan data provided by the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions.

[0374] "Natural language processing technology" is a technology that allows machines to analyze and understand human language, and is used to summarize data and extract information.

[0375] "Slide materials" are materials used to visually present information in a presentation format.

[0376] An "expert" is someone with specialized knowledge and experience.

[0377] "Feedback" refers to providing evaluations and opinions.

[0378] "Correction" refers to correcting or revising deficiencies or improvements.

[0379] "Selected Educators" refers to education professionals who are selected based on specific criteria.

[0380] "Recording" refers to the act of recording video and audio.

[0381] An "online platform" is a system that provides certain services via the Internet.

[0382] "Archive distribution" refers to storing recorded content and making it available for later viewing.

[0383] "Test questions" refer to questions or tasks used to measure comprehension of the learning content.

[0384] "Generative AI" refers to systems that use artificial intelligence techniques to automatically perform specific tasks.

[0385] "Answer" refers to the correct response to a question.

[0386] A "question" is an inquiry for information or help.

[0387] An "AI model" refers to a model for performing data analysis and inference based on artificial intelligence.

[0388] "Real time" refers to immediate action or response.

[0389] An "emotion engine" is a system that analyzes a user's emotional state and provides appropriate feedback based on that.

[0390] A "curriculum" is an educational content and plan that is structured around specific learning objectives.

[0391] "Individually tailored" refers to responses and changes that are optimized for each individual.

[0392] The embodiment of the present invention is a system for efficiently generating educational content and optimizing a user's learning experience. Specific procedures for implementing the present invention and the hardware and software used will be described below.

[0393] The server first obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. This data is obtained by sending an API request to download it or by importing a CSV file that is updated periodically. The obtained curriculum data is then stored in a database. The requests library is used to obtain the data.

[0394] Next, we summarize the acquired curriculum data using natural language processing techniques. Specifically, we use the summarizer model from the transformers library. This model is used to summarize large-scale text data and extract important parts of the data.

[0395] The summarized data is automatically entered into slide decks, which are generated using the pptx library and pre-defined templates, and graphs and charts are automatically generated and added to the slides, if needed.

[0396] The generated slides are sent to experts who provide feedback. The experts then send their feedback to the server via their devices, and the server then corrects the slides based on that feedback. This correction includes both manual and automatic corrections.

[0397] Next, the selected educators will record the lessons and upload the video files to the server via their devices. The server will then archive the video files on an online platform for users to access at any time. Users can access the archived lesson videos using the provided link.

[0398] The server also uses AI to automatically generate test questions based on the learning content. These questions also include answers, and users can use them as practice questions. The generated test questions are customized according to the user's learning progress and are provided based on an individual learning plan.

[0399] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model (e.g., QAModel) to generate answers in real time, using the spacy library for natural language analysis.

[0400] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. This engine adjusts the progress of the learning curriculum individually, providing the user with the optimal learning environment.

[0401] For example, if a user submits a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the confusion and provide a detailed explanation, which will increase motivation and enable more efficient learning.

[0402] Example prompt sentence:

[0403] "Please tell me how to solve the following linear equation."

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1:

[0406] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions through API requests or periodically updated CSV files. The obtained data is in raw format (JSON or CSV) and is stored in a database. The input is data from educational institutions, and the output is curriculum data stored in the database.

[0407] Step 2:

[0408] The server summarizes the stored curriculum data using natural language processing techniques. Specifically, it applies the summarizer model from the transformers library to summarize long-form curriculum data into key concepts and learning objectives. The input is the curriculum data stored in the database, and the output is the summarized curriculum data.

[0409] Step 3:

[0410] The server automatically generates slides based on the summarized curriculum data. It uses the pptx library to insert the summary content into a pre-defined template. Graphs and charts are also automatically generated and added as needed. The input is the summarized curriculum data, and the output is the generated slides.

[0411] Step 4:

[0412] The server sends the generated slides to the expert and receives feedback. The expert then writes down their evaluation and opinions, which are then sent back to the server. The input is the generated slides, and the output is the feedback provided by the expert.

[0413] Step 5:

[0414] The server then modifies the slides based on the expert feedback. The modifications incorporate the improvements pointed out, and the data is reprocessed as necessary. The input is the expert feedback, and the output is the modified slides.

[0415] Step 6:

[0416] Selected educators record their lessons and upload the video files to a server via their devices. The server then archives the video files on an online platform and makes them accessible. The input is the recorded lesson video, and the output is the lesson video stored on the online platform.

[0417] Step 7:

[0418] The server automatically creates test questions based on the learning content using generative AI. The generative AI model analyzes the educational curriculum data and generates high-quality test questions and answers based on it. The input is the educational curriculum data, and the output is the generated test questions and answers.

[0419] Step 8:

[0420] Users submit questions through the educational platform. The submitted questions are received by the server and analyzed by the AI ​​model. As a result of the analysis, an appropriate answer is generated in real time. The input is the user's question, and the output is the answer provided by the AI ​​model.

[0421] Step 9:

[0422] The server uses an emotion engine to recognize the user's emotions and individually adjusts the learning curriculum. The emotion engine analyzes the user's emotional data during learning and provides appropriate feedback and learning materials based on that data. The input is the user's emotional data, and the output is an individually adjusted learning curriculum.

[0423] For example, if a user sends a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the user's confusion and provide a detailed explanation, deepening the user's understanding and increasing their motivation to learn.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0439] 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."

[0440] The present invention relates to a system for digitizing educational curriculum data and providing high-quality educational content. The program processing of this system is explained below in natural language.

[0441] The server's role begins by obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions and summarizing it using natural language processing technology. Specifically, the server sends an API request to download curriculum data, analyzes it using natural language processing algorithms, extracts key concepts and learning objectives, and generates a summary text. For example, the curriculum section on "Solving Linear Equations" might be summarized as "Basic Solutions and Applications of Linear Equations."

[0442] The server then automatically generates slides based on the summarized curriculum. Predefined slide templates are used, and the summary data is inserted into each template. Graphs and charts are also automatically generated based on the numerical data and added to the slides. For example, a slide containing step-by-step instructions and charts for solving a linear equation can be generated.

[0443] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step does not provide enough concrete examples," and the slides are then modified based on that feedback.

[0444] In addition, selected teachers will record educational content and upload the video files to the server, which will then archive the video files on the online platform for users to access at any time. For example, a video lesson on solving linear equations will be uploaded and made available for users to watch.

[0445] Test questions based on the learning content are automatically created by the server using a generation AI. The questions also include answers, which users can use as practice questions. As a specific example, the problem "Solve the following linear equation" and its answer are generated.

[0446] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0447] In this way, the system of the present invention efficiently digitizes curriculum data and makes it possible to provide high-quality educational content, which not only effectively addresses the issues of students not attending school and teacher shortages, but also enables learning regardless of location or time.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending API requests to download the data or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[0451] Step 2:

[0452] The server then uses natural language processing techniques to summarize the acquired curriculum data. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. The summarized data is then stored back in the database.

[0453] Step 3:

[0454] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded, and the summary content is inserted in the appropriate positions. Graphs and charts are also generated and added to the slides as needed.

[0455] Step 4:

[0456] The server sends the generated slides to the expert via the terminal, who reviews the slides and provides feedback. This feedback is then sent from the terminal to the server.

[0457] Step 5:

[0458] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved and the server proceeds to the next step.

[0459] Step 6:

[0460] Selected teachers record educational content and upload the video files to a server via their devices, where they are saved in a specific format.

[0461] Step 7:

[0462] The server archives the uploaded video files on an online platform, and a link is generated and saved for users to access at any time.

[0463] Step 8:

[0464] The server uses AI to generate test questions based on the learning content, and answers are automatically generated for the generated test questions and stored in a database.

[0465] Step 9:

[0466] Users submit questions through the educational platform, which are entered in text format and sent to the server.

[0467] Step 10:

[0468] The server receives questions sent by users and uses AI models to generate answers in real time, which are then returned to the users.

[0469] The above are the specific processing steps performed by the server, the terminal, and the user.

[0470] Example 1

[0471] 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."

[0472] In today's educational environment, improving the quality of educational content requires efficient digitization of educational data and the effective creation of teaching materials based on that data. However, manually processing large amounts of educational data and creating appropriate teaching materials is a major challenge for teachers and educational institutions, requiring time and effort. Furthermore, there is a need for efficient means to provide high-quality education regardless of location or time, especially for students who are absent from school due to teacher shortages.

[0473] 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.

[0474] In this invention, the server includes: means for acquiring educational data from educational institutions; means for summarizing the acquired educational data using natural language processing technology; means for automatically generating slides based on the summarized educational data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as archives; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; and means for generating answers to the submitted questions in real time using an AI model. This enables efficient digitization of educational data and the provision of high-quality educational content.

[0475] An "educational institution" is any public or private organization that provides education-related data or information.

[0476] "Educational Data" means information or data related to education, such as curriculum, learning objectives, teaching materials, and test questions.

[0477] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0478] "Slide materials" are electronic presentation materials for visually presenting educational content.

[0479] An "expert" is someone who has advanced knowledge and skills in a particular field.

[0480] "Feedback" is the act of providing opinions or evaluations of information or services provided.

[0481] An "online platform" is a basic system for providing and sharing various services and content via the Internet.

[0482] "Archive distribution" refers to the saving and distribution of recorded content so that it can be viewed at a later date.

[0483] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[0484] A "test item" is a question or problem used to assess a learner's understanding or knowledge.

[0485] An "answer" is a correct response or interpretation to a test question or question.

[0486] An "education platform" is an online system that provides education-related services and content in a centralized manner.

[0487] An "AI model" is a complex computational model that combines artificial intelligence algorithms and analytical methods.

[0488] "Real time" refers to a short period of time in which a response is returned immediately after the user does something.

[0489] This invention is a system for digitizing educational data and providing high-quality educational content. This system automates the process of acquiring educational data from educational institutions and summarizing, processing, and distributing it.

[0490] At the heart of the system is a server, which performs multiple functions. The server has a means of acquiring educational data from educational institutions. This means, for example, downloading the educational data using an API request. The server then summarizes the acquired educational data using natural language processing techniques. To do this, it uses a generative AI model to extract and concisely summarize the important information in the educational data.

[0491] For example, the server might send the following prompt to the generative AI model:

[0492] Summarize the curriculum data and extract key concepts and learning objectives. As a specific example, summarize how to solve linear equations.

[0493] The server then automatically generates slides based on the summarized educational data. During this process, the summary data is inserted into a predefined slide template, and graphs and diagrams are automatically generated based on the numerical data. The slides are then sent to experts via the device, who review and provide feedback. As a concrete example, consider a scenario in which a mathematics teacher gives feedback such as, "This solution step does not provide enough concrete examples."

[0494] The server receives feedback, modifies the slides, and sends them back to the experts, improving the quality of the materials provided. Additionally, selected instructors record educational content and upload the video files from their devices to the server. The server then archives the video files on an online platform, making them accessible to users at any time.

[0495] The server also uses a generative AI model to automatically generate test questions based on the learning content. Examples of prompts sent by the server include:

[0496] Create test questions based on the following learning: Provide specific questions and solutions related to solving linear equations.

[0497] Users can submit questions through the educational platform, and the server generates answers in real time using AI models. For example, if a user asks, "I don't really know how to solve this linear equation," the server will instantly provide an explanation and sample answer.

[0498] This will enable the efficient digitization of educational data and the provision of high-quality educational content, and will also strengthen educational support for students who are experiencing teacher shortages or who are not attending school.

[0499] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0500] Step 1:

[0501] Acquisition of educational data

[0502] The server obtains educational data from the educational institution.

[0503] Specific operation: The server sends an API request to download educational data.

[0504] Input: API request (e.g., GET / education_curriculum)

[0505] Output: Retrieved educational data (e.g., detailed educational course information)

[0506] Step 2:

[0507] Education Data Summary

[0508] The educational data acquired by the server is summarized using natural language processing technology.

[0509] Specific operation: The server sends prompts to the generative AI model to extract key concepts and learning objectives.

[0510] Input: Captured educational data, prompt (e.g., "Please summarize the educational course data and extract key concepts and learning objectives.")

[0511] Output: Summary text (e.g. "Basic methods for solving linear equations and examples of their applications")

[0512] Step 3:

[0513] Automatic generation of slides

[0514] The server automatically generates slide materials based on the summarized educational data.

[0515] What it does: Inserts summary data into predefined slide templates and automatically generates graphs and charts.

[0516] Input: Summary text, slide template

[0517] Output: Generated slide deck (e.g., slide on solving linear equations)

[0518] Step 4:

[0519] Slide deck review and feedback

[0520] The generated slide materials are sent to the expert via the terminal.

[0521] Concrete action: An expert reviews a slide deck and sends feedback from the device to the server. For example, a math teacher requests the addition of a concrete example.

[0522] Input: Generated slide deck, expert feedback

[0523] Output: Slide deck with feedback

[0524] Step 5:

[0525] Editing slide materials

[0526] The server modifies the slide deck based on the expert feedback.

[0527] Specific operations: Modify templates and generate additional graphs and charts.

[0528] Input: Slide deck with feedback

[0529] Output: Revised slide deck

[0530] Step 6:

[0531] Recording and archiving educational content

[0532] Selected teachers use the devices to record educational content and upload the video files to the server.

[0533] What it does: Teachers record videos on their devices and upload them to a server, which then archives the videos on an online platform.

[0534] Input: Recorded lesson video

[0535] Output: Video archived on an online platform

[0536] Step 7:

[0537] Automatic generation of test questions

[0538] The server automatically creates test questions based on the learning content using a generative AI model.

[0539] Specific operation: The server sends a prompt to the generative AI model, which generates test questions and their answers.

[0540] Input: Study content, prompt (e.g. "Please create test questions based on the following study content")

[0541] Output: Generated test questions and their answers

[0542] Step 8:

[0543] User questions and real-time answers

[0544] A user submits a question through the education platform.

[0545] How it works: The user enters a question and presses the submit button. The server then uses the AI ​​model to analyze the question and generate an answer in real time.

[0546] Input: User question, prompt (e.g., "I'm not sure how to solve this linear equation.")

[0547] Output: Generated answers (e.g. explanations and sample answers)

[0548] Each of the above steps will enable the efficient digitization of educational data and the provision of high-quality educational content.

[0549] (Application example 1)

[0550] 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."

[0551] In modern education, efficient provision of high-quality educational content and deepening student understanding requires the organization of vast amounts of educational data and its presentation in an easy-to-understand manner. However, traditional methods require time-consuming manual creation of materials and feedback, making real-time support difficult. Furthermore, a lack of universally accessible educational platforms means that learning environments remain dependent on specific educational institutions. To solve these problems, an educational support system that utilizes advanced automation technology and the Internet is needed.

[0552] 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.

[0553] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as an archive; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means installed on a smartphone or smart glasses for delivering educational content and providing feedback. This allows for the efficient provision of high-quality educational content, rapid reflection of feedback, and the advancement of learning regardless of location or time.

[0554] "Curriculum data" refers to information stored in digital format that contains the contents of curricula and lesson plans established by educational institutions.

[0555] "Natural language processing technology" refers to technology that processes human language using computers, and includes systems that understand and generate text.

[0556] "Slide materials" are presentation materials consisting of a series of slides used to visually explain educational content.

[0557] An "expert" is a person who has specialized knowledge and experience in a particular field.

[0558] "Feedback" means evaluation and suggestions for improvement provided based on information and results received.

[0559] An "online platform" is a system that serves as the foundation for providing services and content via the Internet.

[0560] "Archive distribution" refers to storing recorded content so that users can access and view it later.

[0561] "Generative AI" is a system that automatically generates text and questions using artificial intelligence technology.

[0562] An "AI model" is an artificial intelligence algorithm and structure used to learn from data and perform specific tasks.

[0563] A "smartphone" is a mobile phone that has advanced information processing capabilities and is capable of running applications.

[0564] "Smart glasses" are wearable devices that have the ability to display information within the field of view and realize augmented reality.

[0565] "Educational content" refers to digital learning materials such as textbooks, videos, and workbooks used to provide education to learners.

[0566] The present invention relates to a system for distributing and providing feedback on educational content. Specific embodiments of this system will be described below.

[0567] System Configuration

[0568] The system consists of a server, terminals (smartphones or smart glasses), and users. The server is responsible for acquiring curriculum data from educational institutions and processing and distributing it. Meanwhile, the terminals function as an interface for users to receive and study educational content.

[0569] Hardware and Software

[0570] The server will be equipped with hardware with high-speed data processing capabilities and the following software:

[0571] Natural Language Processing Technology: Hugging Face transformers library

[0572] Data acquisition: requests library (Python)

[0573] Slide generation: python-pptx library

[0574] Question answering system: Hugging Face transformers library and pre-trained models (e.g., distilbert-base-cased-distilled-squad)

[0575] The smartphones and smart glasses used as terminals also have a corresponding application installed, which has the function of receiving and displaying educational content.

[0576] Overview of program processing

[0577] Acquisition and summary of curriculum data

[0578] First, the server retrieves curriculum data from educational institutions using an API. This data is then summarized using natural language processing techniques to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" is summarized as "Basic Solutions and Applications of Linear Equations."

[0579] Automatic generation of slides

[0580] The system then automatically generates slide decks based on the summarized data, using predefined slide templates to populate them with the appropriate summary data, graphs, and charts—for example, slides containing step-by-step instructions and charts for solving linear equations.

[0581] Feedback and Corrections

[0582] The generated slides are sent to experts via the terminal. The server improves the slides by receiving feedback from the experts. For example, if a math teacher gives feedback such as "This solution step does not include enough concrete examples," the slides are revised based on that feedback.

[0583] Distribution and archiving of lesson videos

[0584] In addition, videos of lessons recorded by selected instructors will be uploaded to the server and distributed through the online platform. Users can access these videos at any time. For example, a lesson video on solving linear equations will be uploaded and made available for users to watch.

[0585] Automatic generation of test questions

[0586] Test questions based on the learning content are automatically created using generative AI. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer will be generated.

[0587] Real-time question answering

[0588] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0589] Examples of prompt statements

[0590] Examples of specific prompts include the following:

[0591] Example of input prompt:

[0592] Summarize what you learned about "Solving Linear Equations."

[0593] Example of generated results:

[0594] This article summarizes the basic methods and applications of solving linear equations. A linear equation is an equation that is calculated only once for one variable... (summary below)

[0595] The above is an embodiment of the present invention, which makes it possible to efficiently provide high-quality educational content, quickly incorporate feedback, and advance learning without being restricted by location or time.

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] The server retrieves curriculum data from the educational institution. The server uses an API to request and receive the required data, while verifying the accuracy and completeness of the data. The input is data from the educational institution's API endpoint, and the output is the retrieved curriculum data.

[0599] Step 2:

[0600] The server summarizes the acquired curriculum data using natural language processing techniques. The server uses natural language processing tools such as Hugging Face's transformers library to extract key concepts and learning objectives. The input is the acquired curriculum data, and the output is summarized text data.

[0601] Step 3:

[0602] The server automatically generates slide presentations based on the summarized data. It uses the python-pptx library to insert summarized data, graphs, and figures into predefined slide templates. The input is the summarized text data and data visualization information, and the output is the generated slide presentation file.

[0603] Step 4:

[0604] The server sends the generated slides to the terminal and requests the expert to review them. The expert reviews the slides via the terminal and provides feedback. The input is the generated slides, and the output is the feedback from the expert.

[0605] Step 5:

[0606] The server receives the expert's feedback and modifies the slide deck based on it. The server adjusts the slide template and content according to the expert's opinions and suggestions. The input is the expert's feedback and the original slide deck, and the output is the modified slide deck.

[0607] Step 6:

[0608] Selected faculty members record their classes and upload the video files to a server, which stores them in a digital archive and distributes them through an online platform. The input is the recorded class video, and the output is the archived video file and its distribution link.

[0609] Step 7:

[0610] The server generates test questions based on the learning content. It uses a generative AI model to automatically create formatted test questions and their answers. The input is the learning content and summary data, and the output is the generated test questions and their answers.

[0611] Step 8:

[0612] Users submit questions through the educational platform. The submitted questions are received by the server, which uses an AI model to analyze the questions and generate answers in real time. The input is the user's question, and the output is the generated answer.

[0613] Step 9:

[0614] The server sends the generated answer to the user through the educational platform. The terminal displays the answer to the user in real time and provides necessary educational support. The input is the generated answer, and the output is the answer content provided to the user.

[0615] The above are the specific processing steps of the system for implementing the invention. In this way, high-quality educational content and rapid feedback are provided, helping learners to deepen their understanding.

[0616] 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.

[0617] This invention relates to a system for digitizing educational curriculum data and providing high-quality educational content, and further optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions. Below, the program processing of this system is explained in natural language.

[0618] The server's role is to obtain curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. The data is downloaded by sending an API request or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[0619] The server then summarizes the acquired curriculum data using natural language processing techniques. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored back in the database.

[0620] The server then automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted in the appropriate locations. Graphs and charts are also generated and added to the slides as needed. For example, a slide containing step-by-step instructions and charts for solving linear equations can be generated.

[0621] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step lacks concrete examples," and the slides are then modified based on that feedback.

[0622] Next, the selected teachers record the educational content and upload the video files to the server via their devices. The server then archives the video files on an online platform so that users can access them at any time. For example, a video lesson on solving linear equations can be uploaded and made available for users to watch.

[0623] The server automatically generates test questions based on the learning content using AI generation. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer can be generated.

[0624] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0625] Furthermore, an emotion engine is built into the system to recognize the user's emotions. The server analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. For example, if the user is having difficulty understanding something, the emotion engine will use that information to generate and provide additional explanations or encouraging messages.

[0626] Based on the emotional data, the server can individually adjust the progress of the learning curriculum and provide the user with an optimal learning environment. This increases motivation and enables efficient learning. The emotional engine automatically generates messages to increase the user's motivation, providing personalized support according to the learner's emotional state.

[0627] With the above configuration, the present invention can improve the quality of education and effectively address the issues of students not attending school and teacher shortages. Furthermore, by recognizing users' emotions and optimizing the learning experience, it is possible to provide a more fulfilling educational environment.

[0628] The processing flow will be explained below.

[0629] Step 1:

[0630] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending an API request to download the data or by importing a periodically updated CSV file from a defined location. The obtained data is then stored in a database.

[0631] Step 2:

[0632] The server summarizes the acquired curriculum data using natural language processing techniques. Topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored in a database.

[0633] Step 3:

[0634] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted into the appropriate slides. Furthermore, the server generates necessary graphs and charts based on the numerical data and incorporates them into the slides. For example, it generates slides that include step-by-step instructions on how to solve a linear equation and accompanying graphs to visually complement the explanation.

[0635] Step 4:

[0636] The generated slides are sent to the expert via the terminal, which then sends them to the expert's email address or via a dedicated review system. The slides are then sent via email or uploaded for review on a dedicated platform.

[0637] Step 5:

[0638] Experts review the slides and provide feedback, which is sent back to the system via the device. For example, a math teacher might say, "This solution step doesn't provide enough concrete examples." That feedback is then sent to the server via the device.

[0639] Step 6:

[0640] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved in the database and proceed to the next step.

[0641] Step 7:

[0642] Selected teachers record educational content. The recorded lesson videos are uploaded to the server via their devices. The video files are saved in a specific format (e.g., MP4).

[0643] Step 8:

[0644] The server archives the uploaded video files on the online platform, and a link is generated and saved for users to access at any time. For example, a video lesson on solving linear equations can be uploaded and viewed by users.

[0645] Step 9:

[0646] The server automatically generates test questions based on the learning content using AI. The generated test questions also include the answers, which are saved in the database. For example, a question such as "Solve the following linear equation" and its answer are generated.

[0647] Step 10:

[0648] A user submits a question through the educational platform. The question is entered in text format and sent to the server. For example, a user submits a question such as, "I don't really understand how to solve this linear equation."

[0649] Step 11:

[0650] The server receives questions sent by users and uses an AI model to generate answers in real time. The generated answers are then returned to the users. For example, the AI ​​returns sample answers and detailed explanations in real time.

[0651] Step 12:

[0652] The server uses an emotion engine that recognizes the user's emotions to analyze the user's learning status in real time, for example, by detecting and analyzing the user's facial expressions and tone of voice through a camera or microphone.

[0653] Step 13:

[0654] The server adjusts the progress of the learning curriculum individually based on the emotion data. If the user has difficulty understanding, it provides additional explanations and supplementary materials. For example, if the user is confused, it provides additional lecture videos with supplementary explanations.

[0655] Step 14:

[0656] Based on the emotional data generated by the emotion engine, a motivational message is automatically generated and sent to the user. For example, a message such as "You're doing great! You're almost at the next stage" can be generated and sent to the user.

[0657] The above are the specific processing steps performed by the server, the terminal, and the user.

[0658] Example 2

[0659] 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."

[0660] In modern education, effectively providing high-quality educational content is a key challenge. However, traditional educational systems lack the means to efficiently digitize curriculum data, summarize, create materials, incorporate feedback, distribute video lectures, generate test questions, respond to questions in real time, and recognize and provide feedback to learners. As a result, the quality of education declines and learners' motivation decreases. Furthermore, it is difficult to provide a data-based, personalized learning curriculum.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0662] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating presentation materials based on the summarized curriculum data; means for sending the generated presentation materials to experts and receiving feedback; means for revising the presentation materials based on the expert feedback; means for uploading lectures recorded by selected teachers to an online platform and archiving them; means for creating test questions based on learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; means for providing feedback and learning materials according to the user's learning status using an emotion engine that recognizes the user's emotions; and means for individually adjusting the learning curriculum based on the emotion data. This makes it possible to efficiently and effectively provide educational content and create a personalized learning environment.

[0663] "Curriculum Data" refers to information about the curriculum provided by an educational institution, including, specifically, subjects, learning objectives, topics, learning procedures, etc.

[0664] "Natural language processing technology" refers to the technology of processing human language using a computer, and specifically includes text summarization, topic modeling, text classification, etc.

[0665] "Presentation materials" are materials that summarize the learning content in a visually easy-to-understand manner, and are generally in the form of slides or visual aids.

[0666] An "expert" is someone who has deep knowledge and experience in a particular field, and typically refers to educators and academics.

[0667] An "online platform" is a platform for providing services and content via the Internet, and in the education field it includes the distribution of video lessons and learning management systems.

[0668] "Generative AI" refers to artificial intelligence that generates text and images, specifically natural language generation and image generation.

[0669] An "AI model" is an artificial intelligence framework that uses machine learning or deep learning to perform specific tasks, such as classification, prediction, and generation.

[0670] An "emotion engine" is a system for analyzing a user's emotional state in real time, specifically identifying emotions using facial expression and voice recognition technology.

[0671] An "educational platform" is a web application that manages and provides education-related services and content in an integrated manner, and serves as the foundation for users to access and use learning content.

[0672] A "learning curriculum" is a plan or schedule that defines the progress of learning, and specifically includes the learning content, learning goals, and time allocation for each subject.

[0673] The present invention is a system for digitizing educational curriculum data and providing high-quality educational content, and in particular, optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions.

[0674] System Overview

[0675] This system consists of three elements: a server, a terminal, and a user. The server plays the main role of acquiring, processing, storing, summarizing, and generating data, while the terminal acts as data input and user interface. The user is a consumer of educational content, learning and providing feedback.

[0676] Hardware and Software Configuration

[0677] Server: A high-performance server is used for data processing and storage. MySQL is used as the database, and BERT and T5 models are used for natural language processing. Google Slides API is used to generate presentation materials, and Slack API is used to collect feedback.

[0678] Devices: Computers, tablets, and smartphones are used as interfaces between users and the server. Web applications are used for video recording and uploading.

[0679] Users: Consume content through the educational platform and use Microsoft Azure's Emotion API for emotion recognition technology.

[0680] Program processing

[0681] Data Acquisition and Summarization

[0682] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and educational institutions through API requests and CSV file imports. The obtained data is checked for consistency and stored in a database.

[0683] The server uses natural language processing techniques (BERT topic model and T5 summarization model) to summarize the curriculum data and extract key concepts and learning objectives.

[0684] Example: Summarize the section "Solving Linear Equations" as "Basic Solutions of Linear Equations and Applications."

[0685] Automatic generation of presentation materials

[0686] The server uses the summary data to generate presentation materials via the Google Slides API, and if necessary, creates graphs and charts using the D3.js library and adds them to the materials.

[0687] For example: A slide includes "Step-by-step instructions on solving linear equations and corresponding graphs."

[0688] Feedback and Corrections

[0689] The generated presentation materials are sent to experts via the terminal, and feedback is collected using the Slack API, and the materials are updated based on the feedback using an automatic correction script.

[0690] For example, if the feedback is that the solution steps do not contain enough concrete examples, insert additional concrete examples.

[0691] Recording and distributing educational content

[0692] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then compress the videos, upload them to the platform using the YouTube API, and generate an access link.

[0693] Example: A "Lesson video on solving linear equations" is uploaded and made available for users to watch.

[0694] Automatic generation of test questions

[0695] The server uses generative AI (e.g., GPT) to automatically generate test questions and answers based on the learning content.

[0696] Example: The problem is "Solve the following linear equation \(2x + 3 = 7\)" and the answer is "\( x = 2 \)".

[0697] User questions and AI answers

[0698] Users submit questions through the educational platform, and the server uses an AI model to analyze the questions and generate answers in real time.

[0699] Example: In response to the prompt "I don't really know how to solve a linear equation," provide detailed solution steps along with concrete examples.

[0700] Emotion Recognition and Feedback

[0701] The server uses an emotion engine to analyze emotions from the user's facial expressions and voice in real time while they are learning, and provides feedback and additional learning materials.

[0702] For example, if the user shows signs of confusion, the emotion engine can generate and provide additional explanations or encouraging messages.

[0703] Individualized learning curriculum

[0704] The server adjusts the learning curriculum based on the emotion data, providing additional review materials to users who are falling behind, and generating encouraging messages from the emotion engine to motivate users.

[0705] The present invention makes it possible to provide educational content efficiently and effectively and to create a personalized learning environment.

[0706] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0707] Step 1:

[0708] Acquisition of curriculum data

[0709] The server sends a request to the API endpoint provided by the educational institution to obtain the latest curriculum data. The data is received as a CSV file or API response.

[0710] Input: API request or CSV file path

[0711] Output: Curriculum data (JSON or CSV format)

[0712] Specific operation: The server sends a request to the API as a scheduled task every morning at 8:00 to receive new data, which is then stored in the database after undergoing data integrity checks.

[0713] Step 2:

[0714] Data Summarization

[0715] The server summarizes the acquired educational curriculum data using natural language processing techniques, such as the BERT topic model and the T5 summarization model.

[0716] Input: Curriculum data

[0717] Output: Summarized curriculum data

[0718] What it does: The server uses newly saved data as triggers to run NLP algorithms and summarize key concepts and learning objectives, for example summarizing the "Solving Linear Equations" section as "Basic Solutions and Applications of Linear Equations."

[0719] Step 3:

[0720] Automatic generation of presentation materials

[0721] The server automatically generates presentation materials using the Google Slides API based on the summarized curriculum data.

[0722] Input: summarized curriculum data

[0723] Output: Generated presentation materials (Google Slides format)

[0724] What happens: The server loads the template slides, inserts the summary data into the appropriate slides, and optionally creates graphs and charts using the D3.js library and adds them to the slides.

[0725] Step 4:

[0726] Sending and getting feedback

[0727] The server sends the generated presentation materials to the experts, receives feedback, and posts the material link to a dedicated channel using the Slack API.

[0728] Input: Generated presentation materials

[0729] Output: Expert feedback

[0730] How it works: The server automatically posts a link to the document in a Slack channel, and experts can comment on it in Google Slides. The comments are sent to the server in real time.

[0731] Step 5:

[0732] Modifying materials based on feedback

[0733] The server then modifies the presentation materials based on the received feedback, and executes an automatic correction script depending on the feedback.

[0734] Input: Expert feedback

[0735] Output: Revised presentation

[0736] Specific actions: For example, if the feedback is that "the solution steps do not contain enough concrete examples," the server executes a correction script to add concrete examples and updates the materials.

[0737] Step 6:

[0738] Recording and distributing educational content

[0739] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then upload the videos to an online platform for distribution and archiving.

[0740] Input: Recorded lesson video

[0741] Output: Link to a video of the lesson that can be distributed via an online platform

[0742] How it works: Teachers record videos using a web application and submit them through a dedicated upload form. The server compresses the video and uploads it using the YouTube API. A link is automatically generated and users can access it.

[0743] Step 7:

[0744] Automatic generation of test questions

[0745] The server uses a generative AI model (e.g., GPT) to automatically generate test questions based on the learning content, including the answers.

[0746] Input: Curriculum data

[0747] Output: Test questions and their answers

[0748] How it works: The server uses a generative AI model to generate test questions based on the summary data. For example, it generates the question "Solve the following linear equation \(2x + 3 = 7\)" and the answer "\(x = 2\)."

[0749] Step 8:

[0750] User questions and AI answers

[0751] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time.

[0752] Input: User question

[0753] Output: Real-time answers by AI

[0754] Specific operation: When a user inputs a question into the educational platform, the server immediately analyzes the question and generates an appropriate answer. Prompt sentence: For example, in response to "I don't really understand how to solve a linear equation," the server responds with a concrete example and steps to solve the equation.

[0755] Step 9:

[0756] Emotion Recognition and Feedback

[0757] The server recognizes the user's emotions from their facial expressions and voice, and provides feedback and learning materials based on that information, using Microsoft Azure's Emotion API.

[0758] Input: User's facial expression data, voice data

[0759] Output: Feedback and learning materials based on emotion recognition

[0760] Specific operation: The server analyzes the user's emotional data in real time and generates additional explanations or encouraging messages if the user has difficulty understanding. For example, if a confused expression is recognized, the system will provide a more detailed explanation.

[0761] Step 10:

[0762] Individualized learning curriculum

[0763] The server adjusts the learning curriculum based on the user's emotional data, providing review materials and encouraging messages according to the user's progress and level of understanding.

[0764] Input: Emotion data, learning progress data

[0765] Output: personalized learning curriculum and encouragement messages

[0766] Specific operation: The server uses an emotion engine to analyze the user's motivation, and if progress is slow, it provides more review materials and generates and provides appropriate encouraging messages.

[0767] This allows the system to efficiently and effectively deliver educational content and personalize and optimize the user's learning experience.

[0768] (Application example 2)

[0769] 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."

[0770] Traditional educational systems face many challenges in maintaining the quality of educational content and managing learner motivation. It is particularly difficult to efficiently process large volumes of curriculum data and create high-quality teaching materials. It is also difficult to grasp learners' emotions and comprehension levels in real time and provide appropriate feedback based on that information. These challenges often result in a decline in learning motivation and a drop in learning efficiency.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) or other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected educators to an online platform and archiving them; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means for individually adjusting the learning curriculum using an emotion engine that recognizes the user's emotions. This enables improved quality of educational content and management of learner motivation.

[0772] "Ministry of Education, Culture, Sports, Science and Technology and other educational institutions" refers to the government ministries and agencies responsible for the administration of education, culture, sports, science and technology in Japan and other educational institutions.

[0773] "Curriculum data" refers to official curriculum and teaching plan data provided by the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions.

[0774] "Natural language processing technology" is a technology that allows machines to analyze and understand human language, and is used to summarize data and extract information.

[0775] "Slide materials" are materials used to visually present information in a presentation format.

[0776] An "expert" is someone with specialized knowledge and experience.

[0777] "Feedback" refers to providing evaluations and opinions.

[0778] "Correction" refers to correcting or revising deficiencies or improvements.

[0779] "Selected Educators" refers to education professionals who are selected based on specific criteria.

[0780] "Recording" refers to the act of recording video and audio.

[0781] An "online platform" is a system that provides certain services via the Internet.

[0782] "Archive distribution" refers to storing recorded content and making it available for later viewing.

[0783] "Test questions" refer to questions or tasks used to measure comprehension of the learning content.

[0784] "Generative AI" refers to systems that use artificial intelligence techniques to automatically perform specific tasks.

[0785] "Answer" refers to the correct response to a question.

[0786] A "question" is an inquiry for information or help.

[0787] An "AI model" refers to a model for performing data analysis and inference based on artificial intelligence.

[0788] "Real time" refers to immediate action or response.

[0789] An "emotion engine" is a system that analyzes a user's emotional state and provides appropriate feedback based on that.

[0790] A "curriculum" is an educational content and plan that is structured around specific learning objectives.

[0791] "Individually tailored" refers to responses and changes that are optimized for each individual.

[0792] The embodiment of the present invention is a system for efficiently generating educational content and optimizing a user's learning experience. Specific procedures for implementing the present invention and the hardware and software used will be described below.

[0793] The server first obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. This data is obtained by sending an API request to download it or by importing a CSV file that is updated periodically. The obtained curriculum data is then stored in a database. The requests library is used to obtain the data.

[0794] Next, we summarize the acquired curriculum data using natural language processing techniques. Specifically, we use the summarizer model from the transformers library. This model is used to summarize large-scale text data and extract important parts of the data.

[0795] The summarized data is automatically entered into slide decks, which are generated using the pptx library and pre-defined templates, and graphs and charts are automatically generated and added to the slides, if needed.

[0796] The generated slides are sent to experts who provide feedback. The experts then send their feedback to the server via their devices, and the server then corrects the slides based on that feedback. This correction includes both manual and automatic corrections.

[0797] Next, the selected educators will record the lessons and upload the video files to the server via their devices. The server will then archive the video files on an online platform for users to access at any time. Users can access the archived lesson videos using the provided link.

[0798] The server also uses AI to automatically generate test questions based on the learning content. These questions also include answers, and users can use them as practice questions. The generated test questions are customized according to the user's learning progress and are provided based on an individual learning plan.

[0799] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model (e.g., QAModel) to generate answers in real time, using the spacy library for natural language analysis.

[0800] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. This engine adjusts the progress of the learning curriculum individually, providing the user with the optimal learning environment.

[0801] For example, if a user submits a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the confusion and provide a detailed explanation, which will increase motivation and enable more efficient learning.

[0802] Example prompt sentence:

[0803] "Please tell me how to solve the following linear equation."

[0804] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0805] Step 1:

[0806] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions through API requests or periodically updated CSV files. The obtained data is in raw format (JSON or CSV) and is stored in a database. The input is data from educational institutions, and the output is curriculum data stored in the database.

[0807] Step 2:

[0808] The server summarizes the stored curriculum data using natural language processing techniques. Specifically, it applies the summarizer model from the transformers library to summarize long-form curriculum data into key concepts and learning objectives. The input is the curriculum data stored in the database, and the output is the summarized curriculum data.

[0809] Step 3:

[0810] The server automatically generates slides based on the summarized curriculum data. It uses the pptx library to insert the summary content into a pre-defined template. Graphs and charts are also automatically generated and added as needed. The input is the summarized curriculum data, and the output is the generated slides.

[0811] Step 4:

[0812] The server sends the generated slides to the expert and receives feedback. The expert then writes down their evaluation and opinions, which are then sent back to the server. The input is the generated slides, and the output is the feedback provided by the expert.

[0813] Step 5:

[0814] The server then modifies the slides based on the expert feedback. The modifications incorporate the improvements pointed out, and the data is reprocessed as necessary. The input is the expert feedback, and the output is the modified slides.

[0815] Step 6:

[0816] Selected educators record their lessons and upload the video files to a server via their devices. The server then archives the video files on an online platform and makes them accessible. The input is the recorded lesson video, and the output is the lesson video stored on the online platform.

[0817] Step 7:

[0818] The server automatically creates test questions based on the learning content using generative AI. The generative AI model analyzes the educational curriculum data and generates high-quality test questions and answers based on it. The input is the educational curriculum data, and the output is the generated test questions and answers.

[0819] Step 8:

[0820] Users submit questions through the educational platform. The submitted questions are received by the server and analyzed by the AI ​​model. As a result of the analysis, an appropriate answer is generated in real time. The input is the user's question, and the output is the answer provided by the AI ​​model.

[0821] Step 9:

[0822] The server uses an emotion engine to recognize the user's emotions and individually adjusts the learning curriculum. The emotion engine analyzes the user's emotional data during learning and provides appropriate feedback and learning materials based on that data. The input is the user's emotional data, and the output is an individually adjusted learning curriculum.

[0823] For example, if a user sends a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the user's confusion and provide a detailed explanation, deepening the user's understanding and increasing their motivation to learn.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] [Third embodiment]

[0828] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0829] 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.

[0830] 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).

[0831] 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.

[0832] 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.

[0833] 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).

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] The present invention relates to a system for digitizing educational curriculum data and providing high-quality educational content. The program processing of this system is explained below in natural language.

[0841] The server's role begins by obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions and summarizing it using natural language processing technology. Specifically, the server sends an API request to download curriculum data, analyzes it using natural language processing algorithms, extracts key concepts and learning objectives, and generates a summary text. For example, the curriculum section on "Solving Linear Equations" might be summarized as "Basic Solutions and Applications of Linear Equations."

[0842] The server then automatically generates slides based on the summarized curriculum. Predefined slide templates are used, and the summary data is inserted into each template. Graphs and charts are also automatically generated based on the numerical data and added to the slides. For example, a slide containing step-by-step instructions and charts for solving a linear equation can be generated.

[0843] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step does not provide enough concrete examples," and the slides are then modified based on that feedback.

[0844] In addition, selected teachers will record educational content and upload the video files to the server, which will then archive the video files on the online platform for users to access at any time. For example, a video lesson on solving linear equations will be uploaded and made available for users to watch.

[0845] Test questions based on the learning content are automatically created by the server using a generation AI. The questions also include answers, which users can use as practice questions. As a specific example, the problem "Solve the following linear equation" and its answer are generated.

[0846] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0847] In this way, the system of the present invention efficiently digitizes curriculum data and makes it possible to provide high-quality educational content, which not only effectively addresses the issues of students not attending school and teacher shortages, but also enables learning regardless of location or time.

[0848] The processing flow will be explained below.

[0849] Step 1:

[0850] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending API requests to download the data or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[0851] Step 2:

[0852] The server then uses natural language processing techniques to summarize the acquired curriculum data. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. The summarized data is then stored back in the database.

[0853] Step 3:

[0854] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded, and the summary content is inserted in the appropriate positions. Graphs and charts are also generated and added to the slides as needed.

[0855] Step 4:

[0856] The server sends the generated slides to the expert via the terminal, who reviews the slides and provides feedback. This feedback is then sent from the terminal to the server.

[0857] Step 5:

[0858] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved and the process moves to the next step.

[0859] Step 6:

[0860] Selected teachers record educational content and upload the video files to a server via their devices, where they are saved in a specific format.

[0861] Step 7:

[0862] The server archives the uploaded video files on an online platform, and a link is generated and saved for users to access at any time.

[0863] Step 8:

[0864] The server uses AI to generate test questions based on the learning content, and answers are automatically generated for the generated test questions and stored in a database.

[0865] Step 9:

[0866] Users submit questions through the educational platform, which are entered in text format and sent to the server.

[0867] Step 10:

[0868] The server receives questions sent by users and uses AI models to generate answers in real time, which are then returned to the users.

[0869] The above are the specific processing steps performed by the server, the terminal, and the user.

[0870] Example 1

[0871] 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."

[0872] In today's educational environment, improving the quality of educational content requires efficient digitization of educational data and the effective creation of teaching materials based on that data. However, manually processing large amounts of educational data and creating appropriate teaching materials is a major challenge for teachers and educational institutions, requiring time and effort. Furthermore, there is a need for efficient means to provide high-quality education regardless of location or time, especially for students who are absent from school due to teacher shortages.

[0873] 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.

[0874] In this invention, the server includes: means for acquiring educational data from educational institutions; means for summarizing the acquired educational data using natural language processing technology; means for automatically generating slides based on the summarized educational data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as archives; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; and means for generating answers to the submitted questions in real time using an AI model. This enables efficient digitization of educational data and the provision of high-quality educational content.

[0875] An "educational institution" is any public or private organization that provides education-related data or information.

[0876] "Educational Data" means information or data related to education, such as curriculum, learning objectives, teaching materials, and test questions.

[0877] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0878] "Slide materials" are electronic presentation materials for visually presenting educational content.

[0879] An "expert" is someone who has advanced knowledge and skills in a particular field.

[0880] "Feedback" is the act of providing opinions or evaluations of information or services provided.

[0881] An "online platform" is a basic system for providing and sharing various services and content via the Internet.

[0882] "Archive distribution" refers to the saving and distribution of recorded content so that it can be viewed at a later date.

[0883] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[0884] A "test item" is a question or problem used to assess a learner's understanding or knowledge.

[0885] An "answer" is a correct response or interpretation to a test question or question.

[0886] An "education platform" is an online system that provides education-related services and content in a centralized manner.

[0887] An "AI model" is a complex computational model that combines artificial intelligence algorithms and analytical methods.

[0888] "Real time" refers to a short period of time in which a response is returned immediately after the user does something.

[0889] This invention is a system for digitizing educational data and providing high-quality educational content. This system automates the process of acquiring educational data from educational institutions and summarizing, processing, and distributing it.

[0890] At the heart of the system is a server, which performs multiple functions. The server has a means of acquiring educational data from educational institutions. This means, for example, downloading the educational data using an API request. The server then summarizes the acquired educational data using natural language processing techniques. To do this, it uses a generative AI model to extract and concisely summarize the important information in the educational data.

[0891] For example, the server might send the following prompt to the generative AI model:

[0892] Summarize the curriculum data and extract key concepts and learning objectives. As a specific example, summarize how to solve linear equations.

[0893] The server then automatically generates slides based on the summarized educational data. During this process, the summary data is inserted into a predefined slide template, and graphs and diagrams are automatically generated based on the numerical data. The slides are then sent to experts via the device, who review and provide feedback. As a concrete example, consider a scenario in which a mathematics teacher gives feedback such as, "This solution step does not provide enough concrete examples."

[0894] The server receives feedback, modifies the slides, and sends them back to the experts, improving the quality of the materials provided. Additionally, selected instructors record educational content and upload the video files from their devices to the server. The server then archives the video files on an online platform, making them accessible to users at any time.

[0895] The server also uses a generative AI model to automatically generate test questions based on the learning content. Examples of prompts sent by the server include:

[0896] Create test questions based on the following learning: Provide specific questions and solutions related to solving linear equations.

[0897] Users can submit questions through the educational platform, and the server generates answers in real time using AI models. For example, if a user asks, "I don't really know how to solve this linear equation," the server will instantly provide an explanation and sample answer.

[0898] This will enable the efficient digitization of educational data and the provision of high-quality educational content, and will also strengthen educational support for students who are experiencing teacher shortages or who are not attending school.

[0899] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0900] Step 1:

[0901] Acquisition of educational data

[0902] The server obtains educational data from the educational institution.

[0903] Specific operation: The server sends an API request to download educational data.

[0904] Input: API request (e.g., GET / education_curriculum)

[0905] Output: Retrieved educational data (e.g., detailed educational course information)

[0906] Step 2:

[0907] Education Data Summary

[0908] The educational data acquired by the server is summarized using natural language processing technology.

[0909] Specific operation: The server sends prompts to the generative AI model to extract key concepts and learning objectives.

[0910] Input: Captured educational data, prompt (e.g., "Please summarize the educational course data and extract key concepts and learning objectives.")

[0911] Output: Summary text (e.g. "Basic methods for solving linear equations and examples of their applications")

[0912] Step 3:

[0913] Automatic generation of slides

[0914] The server automatically generates slide materials based on the summarized educational data.

[0915] What it does: Inserts summary data into predefined slide templates and automatically generates graphs and charts.

[0916] Input: Summary text, slide template

[0917] Output: Generated slide deck (e.g., slide on solving linear equations)

[0918] Step 4:

[0919] Slide deck review and feedback

[0920] The generated slide materials are sent to the expert via the terminal.

[0921] Concrete action: An expert reviews a slide deck and sends feedback from the device to the server. For example, a math teacher requests the addition of a concrete example.

[0922] Input: Generated slide deck, expert feedback

[0923] Output: Slide deck with feedback

[0924] Step 5:

[0925] Editing slide materials

[0926] The server modifies the slide deck based on the expert feedback.

[0927] Specific operations: Modify templates and generate additional graphs and charts.

[0928] Input: Slide deck with feedback

[0929] Output: Revised slide deck

[0930] Step 6:

[0931] Recording and archiving educational content

[0932] Selected teachers use the devices to record educational content and upload the video files to the server.

[0933] What it does: Teachers record videos on their devices and upload them to a server, which then archives the videos on an online platform.

[0934] Input: Recorded lesson video

[0935] Output: Video archived on an online platform

[0936] Step 7:

[0937] Automatic generation of test questions

[0938] The server automatically creates test questions based on the learning content using a generative AI model.

[0939] Specific operation: The server sends a prompt to the generative AI model, which generates test questions and their answers.

[0940] Input: Study content, prompt (e.g. "Please create test questions based on the following study content")

[0941] Output: Generated test questions and their answers

[0942] Step 8:

[0943] User questions and real-time answers

[0944] A user submits a question through the education platform.

[0945] How it works: The user enters a question and presses the submit button. The server then uses the AI ​​model to analyze the question and generate an answer in real time.

[0946] Input: User question, prompt (e.g., "I'm not sure how to solve this linear equation.")

[0947] Output: Generated answers (e.g. explanations and sample answers)

[0948] Each of the above steps will enable the efficient digitization of educational data and the provision of high-quality educational content.

[0949] (Application example 1)

[0950] 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."

[0951] In modern education, efficient provision of high-quality educational content and deepening student understanding requires the organization of vast amounts of educational data and its presentation in an easy-to-understand manner. However, traditional methods require time-consuming manual creation of materials and feedback, making real-time support difficult. Furthermore, a lack of universally accessible educational platforms means that learning environments remain dependent on specific educational institutions. To solve these problems, an educational support system that utilizes advanced automation technology and the Internet is needed.

[0952] 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.

[0953] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as an archive; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means installed on a smartphone or smart glasses for delivering educational content and providing feedback. This allows for the efficient provision of high-quality educational content, rapid reflection of feedback, and the advancement of learning regardless of location or time.

[0954] "Curriculum data" refers to information stored in digital format that contains the contents of curricula and lesson plans established by educational institutions.

[0955] "Natural language processing technology" refers to technology that processes human language using computers, and includes systems that understand and generate text.

[0956] "Slide materials" are presentation materials consisting of a series of slides used to visually explain educational content.

[0957] An "expert" is a person who has specialized knowledge and experience in a particular field.

[0958] "Feedback" means evaluation and suggestions for improvement provided based on information and results received.

[0959] An "online platform" is a system that serves as the foundation for providing services and content via the Internet.

[0960] "Archive distribution" refers to storing recorded content so that users can access and view it later.

[0961] "Generative AI" is a system that automatically generates text and questions using artificial intelligence technology.

[0962] An "AI model" is an artificial intelligence algorithm and structure used to learn from data and perform specific tasks.

[0963] A "smartphone" is a mobile phone that has advanced information processing capabilities and is capable of running applications.

[0964] "Smart glasses" are wearable devices that have the ability to display information within the field of view and realize augmented reality.

[0965] "Educational content" refers to digital learning materials such as textbooks, videos, and workbooks used to provide education to learners.

[0966] The present invention relates to a system for distributing and providing feedback on educational content. Specific embodiments of this system will be described below.

[0967] System Configuration

[0968] The system consists of a server, terminals (smartphones or smart glasses), and users. The server is responsible for acquiring curriculum data from educational institutions and processing and distributing it. Meanwhile, the terminals function as an interface for users to receive and study educational content.

[0969] Hardware and Software

[0970] The server will be equipped with hardware with high-speed data processing capabilities and the following software:

[0971] Natural Language Processing Technology: Hugging Face transformers library

[0972] Data acquisition: requests library (Python)

[0973] Slide generation: python-pptx library

[0974] Question answering system: Hugging Face transformers library and pre-trained models (e.g., distilbert-base-cased-distilled-squad)

[0975] The smartphones and smart glasses used as terminals also have a corresponding application installed, which has the function of receiving and displaying educational content.

[0976] Overview of program processing

[0977] Acquisition and summary of curriculum data

[0978] First, the server retrieves curriculum data from educational institutions using an API. This data is then summarized using natural language processing techniques to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" is summarized as "Basic Solutions and Applications of Linear Equations."

[0979] Automatic generation of slides

[0980] The system then automatically generates slide decks based on the summarized data, using predefined slide templates to populate them with the appropriate summary data, graphs, and charts—for example, slides containing step-by-step instructions and charts for solving linear equations.

[0981] Feedback and Corrections

[0982] The generated slides are sent to experts via the terminal. The server improves the slides by receiving feedback from the experts. For example, if a math teacher gives feedback such as "This solution step does not include enough concrete examples," the slides are revised based on that feedback.

[0983] Distribution and archiving of lesson videos

[0984] In addition, videos of lessons recorded by selected instructors will be uploaded to the server and distributed through the online platform. Users can access these videos at any time. For example, a lesson video on solving linear equations will be uploaded and made available for users to watch.

[0985] Automatic generation of test questions

[0986] Test questions based on the learning content are automatically created using generative AI. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer will be generated.

[0987] Real-time question answering

[0988] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[0989] Examples of prompt statements

[0990] Examples of specific prompts include the following:

[0991] Example of input prompt:

[0992] Summarize what you learned about "Solving Linear Equations."

[0993] Example of generated results:

[0994] This article summarizes the basic methods and applications of solving linear equations. A linear equation is an equation that is calculated only once for one variable... (summary below)

[0995] The above is an embodiment of the present invention, which makes it possible to efficiently provide high-quality educational content, quickly incorporate feedback, and advance learning without being restricted by location or time.

[0996] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0997] Step 1:

[0998] The server retrieves curriculum data from the educational institution. The server uses an API to request and receive the required data, while verifying the accuracy and completeness of the data. The input is data from the educational institution's API endpoint, and the output is the retrieved curriculum data.

[0999] Step 2:

[1000] The server summarizes the acquired curriculum data using natural language processing techniques. The server uses natural language processing tools such as Hugging Face's transformers library to extract key concepts and learning objectives. The input is the acquired curriculum data, and the output is summarized text data.

[1001] Step 3:

[1002] The server automatically generates slide presentations based on the summarized data. It uses the python-pptx library to insert summarized data, graphs, and figures into predefined slide templates. The input is the summarized text data and data visualization information, and the output is the generated slide presentation file.

[1003] Step 4:

[1004] The server sends the generated slides to the terminal and requests the expert to review them. The expert reviews the slides via the terminal and provides feedback. The input is the generated slides, and the output is the feedback from the expert.

[1005] Step 5:

[1006] The server receives the expert's feedback and modifies the slide deck based on it. The server adjusts the slide template and content according to the expert's opinions and suggestions. The input is the expert's feedback and the original slide deck, and the output is the modified slide deck.

[1007] Step 6:

[1008] Selected faculty members record their classes and upload the video files to a server, which stores them in a digital archive and distributes them through an online platform. The input is the recorded class video, and the output is the archived video file and its distribution link.

[1009] Step 7:

[1010] The server generates test questions based on the learning content. It uses a generative AI model to automatically create formatted test questions and their answers. The input is the learning content and summary data, and the output is the generated test questions and their answers.

[1011] Step 8:

[1012] Users submit questions through the educational platform. The submitted questions are received by the server, which uses an AI model to analyze the questions and generate answers in real time. The input is the user's question, and the output is the generated answer.

[1013] Step 9:

[1014] The server sends the generated answer to the user through the educational platform. The terminal displays the answer to the user in real time and provides necessary educational support. The input is the generated answer, and the output is the answer content provided to the user.

[1015] The above are the specific processing steps of the system for implementing the invention. In this way, high-quality educational content and rapid feedback are provided, helping learners to deepen their understanding.

[1016] 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.

[1017] This invention relates to a system for digitizing educational curriculum data and providing high-quality educational content, and further optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions. Below, the program processing of this system is explained in natural language.

[1018] The server's role is to obtain curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. The data is downloaded by sending an API request or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[1019] The server then summarizes the acquired curriculum data using natural language processing techniques. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored back in the database.

[1020] The server then automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted in the appropriate locations. Graphs and charts are also generated and added to the slides as needed. For example, a slide containing step-by-step instructions and charts for solving linear equations can be generated.

[1021] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step lacks concrete examples," and the slides are then modified based on that feedback.

[1022] Next, the selected teachers record the educational content and upload the video files to the server via their devices. The server then archives the video files on an online platform so that users can access them at any time. For example, a video lesson on solving linear equations can be uploaded and made available for users to watch.

[1023] The server automatically generates test questions based on the learning content using AI generation. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer can be generated.

[1024] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[1025] Furthermore, an emotion engine is built into the system to recognize the user's emotions. The server analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. For example, if the user is having difficulty understanding something, the emotion engine will use that information to generate and provide additional explanations or encouraging messages.

[1026] Based on the emotional data, the server can individually adjust the progress of the learning curriculum and provide the user with an optimal learning environment. This increases motivation and enables efficient learning. The emotional engine automatically generates messages to increase the user's motivation, providing personalized support according to the learner's emotional state.

[1027] With the above configuration, the present invention can improve the quality of education and effectively address the issues of students not attending school and teacher shortages. Furthermore, by recognizing users' emotions and optimizing the learning experience, it is possible to provide a more fulfilling educational environment.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending an API request to download the data or by importing a periodically updated CSV file from a defined location. The obtained data is then stored in a database.

[1031] Step 2:

[1032] The server summarizes the acquired curriculum data using natural language processing techniques. Topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored in a database.

[1033] Step 3:

[1034] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted into the appropriate slides. Furthermore, the server generates necessary graphs and charts based on the numerical data and incorporates them into the slides. For example, it generates slides that include step-by-step instructions on how to solve a linear equation and accompanying graphs to visually complement the explanation.

[1035] Step 4:

[1036] The generated slides are sent to the expert via the terminal, which then sends them to the expert's email address or via a dedicated review system. The slides are then sent via email or uploaded for review on a dedicated platform.

[1037] Step 5:

[1038] Experts review the slides and provide feedback, which is sent back to the system via the device. For example, a math teacher might say, "This solution step doesn't provide enough concrete examples." That feedback is then sent to the server via the device.

[1039] Step 6:

[1040] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved in the database and proceed to the next step.

[1041] Step 7:

[1042] Selected teachers record educational content. The recorded lesson videos are uploaded to the server via their devices. The video files are saved in a specific format (e.g., MP4).

[1043] Step 8:

[1044] The server archives the uploaded video files on the online platform, and a link is generated and saved for users to access at any time. For example, a video lesson on solving linear equations can be uploaded and viewed by users.

[1045] Step 9:

[1046] The server automatically generates test questions based on the learning content using AI. The generated test questions also include the answers, which are saved in the database. For example, a question such as "Solve the following linear equation" and its answer are generated.

[1047] Step 10:

[1048] A user submits a question through the educational platform. The question is entered in text format and sent to the server. For example, a user submits a question such as, "I don't really understand how to solve this linear equation."

[1049] Step 11:

[1050] The server receives questions sent by users and uses an AI model to generate answers in real time. The generated answers are then returned to the users. For example, the AI ​​returns sample answers and detailed explanations in real time.

[1051] Step 12:

[1052] The server uses an emotion engine that recognizes the user's emotions to analyze the user's learning status in real time, for example, by detecting and analyzing the user's facial expressions and tone of voice through a camera or microphone.

[1053] Step 13:

[1054] The server adjusts the progress of the learning curriculum individually based on the emotion data. If the user has difficulty understanding, it provides additional explanations and supplementary materials. For example, if the user is confused, it provides additional lecture videos with supplementary explanations.

[1055] Step 14:

[1056] Based on the emotional data generated by the emotion engine, a motivational message is automatically generated and sent to the user. For example, a message such as "You're doing great! You're almost at the next stage" can be generated and sent to the user.

[1057] The above are the specific processing steps performed by the server, the terminal, and the user.

[1058] Example 2

[1059] 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."

[1060] In modern education, effectively providing high-quality educational content is a key challenge. However, traditional educational systems lack the means to efficiently digitize curriculum data, summarize, create materials, incorporate feedback, distribute video lectures, generate test questions, respond to questions in real time, and recognize and provide feedback to learners. As a result, the quality of education declines and learners' motivation decreases. Furthermore, it is difficult to provide a data-based, personalized learning curriculum.

[1061] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1062] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating presentation materials based on the summarized curriculum data; means for sending the generated presentation materials to experts and receiving feedback; means for revising the presentation materials based on the expert feedback; means for uploading lectures recorded by selected teachers to an online platform and archiving them; means for creating test questions based on learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; means for providing feedback and learning materials according to the user's learning status using an emotion engine that recognizes the user's emotions; and means for individually adjusting the learning curriculum based on the emotion data. This makes it possible to efficiently and effectively provide educational content and create a personalized learning environment.

[1063] "Curriculum Data" refers to information about the curriculum provided by an educational institution, including, specifically, subjects, learning objectives, topics, learning procedures, etc.

[1064] "Natural language processing technology" refers to the technology of processing human language using a computer, and specifically includes text summarization, topic modeling, text classification, etc.

[1065] "Presentation materials" are materials that summarize the learning content in a visually easy-to-understand manner, and are generally in the form of slides or visual aids.

[1066] An "expert" is someone who has deep knowledge and experience in a particular field, and typically refers to educators and academics.

[1067] An "online platform" is a platform for providing services and content via the Internet, and in the education field it includes the distribution of video lessons and learning management systems.

[1068] "Generative AI" refers to artificial intelligence that generates text and images, specifically natural language generation and image generation.

[1069] An "AI model" is an artificial intelligence framework that uses machine learning or deep learning to perform specific tasks, such as classification, prediction, and generation.

[1070] An "emotion engine" is a system for analyzing a user's emotional state in real time, specifically identifying emotions using facial expression and voice recognition technology.

[1071] An "educational platform" is a web application that manages and provides education-related services and content in an integrated manner, and serves as the foundation for users to access and use learning content.

[1072] A "learning curriculum" is a plan or schedule that defines the progress of learning, and specifically includes the learning content, learning goals, and time allocation for each subject.

[1073] The present invention is a system for digitizing educational curriculum data and providing high-quality educational content, and in particular, optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions.

[1074] System Overview

[1075] This system consists of three elements: a server, a terminal, and a user. The server plays the main role of acquiring, processing, storing, summarizing, and generating data, while the terminal acts as data input and user interface. The user is a consumer of educational content, learning and providing feedback.

[1076] Hardware and Software Configuration

[1077] Server: A high-performance server is used for data processing and storage. MySQL is used as the database, and BERT and T5 models are used for natural language processing. Google Slides API is used to generate presentation materials, and Slack API is used to collect feedback.

[1078] Devices: Computers, tablets, and smartphones are used as interfaces between users and the server. Web applications are used for video recording and uploading.

[1079] Users: Consume content through the educational platform and use Microsoft Azure's Emotion API for emotion recognition technology.

[1080] Program processing

[1081] Data Acquisition and Summarization

[1082] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and educational institutions through API requests and CSV file imports. The obtained data is checked for consistency and stored in a database.

[1083] The server uses natural language processing techniques (BERT topic model and T5 summarization model) to summarize the curriculum data and extract key concepts and learning objectives.

[1084] Example: Summarize the section "Solving Linear Equations" as "Basic Solutions of Linear Equations and Applications."

[1085] Automatic generation of presentation materials

[1086] The server uses the summary data to generate presentation materials via the Google Slides API, and if necessary, creates graphs and charts using the D3.js library and adds them to the materials.

[1087] For example: A slide includes "Step-by-step instructions on solving linear equations and corresponding graphs."

[1088] Feedback and Corrections

[1089] The generated presentation materials are sent to experts via the terminal, and feedback is collected using the Slack API, and the materials are updated based on the feedback using an automatic correction script.

[1090] For example, if the feedback is that the solution steps do not contain enough concrete examples, insert additional concrete examples.

[1091] Recording and distributing educational content

[1092] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then compress the videos, upload them to the platform using the YouTube API, and generate an access link.

[1093] Example: A "Lesson video on solving linear equations" is uploaded and made available for users to watch.

[1094] Automatic generation of test questions

[1095] The server uses generative AI (e.g., GPT) to automatically generate test questions and answers based on the learning content.

[1096] Example: The problem is "Solve the following linear equation \(2x + 3 = 7\)" and the answer is "\( x = 2 \)".

[1097] User questions and AI answers

[1098] Users submit questions through the educational platform, and the server uses an AI model to analyze the questions and generate answers in real time.

[1099] Example: In response to the prompt "I don't really know how to solve a linear equation," provide detailed solution steps along with concrete examples.

[1100] Emotion Recognition and Feedback

[1101] The server uses an emotion engine to analyze emotions from the user's facial expressions and voice in real time while they are learning, and provides feedback and additional learning materials.

[1102] For example, if the user shows signs of confusion, the emotion engine can generate and provide additional explanations or encouraging messages.

[1103] Individualized learning curriculum

[1104] The server adjusts the learning curriculum based on the emotion data, providing additional review materials to users who are falling behind, and generating encouraging messages from the emotion engine to motivate users.

[1105] The present invention makes it possible to provide educational content efficiently and effectively and to create a personalized learning environment.

[1106] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1107] Step 1:

[1108] Acquisition of curriculum data

[1109] The server sends a request to the API endpoint provided by the educational institution to obtain the latest curriculum data. The obtained data is received in the form of a CSV file or API response.

[1110] Input: API request or CSV file path

[1111] Output: Curriculum data (JSON or CSV format)

[1112] Specific operation: The server sends a request to the API as a scheduled task every morning at 8:00 to receive new data, which is then stored in the database after undergoing data integrity checks.

[1113] Step 2:

[1114] Data Summarization

[1115] The server summarizes the acquired educational curriculum data using natural language processing techniques, such as the BERT topic model and the T5 summarization model.

[1116] Input: Curriculum data

[1117] Output: Summarized curriculum data

[1118] What happens: The server is triggered by newly saved data and runs NLP algorithms to summarize key concepts and learning objectives, for example summarizing the "Solving Linear Equations" section as "Basic Solutions and Applications of Linear Equations."

[1119] Step 3:

[1120] Automatic generation of presentation materials

[1121] The server automatically generates presentation materials using the Google Slides API based on the summarized curriculum data.

[1122] Input: summarized curriculum data

[1123] Output: Generated presentation materials (Google Slides format)

[1124] What happens: The server loads the template slides, inserts the summary data into the appropriate slides, and optionally creates graphs and charts using the D3.js library and adds them to the slides.

[1125] Step 4:

[1126] Sending and getting feedback

[1127] The server sends the generated presentation materials to the experts, receives feedback, and posts the material link to a dedicated channel using the Slack API.

[1128] Input: Generated presentation materials

[1129] Output: Expert feedback

[1130] How it works: The server automatically posts a link to the document in a Slack channel, and experts can comment on it in Google Slides. The comments are sent to the server in real time.

[1131] Step 5:

[1132] Modifying materials based on feedback

[1133] The server then modifies the presentation materials based on the received feedback, and executes an automatic correction script depending on the feedback.

[1134] Input: Expert feedback

[1135] Output: Revised presentation

[1136] Specific actions: For example, if the feedback is that "the solution steps do not contain enough concrete examples," the server executes a correction script to add concrete examples and updates the materials.

[1137] Step 6:

[1138] Recording and distributing educational content

[1139] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then upload the videos to an online platform for distribution and archiving.

[1140] Input: Recorded lesson video

[1141] Output: Link to a video of the lesson that can be distributed via an online platform

[1142] How it works: Teachers record videos using a web application and submit them through a dedicated upload form. The server compresses the video and uploads it using the YouTube API. A link is automatically generated and users can access it.

[1143] Step 7:

[1144] Automatic generation of test questions

[1145] The server uses a generative AI model (e.g., GPT) to automatically generate test questions based on the learning content, including the answers.

[1146] Input: Curriculum data

[1147] Output: Test questions and their answers

[1148] How it works: The server uses a generative AI model to generate test questions based on the summary data. For example, it generates the question "Solve the following linear equation \(2x + 3 = 7\)" and the answer "\(x = 2\)."

[1149] Step 8:

[1150] User questions and AI answers

[1151] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time.

[1152] Input: User question

[1153] Output: Real-time answers by AI

[1154] Specific operation: When a user inputs a question into the educational platform, the server immediately analyzes the question and generates an appropriate answer. Prompt sentence: For example, in response to "I don't really understand how to solve a linear equation," the server responds with a concrete example and steps to solve the equation.

[1155] Step 9:

[1156] Emotion Recognition and Feedback

[1157] The server recognizes the user's emotions from their facial expressions and voice, and provides feedback and learning materials based on that information, using Microsoft Azure's Emotion API.

[1158] Input: User's facial expression data, voice data

[1159] Output: Feedback and learning materials based on emotion recognition

[1160] Specific operation: The server analyzes the user's emotional data in real time and generates additional explanations or encouraging messages if the user has difficulty understanding. For example, if a confused expression is recognized, the system will provide a more detailed explanation.

[1161] Step 10:

[1162] Individualized learning curriculum

[1163] The server adjusts the learning curriculum based on the user's emotional data, providing review materials and encouraging messages according to the user's progress and level of understanding.

[1164] Input: Emotion data, learning progress data

[1165] Output: personalized learning curriculum and encouragement messages

[1166] Specific operation: The server uses an emotion engine to analyze the user's motivation, and if progress is slow, it provides more review materials and generates and provides appropriate encouraging messages.

[1167] This allows the system to efficiently and effectively deliver educational content and personalize and optimize the user's learning experience.

[1168] (Application example 2)

[1169] 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."

[1170] Traditional educational systems face many challenges in maintaining the quality of educational content and managing learner motivation. It is particularly difficult to efficiently process large volumes of curriculum data and create high-quality teaching materials. It is also difficult to grasp learners' emotions and comprehension levels in real time and provide appropriate feedback based on that information. These challenges often result in a decline in learning motivation and a drop in learning efficiency.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) or other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected educators to an online platform and archiving them; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means for individually adjusting the learning curriculum using an emotion engine that recognizes the user's emotions. This enables improved quality of educational content and management of learner motivation.

[1172] "Ministry of Education, Culture, Sports, Science and Technology and other educational institutions" refers to the government ministries and agencies responsible for the administration of education, culture, sports, science and technology in Japan and other educational institutions.

[1173] "Curriculum data" refers to official curriculum and teaching plan data provided by the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions.

[1174] "Natural language processing technology" is a technology that allows machines to analyze and understand human language, and is used to summarize data and extract information.

[1175] "Slide materials" are materials used to visually present information in a presentation format.

[1176] An "expert" is someone with specialized knowledge and experience.

[1177] "Feedback" refers to providing evaluations and opinions.

[1178] "Correction" refers to correcting or revising deficiencies or improvements.

[1179] "Selected Educators" refers to education professionals who are selected based on specific criteria.

[1180] "Recording" refers to the act of recording video and audio.

[1181] An "online platform" is a system that provides certain services via the Internet.

[1182] "Archive distribution" refers to storing recorded content and making it available for later viewing.

[1183] "Test questions" refer to questions or tasks used to measure comprehension of the learning content.

[1184] "Generative AI" refers to systems that use artificial intelligence techniques to automatically perform specific tasks.

[1185] "Answer" refers to the correct response to a question.

[1186] A "question" is an inquiry for information or help.

[1187] An "AI model" refers to a model for performing data analysis and inference based on artificial intelligence.

[1188] "Real time" refers to immediate action or response.

[1189] An "emotion engine" is a system that analyzes a user's emotional state and provides appropriate feedback based on that.

[1190] A "curriculum" is an educational content and plan that is structured around specific learning objectives.

[1191] "Individually tailored" refers to responses and changes that are optimized for each individual.

[1192] The embodiment of the present invention is a system for efficiently generating educational content and optimizing a user's learning experience. Specific procedures for implementing the present invention and the hardware and software used will be described below.

[1193] The server first obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. This data is obtained by sending an API request to download it or by importing a CSV file that is updated periodically. The obtained curriculum data is then stored in a database. The requests library is used to obtain the data.

[1194] Next, we summarize the acquired curriculum data using natural language processing techniques. Specifically, we use the summarizer model from the transformers library. This model is used to summarize large-scale text data and extract important parts of the data.

[1195] The summarized data is automatically entered into slide decks, which are generated using the pptx library and pre-defined templates, and graphs and charts are automatically generated and added to the slides, if needed.

[1196] The generated slides are sent to experts who provide feedback. The experts then send their feedback to the server via their devices, and the server then corrects the slides based on that feedback. This correction includes both manual and automatic corrections.

[1197] Next, the selected educators will record the lessons and upload the video files to the server via their devices. The server will then archive the video files on an online platform for users to access at any time. Users can access the archived lesson videos using the provided link.

[1198] The server also uses AI to automatically generate test questions based on the learning content. These questions also include answers, and users can use them as practice questions. The generated test questions are customized according to the user's learning progress and are provided based on an individual learning plan.

[1199] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model (e.g., QAModel) to generate answers in real time, using the spacy library for natural language analysis.

[1200] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. This engine adjusts the progress of the learning curriculum individually, providing the user with the optimal learning environment.

[1201] For example, if a user submits a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the confusion and provide a detailed explanation, which will increase motivation and enable more efficient learning.

[1202] Example prompt sentence:

[1203] "Please tell me how to solve the following linear equation."

[1204] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1205] Step 1:

[1206] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions through API requests or periodically updated CSV files. The obtained data is in raw format (JSON or CSV) and is stored in a database. The input is data from educational institutions, and the output is curriculum data stored in the database.

[1207] Step 2:

[1208] The server summarizes the stored curriculum data using natural language processing techniques. Specifically, it applies the summarizer model from the transformers library to summarize long-form curriculum data into key concepts and learning objectives. The input is the curriculum data stored in the database, and the output is the summarized curriculum data.

[1209] Step 3:

[1210] The server automatically generates slides based on the summarized curriculum data. It uses the pptx library to insert the summary content into a pre-defined template. Graphs and charts are also automatically generated and added as needed. The input is the summarized curriculum data, and the output is the generated slides.

[1211] Step 4:

[1212] The server sends the generated slides to the expert and receives feedback. The expert then writes down their evaluation and opinions, which are then sent back to the server. The input is the generated slides, and the output is the feedback provided by the expert.

[1213] Step 5:

[1214] The server then modifies the slides based on the expert feedback. The modifications incorporate the improvements pointed out, and the data is reprocessed as necessary. The input is the expert feedback, and the output is the modified slides.

[1215] Step 6:

[1216] Selected educators record their lessons and upload the video files to a server via their devices. The server then archives the video files on an online platform and makes them accessible. The input is the recorded lesson video, and the output is the lesson video stored on the online platform.

[1217] Step 7:

[1218] The server automatically creates test questions based on the learning content using generative AI. The generative AI model analyzes the educational curriculum data and generates high-quality test questions and answers based on it. The input is the educational curriculum data, and the output is the generated test questions and answers.

[1219] Step 8:

[1220] Users submit questions through the educational platform. The submitted questions are received by the server and analyzed by the AI ​​model. As a result of the analysis, an appropriate answer is generated in real time. The input is the user's question, and the output is the answer provided by the AI ​​model.

[1221] Step 9:

[1222] The server uses an emotion engine to recognize the user's emotions and individually adjusts the learning curriculum. The emotion engine analyzes the user's emotional data during learning and provides appropriate feedback and learning materials based on that data. The input is the user's emotional data, and the output is an individually adjusted learning curriculum.

[1223] For example, if a user sends a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the user's confusion and provide a detailed explanation, deepening the user's understanding and increasing their motivation to learn.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] [Fourth embodiment]

[1228] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1229] 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.

[1230] 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).

[1231] 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.

[1232] 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.

[1233] 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).

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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."

[1241] The present invention relates to a system for digitizing educational curriculum data and providing high-quality educational content. The program processing of this system is explained below in natural language.

[1242] The server's role begins by obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions and summarizing it using natural language processing technology. Specifically, the server sends an API request to download curriculum data, analyzes it using natural language processing algorithms, extracts key concepts and learning objectives, and generates a summary text. For example, the curriculum section on "Solving Linear Equations" might be summarized as "Basic Solutions and Applications of Linear Equations."

[1243] The server then automatically generates slides based on the summarized curriculum. Predefined slide templates are used, and the summary data is inserted into each template. Graphs and charts are also automatically generated based on the numerical data and added to the slides. For example, a slide containing step-by-step instructions and charts for solving a linear equation can be generated.

[1244] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step does not provide enough concrete examples," and the slides are then modified based on that feedback.

[1245] In addition, selected teachers will record educational content and upload the video files to the server, which will then archive the video files on the online platform for users to access at any time. For example, a video lesson on solving linear equations will be uploaded and made available for users to watch.

[1246] Test questions based on the learning content are automatically created by the server using a generation AI. The questions also include answers, which users can use as practice questions. As a specific example, the problem "Solve the following linear equation" and its answer are generated.

[1247] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[1248] In this way, the system of the present invention efficiently digitizes curriculum data and makes it possible to provide high-quality educational content, which not only effectively addresses the issues of students not attending school and teacher shortages, but also enables learning regardless of location or time.

[1249] The processing flow will be explained below.

[1250] Step 1:

[1251] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending API requests to download the data or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[1252] Step 2:

[1253] The server then uses natural language processing techniques to summarize the acquired curriculum data. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. The summarized data is then stored back in the database.

[1254] Step 3:

[1255] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded, and the summary content is inserted in the appropriate positions. Graphs and charts are also generated and added to the slides as needed.

[1256] Step 4:

[1257] The server sends the generated slides to the expert via the terminal, who reviews the slides and provides feedback. This feedback is then sent from the terminal to the server.

[1258] Step 5:

[1259] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved and the server proceeds to the next step.

[1260] Step 6:

[1261] Selected teachers record educational content and upload the video files to a server via their devices, where they are saved in a specific format.

[1262] Step 7:

[1263] The server archives the uploaded video files on an online platform, and a link is generated and saved for users to access at any time.

[1264] Step 8:

[1265] The server uses AI to generate test questions based on the learning content, and answers are automatically generated for the generated test questions and stored in a database.

[1266] Step 9:

[1267] Users submit questions through the educational platform, which are entered in text format and sent to the server.

[1268] Step 10:

[1269] The server receives questions sent by users and uses AI models to generate answers in real time, which are then returned to the users.

[1270] The above are the specific processing steps performed by the server, the terminal, and the user.

[1271] Example 1

[1272] 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."

[1273] In today's educational environment, improving the quality of educational content requires efficient digitization of educational data and the effective creation of teaching materials based on that data. However, manually processing large amounts of educational data and creating appropriate teaching materials is a major challenge for teachers and educational institutions, requiring time and effort. Furthermore, there is a need for efficient means to provide high-quality education regardless of location or time, especially for students who are absent from school due to teacher shortages.

[1274] 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.

[1275] In this invention, the server includes: means for acquiring educational data from educational institutions; means for summarizing the acquired educational data using natural language processing technology; means for automatically generating slides based on the summarized educational data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as archives; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; and means for generating answers to the submitted questions in real time using an AI model. This enables efficient digitization of educational data and the provision of high-quality educational content.

[1276] An "educational institution" is any public or private organization that provides education-related data or information.

[1277] "Educational Data" means information or data related to education, such as curriculum, learning objectives, teaching materials, and test questions.

[1278] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1279] "Slide materials" are electronic presentation materials for visually presenting educational content.

[1280] An "expert" is someone who has advanced knowledge and skills in a particular field.

[1281] "Feedback" is the act of providing opinions or evaluations of information or services provided.

[1282] An "online platform" is a basic system for providing and sharing various services and content via the Internet.

[1283] "Archive distribution" refers to the saving and distribution of recorded content so that it can be viewed at a later date.

[1284] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[1285] A "test item" is a question or problem used to assess a learner's understanding or knowledge.

[1286] An "answer" is a correct response or interpretation to a test question or question.

[1287] An "education platform" is an online system that provides education-related services and content in a centralized manner.

[1288] An "AI model" is a complex computational model that combines artificial intelligence algorithms and analytical methods.

[1289] "Real time" refers to a short period of time in which a response is returned immediately after the user does something.

[1290] This invention is a system for digitizing educational data and providing high-quality educational content. This system automates the process of acquiring educational data from educational institutions and summarizing, processing, and distributing it.

[1291] At the heart of the system is a server, which performs multiple functions. The server has a means of acquiring educational data from educational institutions. This means, for example, downloading the educational data using an API request. The server then summarizes the acquired educational data using natural language processing techniques. To do this, it uses a generative AI model to extract and concisely summarize the important information in the educational data.

[1292] For example, the server might send the following prompt to the generative AI model:

[1293] Summarize the curriculum data and extract key concepts and learning objectives. As a specific example, summarize how to solve linear equations.

[1294] The server then automatically generates slides based on the summarized educational data. During this process, the summary data is inserted into a predefined slide template, and graphs and diagrams are automatically generated based on the numerical data. The slides are then sent to experts via the device, who review and provide feedback. As a concrete example, consider a scenario in which a mathematics teacher gives feedback such as, "This solution step does not provide enough concrete examples."

[1295] The server receives feedback, modifies the slides, and sends them back to the experts, improving the quality of the materials provided. Additionally, selected instructors record educational content and upload the video files from their devices to the server. The server then archives the video files on an online platform, making them accessible to users at any time.

[1296] The server also uses a generative AI model to automatically generate test questions based on the learning content. Examples of prompts sent by the server include:

[1297] Create test questions based on the following learning: Provide specific questions and solutions related to solving linear equations.

[1298] Users can submit questions through the educational platform, and the server generates answers in real time using AI models. For example, if a user asks, "I don't really know how to solve this linear equation," the server will instantly provide an explanation and sample answer.

[1299] This will enable the efficient digitization of educational data and the provision of high-quality educational content, and will also strengthen educational support for students who are experiencing teacher shortages or who are not attending school.

[1300] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1301] Step 1:

[1302] Acquisition of educational data

[1303] The server obtains educational data from the educational institution.

[1304] Specific operation: The server sends an API request to download educational data.

[1305] Input: API request (e.g., GET / education_curriculum)

[1306] Output: Retrieved educational data (e.g., detailed educational course information)

[1307] Step 2:

[1308] Education Data Summary

[1309] The educational data acquired by the server is summarized using natural language processing technology.

[1310] Specific operation: The server sends prompts to the generative AI model to extract key concepts and learning objectives.

[1311] Input: Captured educational data, prompt (e.g., "Please summarize the educational course data and extract key concepts and learning objectives.")

[1312] Output: Summary text (e.g. "Basic methods for solving linear equations and examples of their applications")

[1313] Step 3:

[1314] Automatic generation of slides

[1315] The server automatically generates slide materials based on the summarized educational data.

[1316] What it does: Inserts summary data into predefined slide templates and automatically generates graphs and charts.

[1317] Input: Summary text, slide template

[1318] Output: Generated slide deck (e.g., slide on solving linear equations)

[1319] Step 4:

[1320] Slide deck review and feedback

[1321] The generated slide materials are sent to the expert via the terminal.

[1322] Concrete action: An expert reviews a slide deck and sends feedback from the device to the server. For example, a math teacher requests the addition of a concrete example.

[1323] Input: Generated slide deck, expert feedback

[1324] Output: Slide deck with feedback

[1325] Step 5:

[1326] Editing slide materials

[1327] The server modifies the slide deck based on the expert feedback.

[1328] Specific operations: Modify templates and generate additional graphs and charts.

[1329] Input: Slide deck with feedback

[1330] Output: Revised slide deck

[1331] Step 6:

[1332] Recording and archiving educational content

[1333] Selected teachers use the devices to record educational content and upload the video files to the server.

[1334] What it does: Teachers record videos on their devices and upload them to a server, which then archives the videos on an online platform.

[1335] Input: Recorded lesson video

[1336] Output: Video archived on an online platform

[1337] Step 7:

[1338] Automatic generation of test questions

[1339] The server automatically creates test questions based on the learning content using a generative AI model.

[1340] Specific operation: The server sends a prompt to the generative AI model, which generates test questions and their answers.

[1341] Input: Study content, prompt (e.g. "Please create test questions based on the following study content")

[1342] Output: Generated test questions and their answers

[1343] Step 8:

[1344] User questions and real-time answers

[1345] A user submits a question through the education platform.

[1346] How it works: The user enters a question and presses the submit button. The server then uses the AI ​​model to analyze the question and generate an answer in real time.

[1347] Input: User question, prompt (e.g., "I'm not sure how to solve this linear equation.")

[1348] Output: Generated answers (e.g. explanations and sample answers)

[1349] Each of the above steps will enable the efficient digitization of educational data and the provision of high-quality educational content.

[1350] (Application example 1)

[1351] 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."

[1352] In modern education, efficient provision of high-quality educational content and deepening student understanding requires the organization of vast amounts of educational data and its presentation in an easy-to-understand manner. However, traditional methods require time-consuming manual creation of materials and feedback, making real-time support difficult. Furthermore, a lack of universally accessible educational platforms means that learning environments remain dependent on specific educational institutions. To solve these problems, an educational support system that utilizes advanced automation technology and the Internet is needed.

[1353] 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.

[1354] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected teachers to an online platform and distributing them as an archive; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means installed on a smartphone or smart glasses for delivering educational content and providing feedback. This allows for the efficient provision of high-quality educational content, rapid reflection of feedback, and the advancement of learning regardless of location or time.

[1355] "Curriculum data" refers to information stored in digital format that contains the contents of curricula and lesson plans established by educational institutions.

[1356] "Natural language processing technology" refers to technology that processes human language using computers, and includes systems that understand and generate text.

[1357] "Slide materials" are presentation materials consisting of a series of slides used to visually explain educational content.

[1358] An "expert" is a person who has specialized knowledge and experience in a particular field.

[1359] "Feedback" means evaluation and suggestions for improvement provided based on information and results received.

[1360] An "online platform" is a system that serves as the foundation for providing services and content via the Internet.

[1361] "Archive distribution" refers to storing recorded content so that users can access and view it later.

[1362] "Generative AI" is a system that automatically generates text and questions using artificial intelligence technology.

[1363] An "AI model" is an artificial intelligence algorithm and structure used to learn from data and perform specific tasks.

[1364] A "smartphone" is a mobile phone that has advanced information processing capabilities and is capable of running applications.

[1365] "Smart glasses" are wearable devices that have the ability to display information within the field of view and realize augmented reality.

[1366] "Educational content" refers to digital learning materials such as textbooks, videos, and workbooks used to provide education to learners.

[1367] The present invention relates to a system for distributing and providing feedback on educational content. Specific embodiments of this system will be described below.

[1368] System Configuration

[1369] The system consists of a server, terminals (smartphones or smart glasses), and users. The server is responsible for acquiring curriculum data from educational institutions and processing and distributing it. Meanwhile, the terminals function as an interface for users to receive and study educational content.

[1370] Hardware and Software

[1371] The server will be equipped with hardware with high-speed data processing capabilities and the following software:

[1372] Natural Language Processing Technology: Hugging Face transformers library

[1373] Data acquisition: requests library (Python)

[1374] Slide generation: python-pptx library

[1375] Question answering system: Hugging Face transformers library and pre-trained models (e.g., distilbert-base-cased-distilled-squad)

[1376] The smartphones and smart glasses used as terminals also have a corresponding application installed, which has the function of receiving and displaying educational content.

[1377] Overview of program processing

[1378] Acquisition and summary of curriculum data

[1379] First, the server retrieves curriculum data from educational institutions using an API. This data is then summarized using natural language processing techniques to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" is summarized as "Basic Solutions and Applications of Linear Equations."

[1380] Automatic generation of slides

[1381] The system then automatically generates slide decks based on the summarized data, using predefined slide templates to populate them with the appropriate summary data, graphs, and charts—for example, slides containing step-by-step instructions and charts for solving linear equations.

[1382] Feedback and Corrections

[1383] The generated slides are sent to experts via the terminal. The server improves the slides by receiving feedback from the experts. For example, if a math teacher gives feedback such as "This solution step does not include enough concrete examples," the slides are revised based on that feedback.

[1384] Distribution and archiving of lesson videos

[1385] In addition, videos of lessons recorded by selected instructors will be uploaded to the server and distributed through the online platform. Users can access these videos at any time. For example, a lesson video on solving linear equations will be uploaded and made available for users to watch.

[1386] Automatic generation of test questions

[1387] Test questions based on the learning content are automatically created using generative AI. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer will be generated.

[1388] Real-time question answering

[1389] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[1390] Examples of prompt statements

[1391] Examples of specific prompts include the following:

[1392] Example of input prompt:

[1393] Summarize what you learned about "Solving Linear Equations."

[1394] Example of generated results:

[1395] This article summarizes the basic methods and applications of solving linear equations. A linear equation is an equation that is calculated only once for one variable... (summary below)

[1396] The above is an embodiment of the present invention, which makes it possible to efficiently provide high-quality educational content, quickly incorporate feedback, and advance learning without being restricted by location or time.

[1397] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1398] Step 1:

[1399] The server retrieves curriculum data from the educational institution. The server uses an API to request and receive the required data, while verifying the accuracy and completeness of the data. The input is data from the educational institution's API endpoint, and the output is the retrieved curriculum data.

[1400] Step 2:

[1401] The server summarizes the acquired curriculum data using natural language processing techniques. The server uses natural language processing tools such as Hugging Face's transformers library to extract key concepts and learning objectives. The input is the acquired curriculum data, and the output is summarized text data.

[1402] Step 3:

[1403] The server automatically generates slide presentations based on the summarized data. It uses the python-pptx library to insert summarized data, graphs, and figures into predefined slide templates. The input is the summarized text data and data visualization information, and the output is the generated slide presentation file.

[1404] Step 4:

[1405] The server sends the generated slides to the terminal and requests the expert to review them. The expert reviews the slides via the terminal and provides feedback. The input is the generated slides, and the output is the feedback from the expert.

[1406] Step 5:

[1407] The server receives the expert's feedback and modifies the slide deck based on it. The server adjusts the slide template and content according to the expert's opinions and suggestions. The input is the expert's feedback and the original slide deck, and the output is the modified slide deck.

[1408] Step 6:

[1409] Selected faculty members record their classes and upload the video files to a server, which stores them in a digital archive and distributes them through an online platform. The input is the recorded class video, and the output is the archived video file and its distribution link.

[1410] Step 7:

[1411] The server generates test questions based on the learning content. It uses a generative AI model to automatically create formatted test questions and their answers. The input is the learning content and summary data, and the output is the generated test questions and their answers.

[1412] Step 8:

[1413] Users submit questions through the educational platform. The submitted questions are received by the server, which uses an AI model to analyze the questions and generate answers in real time. The input is the user's question, and the output is the generated answer.

[1414] Step 9:

[1415] The server sends the generated answer to the user through the educational platform. The terminal displays the answer to the user in real time and provides necessary educational support. The input is the generated answer, and the output is the answer content provided to the user.

[1416] The above are the specific processing steps of the system for implementing the invention. In this way, high-quality educational content and rapid feedback are provided, helping learners to deepen their understanding.

[1417] 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.

[1418] This invention relates to a system for digitizing educational curriculum data and providing high-quality educational content, and further optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions. Below, the program processing of this system is explained in natural language.

[1419] The server's role is to obtain curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. The data is downloaded by sending an API request or by importing a CSV file that is updated periodically. The obtained data is then stored in a database.

[1420] The server then summarizes the acquired curriculum data using natural language processing techniques. At this stage, topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored back in the database.

[1421] The server then automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted in the appropriate locations. Graphs and charts are also generated and added to the slides as needed. For example, a slide containing step-by-step instructions and charts for solving linear equations can be generated.

[1422] The generated slides are sent to an expert via the device. The expert reviews the slides and provides feedback on improvements and opinions. This feedback is sent from the device to the server, which then modifies the slides based on that feedback. For example, a math teacher might provide feedback such as "This solution step lacks concrete examples," and the slides are then modified based on that feedback.

[1423] Next, the selected teachers record the educational content and upload the video files to the server via their devices. The server then archives the video files on an online platform so that users can access them at any time. For example, a video lesson on solving linear equations can be uploaded and made available for users to watch.

[1424] The server automatically generates test questions based on the learning content using AI generation. The questions also include solutions, which users can use as practice questions. For example, a question such as "Solve the following linear equation" and its answer can be generated.

[1425] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time. For example, if a user submits a question such as "I don't really understand how to solve this linear equation," the AI ​​will respond with a sample answer and a detailed explanation.

[1426] Furthermore, an emotion engine is built into the system to recognize the user's emotions. The server analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. For example, if the user is having difficulty understanding something, the emotion engine will use that information to generate and provide additional explanations or encouraging messages.

[1427] Based on the emotional data, the server can individually adjust the progress of the learning curriculum and provide the user with an optimal learning environment. This increases motivation and enables efficient learning. The emotional engine automatically generates messages to increase the user's motivation, providing personalized support according to the learner's emotional state.

[1428] With the above configuration, the present invention can improve the quality of education and effectively address the issues of students not attending school and teacher shortages. Furthermore, by recognizing users' emotions and optimizing the learning experience, it is possible to provide a more fulfilling educational environment.

[1429] The processing flow will be explained below.

[1430] Step 1:

[1431] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions by sending an API request to download the data or by importing a periodically updated CSV file from a defined location. The obtained data is then stored in a database.

[1432] Step 2:

[1433] The server summarizes the acquired curriculum data using natural language processing techniques. Topic modeling and abstract summarization algorithms are applied to extract key concepts and learning objectives. For example, a curriculum section on "Solving Linear Equations" can be summarized as "Basic Solutions and Applications of Linear Equations." The summarized data is then stored in a database.

[1434] Step 3:

[1435] The server automatically generates slides based on the summarized curriculum data. Slide templates are loaded and the summary content is inserted into the appropriate slides. Furthermore, the server generates necessary graphs and charts based on the numerical data and incorporates them into the slides. For example, it generates slides that include step-by-step instructions on how to solve a linear equation and accompanying graphs to visually complement the explanation.

[1436] Step 4:

[1437] The generated slides are sent to the expert via the terminal, which then sends them to the expert's email address or via a dedicated review system. The slides are then sent via email or uploaded for review on a dedicated platform.

[1438] Step 5:

[1439] Experts review the slides and provide feedback, which is sent back to the system via the device. For example, a math teacher might say, "This solution step doesn't provide enough concrete examples." That feedback is then sent to the server via the device.

[1440] Step 6:

[1441] The server modifies the slides based on the expert's feedback, reflects the points pointed out, and regenerates the slides. The modified slides are saved in the database and proceed to the next step.

[1442] Step 7:

[1443] Selected teachers record educational content. The recorded lesson videos are uploaded to the server via their devices. The video files are saved in a specific format (e.g., MP4).

[1444] Step 8:

[1445] The server archives the uploaded video files on the online platform, and a link is generated and saved for users to access at any time. For example, a video lesson on solving linear equations can be uploaded and viewed by users.

[1446] Step 9:

[1447] The server automatically generates test questions based on the learning content using AI. The generated test questions also include the answers, which are saved in the database. For example, a question such as "Solve the following linear equation" and its answer are generated.

[1448] Step 10:

[1449] A user submits a question through the educational platform. The question is entered in text format and sent to the server. For example, a user submits a question such as, "I don't really understand how to solve this linear equation."

[1450] Step 11:

[1451] The server receives questions sent by users and uses an AI model to generate answers in real time. The generated answers are then returned to the users. For example, the AI ​​returns sample answers and detailed explanations in real time.

[1452] Step 12:

[1453] The server uses an emotion engine that recognizes the user's emotions to analyze the user's learning status in real time, for example, by detecting and analyzing the user's facial expressions and tone of voice through a camera or microphone.

[1454] Step 13:

[1455] The server adjusts the progress of the learning curriculum individually based on the emotion data. If the user has difficulty understanding, it provides additional explanations and supplementary materials. For example, if the user is confused, it provides additional lecture videos with supplementary explanations.

[1456] Step 14:

[1457] Based on the emotional data generated by the emotion engine, messages to boost motivation are automatically generated and sent to the user. For example, a message such as "You're doing great! You're almost at the next stage" can be generated and sent to the user.

[1458] The above are the specific processing steps performed by the server, the terminal, and the user.

[1459] Example 2

[1460] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1461] In modern education, effectively providing high-quality educational content is a key challenge. However, traditional educational systems lack the means to efficiently digitize curriculum data, summarize, create materials, incorporate feedback, distribute video lectures, generate test questions, respond to questions in real time, and recognize and provide feedback to learners. As a result, the quality of education declines and learners' motivation decreases. Furthermore, it is difficult to provide a data-driven, personalized learning curriculum.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1463] In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating presentation materials based on the summarized curriculum data; means for sending the generated presentation materials to experts and receiving feedback; means for revising the presentation materials based on the expert feedback; means for uploading lectures recorded by selected teachers to an online platform and distributing them as archives; means for creating test questions based on learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; means for providing feedback and learning materials according to the user's learning status using an emotion engine that recognizes the user's emotions; and means for individually adjusting the learning curriculum based on the emotion data. This makes it possible to efficiently and effectively provide educational content and create a personalized learning environment.

[1464] "Curriculum Data" refers to information about the curriculum provided by an educational institution, including, specifically, subjects, learning objectives, topics, learning procedures, etc.

[1465] "Natural language processing technology" refers to the technology of processing human language using a computer, and specifically includes text summarization, topic modeling, text classification, etc.

[1466] "Presentation materials" are materials that summarize the learning content in a visually easy-to-understand manner, and are generally in the form of slides or visual aids.

[1467] An "expert" is someone who has deep knowledge and experience in a particular field, and typically refers to educators and academics.

[1468] An "online platform" is a platform for providing services and content via the Internet, and in the education field it includes the distribution of video lessons and learning management systems.

[1469] "Generative AI" refers to artificial intelligence that generates text and images, specifically natural language generation and image generation.

[1470] An "AI model" is an artificial intelligence framework that uses machine learning or deep learning to perform specific tasks, such as classification, prediction, and generation.

[1471] An "emotion engine" is a system for analyzing a user's emotional state in real time, specifically identifying emotions using facial expression and voice recognition technology.

[1472] An "educational platform" is a web application that manages and provides education-related services and content in an integrated manner, and serves as the foundation for users to access and use learning content.

[1473] A "learning curriculum" is a plan or schedule that defines the progress of learning, and specifically includes the learning content, learning goals, and time allocation for each subject.

[1474] The present invention is a system for digitizing educational curriculum data and providing high-quality educational content, and in particular, optimizes the learning experience by combining it with an emotion engine that recognizes the user's emotions.

[1475] System Overview

[1476] This system consists of three elements: a server, a terminal, and a user. The server plays the main role of acquiring, processing, storing, summarizing, and generating data, while the terminal acts as data input and user interface. The user is a consumer of educational content, learning and providing feedback.

[1477] Hardware and Software Configuration

[1478] Server: A high-performance server is used for data processing and storage. MySQL is used as the database, and BERT and T5 models are used for natural language processing. Google Slides API is used to generate presentation materials, and Slack API is used to collect feedback.

[1479] Devices: Computers, tablets, and smartphones are used as interfaces between users and the server. Web applications are used for video recording and uploading.

[1480] Users: Consume content through the educational platform and use Microsoft Azure's Emotion API for emotion recognition technology.

[1481] Program processing

[1482] Data Acquisition and Summarization

[1483] The server acquires curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and educational institutions through API requests and CSV file imports. The acquired data is checked for consistency and stored in a database.

[1484] The server uses natural language processing techniques (BERT topic model and T5 summarization model) to summarize the curriculum data and extract key concepts and learning objectives.

[1485] Example: Summarize the section "Solving Linear Equations" as "Basic Solutions of Linear Equations and Applications."

[1486] Automatic generation of presentation materials

[1487] The server uses the summary data to generate presentation materials via the Google Slides API, and if necessary, creates graphs and charts using the D3.js library and adds them to the materials.

[1488] For example: A slide includes "Step-by-step instructions on solving linear equations and corresponding graphs."

[1489] Feedback and Corrections

[1490] The generated presentation materials are sent to experts via the terminal, and feedback is collected using the Slack API, and the materials are updated based on the feedback using an automatic correction script.

[1491] For example, if the feedback is that the solution steps do not contain enough concrete examples, insert additional concrete examples.

[1492] Recording and distributing educational content

[1493] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then compress the videos, upload them to the platform using the YouTube API, and generate an access link.

[1494] Example: A "Lesson video on solving linear equations" is uploaded and made available for users to watch.

[1495] Automatic generation of test questions

[1496] The server uses generative AI (e.g., GPT) to automatically generate test questions and answers based on the learning content.

[1497] Example: The problem is "Solve the following linear equation \(2x + 3 = 7\)" and the answer is "\( x = 2 \)".

[1498] User questions and AI answers

[1499] Users submit questions through the educational platform, and the server uses an AI model to analyze the questions and generate answers in real time.

[1500] Example: In response to the prompt "I don't really know how to solve a linear equation," provide detailed solution steps along with concrete examples.

[1501] Emotion Recognition and Feedback

[1502] The server uses an emotion engine to analyze emotions from the user's facial expressions and voice in real time while they are learning, and provides feedback and additional learning materials.

[1503] For example, if the user shows signs of confusion, the emotion engine can generate and provide additional explanations or encouraging messages.

[1504] Individualized learning curriculum

[1505] The server adjusts the learning curriculum based on the emotion data, providing additional review materials to users who are falling behind, and generating encouraging messages from the emotion engine to motivate users.

[1506] The present invention makes it possible to provide educational content efficiently and effectively and to create a personalized learning environment.

[1507] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1508] Step 1:

[1509] Acquisition of curriculum data

[1510] The server sends a request to the API endpoint provided by the educational institution to obtain the latest curriculum data. The obtained data is received in the form of a CSV file or API response.

[1511] Input: API request or CSV file path

[1512] Output: Curriculum data (JSON or CSV format)

[1513] Specific operation: The server sends a request to the API as a scheduled task every morning at 8:00 to receive new data, which is then stored in the database after undergoing data integrity checks.

[1514] Step 2:

[1515] Data Summarization

[1516] The server summarizes the acquired educational curriculum data using natural language processing techniques, such as the BERT topic model and the T5 summarization model.

[1517] Input: Curriculum data

[1518] Output: Summarized curriculum data

[1519] What happens: The server is triggered by newly saved data and runs NLP algorithms to summarize key concepts and learning objectives, for example summarizing the "Solving Linear Equations" section as "Basic Solutions and Applications of Linear Equations."

[1520] Step 3:

[1521] Automatic generation of presentation materials

[1522] The server automatically generates presentation materials using the Google Slides API based on the summarized curriculum data.

[1523] Input: summarized curriculum data

[1524] Output: Generated presentation materials (Google Slides format)

[1525] What happens: The server loads the template slides, inserts the summary data into the appropriate slides, and optionally creates graphs and charts using the D3.js library and adds them to the slides.

[1526] Step 4:

[1527] Sending and getting feedback

[1528] The server sends the generated presentation materials to the experts, receives feedback, and posts the material link to a dedicated channel using the Slack API.

[1529] Input: Generated presentation materials

[1530] Output: Expert feedback

[1531] How it works: The server automatically posts a link to the document in a Slack channel, and experts can comment on it in Google Slides. The comments are sent to the server in real time.

[1532] Step 5:

[1533] Modifying materials based on feedback

[1534] The server then modifies the presentation materials based on the received feedback, and executes an automatic correction script depending on the feedback.

[1535] Input: Expert feedback

[1536] Output: Revised presentation

[1537] Specific actions: For example, if the feedback is that "the solution steps do not contain enough concrete examples," the server runs a correction script to add concrete examples and updates the materials.

[1538] Step 6:

[1539] Recording and distributing educational content

[1540] Selected teachers will record their lessons using a web application and upload the video files to a server via their devices. The server will then upload the videos to an online platform for distribution and archiving.

[1541] Input: Recorded lesson video

[1542] Output: Link to a video of the lesson that can be distributed via an online platform

[1543] How it works: Teachers record videos using a web application and submit them through a dedicated upload form. The server compresses the video and uploads it using the YouTube API. A link is automatically generated and users can access it.

[1544] Step 7:

[1545] Automatic generation of test questions

[1546] The server uses a generative AI model (e.g., GPT) to automatically generate test questions based on the learning content, including the answers.

[1547] Input: Curriculum data

[1548] Output: Test questions and their answers

[1549] How it works: The server uses a generative AI model to generate test questions based on the summary data. For example, it generates the question "Solve the following linear equation \(2x + 3 = 7\)" and the answer "\(x = 2\)."

[1550] Step 8:

[1551] User questions and AI answers

[1552] Users submit questions through the educational platform, which are then analyzed by the server using an AI model to generate answers in real time.

[1553] Input: User question

[1554] Output: Real-time answers by AI

[1555] Specific operation: When a user inputs a question into the educational platform, the server immediately analyzes the question and generates an appropriate answer. Prompt sentence: For example, in response to "I don't really understand how to solve a linear equation," the server responds with a concrete example and steps to solve the equation.

[1556] Step 9:

[1557] Emotion Recognition and Feedback

[1558] The server recognizes the user's emotions from their facial expressions and voice, and provides feedback and learning materials based on that information, using Microsoft Azure's Emotion API.

[1559] Input: User's facial expression data, voice data

[1560] Output: Feedback and learning materials based on emotion recognition

[1561] Specific operation: The server analyzes the user's emotional data in real time and generates additional explanations or encouraging messages if the user has difficulty understanding. For example, if a confused expression is recognized, the system will provide a more detailed explanation.

[1562] Step 10:

[1563] Individualized learning curriculum

[1564] The server adjusts the learning curriculum based on the user's emotional data, providing review materials and encouraging messages according to the user's progress and level of understanding.

[1565] Input: Emotion data, learning progress data

[1566] Output: personalized learning curriculum and encouragement messages

[1567] Specific operation: The server uses an emotion engine to analyze the user's motivation, and if progress is slow, it provides more review materials and generates and provides appropriate encouraging messages.

[1568] This allows the system to efficiently and effectively deliver educational content and personalize and optimize the user's learning experience.

[1569] (Application example 2)

[1570] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1571] Traditional educational systems face many challenges in maintaining the quality of educational content and managing learner motivation. It is particularly difficult to efficiently process large volumes of curriculum data and create high-quality teaching materials. It is also difficult to grasp learners' emotions and comprehension levels in real time and provide appropriate feedback based on that information. These challenges often result in a decline in learning motivation and a drop in learning efficiency.

[1572] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) or other educational institutions; means for summarizing the acquired curriculum data using natural language processing technology; means for automatically generating slides based on the summarized curriculum data; means for sending the generated slides to experts and receiving feedback; means for revising the slides based on the expert feedback; means for uploading recorded lessons by selected educators to an online platform and archiving them; means for creating test questions based on the learning content using a generative AI and providing answers; means for users to submit questions through the educational platform; means for generating answers to the submitted questions in real time using an AI model; and means for individually adjusting the learning curriculum using an emotion engine that recognizes the user's emotions. This enables improved quality of educational content and management of learner motivation.

[1573] "Ministry of Education, Culture, Sports, Science and Technology and other educational institutions" refers to the government ministries and agencies responsible for the administration of education, culture, sports, science and technology in Japan and other educational institutions.

[1574] "Curriculum data" refers to official curriculum and teaching plan data provided by the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions.

[1575] "Natural language processing technology" is a technology that allows machines to analyze and understand human language, and is used to summarize data and extract information.

[1576] "Slide materials" are materials used to visually present information in a presentation format.

[1577] An "expert" is someone with specialized knowledge and experience.

[1578] "Feedback" refers to providing evaluations and opinions.

[1579] "Correction" refers to correcting or revising deficiencies or improvements.

[1580] "Selected Educators" refers to education professionals who are selected based on specific criteria.

[1581] "Recording" refers to the act of recording video and audio.

[1582] An "online platform" is a system that provides certain services via the Internet.

[1583] "Archive distribution" refers to storing recorded content and making it available for later viewing.

[1584] "Test questions" refer to questions or tasks used to measure comprehension of the learning content.

[1585] "Generative AI" refers to systems that use artificial intelligence techniques to automatically perform specific tasks.

[1586] "Answer" refers to the correct response to a question.

[1587] A "question" is an inquiry for information or help.

[1588] An "AI model" refers to a model for performing data analysis and inference based on artificial intelligence.

[1589] "Real time" refers to immediate action or response.

[1590] An "emotion engine" is a system that analyzes a user's emotional state and provides appropriate feedback based on that.

[1591] A "curriculum" is an educational content and plan that is structured around specific learning objectives.

[1592] "Individually tailored" refers to responses and changes that are optimized for each individual.

[1593] The embodiment of the present invention is a system for efficiently generating educational content and optimizing a user's learning experience. Specific procedures for implementing the present invention and the hardware and software used will be described below.

[1594] The server first obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions. This data is obtained by sending an API request to download it or by importing a CSV file that is updated periodically. The obtained curriculum data is then stored in a database. The requests library is used to obtain the data.

[1595] Next, we summarize the acquired curriculum data using natural language processing techniques. Specifically, we use the summarizer model from the transformers library. This model is used to summarize large-scale text data and extract important parts of the data.

[1596] The summarized data is automatically entered into slide decks, which are generated using the pptx library and pre-defined templates, and graphs and charts are automatically generated and added to the slides, if needed.

[1597] The generated slides are sent to experts who provide feedback. The experts then send their feedback to the server via their devices, and the server then corrects the slides based on that feedback. This correction includes both manual and automatic corrections.

[1598] Next, the selected educators will record the lessons and upload the video files to the server via their devices. The server will then archive the video files on an online platform for users to access at any time. Users can access the archived lesson videos using the provided link.

[1599] The server also uses AI to automatically generate test questions based on the learning content. These questions also include answers, and users can use them as practice questions. The generated test questions are customized according to the user's learning progress and are provided based on an individual learning plan.

[1600] Users can submit questions through the educational platform, which are then analyzed by the server using an AI model (e.g., QAModel) to generate answers in real time, using the spacy library for natural language analysis.

[1601] Furthermore, the system is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's emotions in real time and provides appropriate feedback and learning materials according to the user's learning status. This engine adjusts the progress of the learning curriculum individually, providing the user with the optimal learning environment.

[1602] For example, if a user submits a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the confusion and provide a detailed explanation, which will increase motivation and enable more efficient learning.

[1603] Example prompt sentence:

[1604] "Please tell me how to solve the following linear equation."

[1605] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1606] Step 1:

[1607] The server obtains curriculum data from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and other educational institutions through API requests or periodically updated CSV files. The obtained data is in raw format (JSON or CSV) and is stored in a database. The input is data from educational institutions, and the output is curriculum data stored in the database.

[1608] Step 2:

[1609] The server summarizes the stored curriculum data using natural language processing techniques. Specifically, it applies the summarizer model from the transformers library to summarize long-form curriculum data into key concepts and learning objectives. The input is the curriculum data stored in the database, and the output is the summarized curriculum data.

[1610] Step 3:

[1611] The server automatically generates slides based on the summarized curriculum data. It uses the pptx library to insert the summary content into a pre-defined template. Graphs and charts are also automatically generated and added as needed. The input is the summarized curriculum data, and the output is the generated slides.

[1612] Step 4:

[1613] The server sends the generated slides to the expert and receives feedback. The expert then writes down their evaluation and opinions, which are then sent back to the server. The input is the generated slides, and the output is the feedback provided by the expert.

[1614] Step 5:

[1615] The server then modifies the slides based on the expert feedback. The modifications incorporate the improvements pointed out, and the data is reprocessed as necessary. The input is the expert feedback, and the output is the modified slides.

[1616] Step 6:

[1617] Selected educators record their lessons and upload the video files to a server via their devices. The server then archives the video files on an online platform and makes them accessible. The input is the recorded lesson video, and the output is the lesson video stored on the online platform.

[1618] Step 7:

[1619] The server automatically creates test questions based on the learning content using generative AI. The generative AI model analyzes the educational curriculum data and generates high-quality test questions and answers based on it. The input is the educational curriculum data, and the output is the generated test questions and answers.

[1620] Step 8:

[1621] Users submit questions through the educational platform. The submitted questions are received by the server and analyzed by the AI ​​model. As a result of the analysis, an appropriate answer is generated in real time. The input is the user's question, and the output is the answer provided by the AI ​​model.

[1622] Step 9:

[1623] The server uses an emotion engine to recognize the user's emotions and individually adjusts the learning curriculum. The emotion engine analyzes the user's emotional data during learning and provides appropriate feedback and learning materials based on that data. The input is the user's emotional data, and the output is an individually adjusted learning curriculum.

[1624] For example, if a user sends a question such as "I don't know how to solve this linear equation," the emotion engine will recognize the user's confusion and provide a detailed explanation, deepening the user's understanding and increasing their motivation to learn.

[1625] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1626] 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.

[1627] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1628] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1629] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1630] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1631] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1632] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1633] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1634] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1635] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1636] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1637] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1638] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1639] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1640] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1641] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1642] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1643] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1644] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1645] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1646] The following is further disclosed regarding the above embodiment.

[1647] (Claim 1)

[1648] A means of obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions;

[1649] A means for summarizing the acquired educational curriculum data using natural language processing technology;

[1650] A means for automatically generating slides based on summarized curriculum data;

[1651] A means for sending the generated slide deck to experts and receiving feedback;

[1652] A means to revise slide materials based on expert feedback, and

[1653] Selected teachers will record their lessons and upload them to an online platform for archiving and distribution.

[1654] A method to create test questions based on learning content using generative AI and provide answers,

[1655] a means for users to submit questions through the educational platform;

[1656] A means of generating answers to submitted questions in real time using AI models; and

[1657] A system including:

[1658] (Claim 2)

[1659] 10. The system of claim 1, further comprising means for adding graphs and figures to the slide deck.

[1660] (Claim 3)

[1661] 10. The system of claim 1, further comprising: means for generating and providing a link to a user to access the archived video lectures.

[1662] "Example 1"

[1663] (Claim 1)

[1664] a means for obtaining educational data from educational institutions;

[1665] A means for summarizing the acquired educational data using natural language processing technology;

[1666] A means for automatically generating slide materials based on summarized educational data;

[1667] a means for sending the generated slide deck to an expert and receiving feedback;

[1668] A means to revise slide decks based on expert feedback;

[1669] Selected teachers will be able to record their lessons and upload them to an online platform for archiving and distribution.

[1670] A method to create test questions based on learning content using generative AI and provide answers,

[1671] a means for users to submit questions through the educational platform;

[1672] A means of generating answers to submitted questions in real time using AI models; and

[1673] A system including:

[1674] (Claim 2)

[1675] 10. The system of claim 1, further comprising means for adding graphs and figures to the slide deck.

[1676] (Claim 3)

[1677] 10. The system of claim 1, further comprising: means for generating and providing a link to a user to access the archived video lectures.

[1678] "Application Example 1"

[1679] (Claim 1)

[1680] A means of obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions;

[1681] A means for summarizing the acquired educational curriculum data using natural language processing technology;

[1682] A means for automatically generating slides based on summarized curriculum data;

[1683] A means for sending the generated slide deck to experts and receiving feedback;

[1684] A means to revise slide materials based on expert feedback, and

[1685] Selected teachers will record their lessons and upload them to an online platform for archiving and distribution.

[1686] A method to create test questions based on learning content using generative AI and provide answers,

[1687] a means for users to submit questions through the educational platform;

[1688] A means of generating answers to submitted questions in real time using AI models; and

[1689] A means of delivering and providing feedback on educational content, installed on smartphones or smart glasses;

[1690] A system including:

[1691] (Claim 2)

[1692] 10. The system of claim 1, further comprising means for adding graphs and figures to the slide deck.

[1693] (Claim 3)

[1694] 10. The system of claim 1, further comprising: means for generating and providing a link to a user to access the archived video lectures.

[1695] "Example 2: Combining Emotion Engines"

[1696] (Claim 1)

[1697] A means of obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions;

[1698] A means for summarizing the acquired educational curriculum data using natural language processing technology;

[1699] A means for automatically generating presentation materials based on the summarized curriculum data;

[1700] A means of sending the generated presentation materials to experts and receivin...

Claims

1. A means of obtaining curriculum data from the Ministry of Education, Culture, Sports, Science and Technology and other educational institutions; A means for summarizing the acquired educational curriculum data using natural language processing technology; A means for automatically generating slides based on summarized curriculum data; A means for sending the generated slide deck to experts and receiving feedback; A means to revise slide materials based on expert feedback, and Selected teachers will record their lessons and upload them to an online platform for archiving and distribution. A method to create test questions based on learning content using generative AI and provide answers, a means for users to submit questions through the educational platform; A means of generating answers to submitted questions in real time using AI models; and A system including:

2. 10. The system of claim 1, further comprising means for adding graphs and figures to the slide deck.

3. The system of claim 1 , further comprising: means for generating and providing a link to a user for accessing the archived video lectures.

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