system

Abstract

We provide the system. [Solution] A means of receiving learning requests from users, Means for crawling information from the internet and online learning platforms, A means of filtering and evaluating the collected information, A means for generating integrated learning content based on the user's learning intent, A means of providing the generated content to the user in an interactive format, A system that includes a means to record the user's learning progress and reflect it in the next learning session.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to modern rapid technological innovation, while individuals need to quickly acquire the skills required in the labor market, high-cost learning materials and time constraints have become barriers to self-study. Therefore, there is a need for a support system to promote learning efficiently at a low cost. In addition, it is necessary to find a method that can provide learning content optimized for individual users.

Means for Solving the Problems

[0005] This invention provides a system that receives user learning requests and crawls relevant information from the internet and online learning platforms. The collected information is evaluated using advanced filtering methods and integrated as learning content optimized for the user. This content is delivered to the user in an interactive format, with explanations and supplementary information added in real time. Furthermore, the system records the user's learning progress and improves the system based on feedback to more effectively support subsequent learning. These functions enable low-cost and efficient self-learning.

[0006] A "user" refers to an individual or legal entity that uses this system for learning.

[0007] A "learning request" refers to a request from a user to communicate to the system the specific content and goals they want to learn.

[0008] The "Internet" refers to a global computer network system used to search for and collect information.

[0009] An "online learning platform" refers to a service or website that provides learning content on the web.

[0010] "Crawling" refers to the process of traversing the internet to collect specific information and gather data.

[0011] "Filtering" refers to the process of selecting collected data based on evaluation criteria and extracting only the necessary information.

[0012] "Integration" refers to the process of combining data collected from multiple sources into a single, cohesive learning content.

[0013] "Dialogue-based learning" refers to a learning method in which the user and the system communicate with each other as the learning process progresses.

[0014] "Real-time" refers to a state where data and information are processed immediately and provided to the user without delay.

[0015] "Explanation" refers to a process that helps users understand by providing detailed explanations of the learning content.

[0016] "Supplementary information" refers to information added to the basic learning content to deepen understanding.

[0017] "Learning progress" refers to an indicator that shows how much a user has achieved with respect to a specific learning goal.

[0018] "Feedback" refers to a process where users convey their opinions and requests regarding the learning experience to the system.

[0019] "System" refers to a computer system that integrates these functions to provide learning support to users.

Brief Description of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the 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.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0034] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] The system of this invention primarily utilizes a server and a terminal to provide learning support to users. First, the system starts operating when the user requests what they want to learn. The user inputs their interests and the fields they want to learn into the terminal. For example, they might input, "I want to learn the basics of machine learning."

[0042] In response, the server analyzes the user's intent and extracts relevant keywords. Based on these keywords, the server crawls the internet and online learning platforms to collect necessary information. In this process, data is obtained from a wide range of sources to find content that matches the user's learning needs.

[0043] The collected information is filtered by an AI algorithm on the server to select only the most important and useful information. Then, based on this information, user-optimized learning content is integrated. For example, this might include materials explaining the fundamental theories of machine learning, video lectures, and practice problems.

[0044] The generated learning content is provided to the device and made available to the user. The user can begin learning based on this provided content and can ask questions or make requests to the server as needed. The server responds in real time, providing explanations and supplementary information to aid the user's understanding.

[0045] Learning progress and specific results are automatically recorded through the device. This data is sent to the server and used to create personalized feedback and future learning plans for each user. User feedback is also collected and used to improve the entire system. In this way, the system can continue to provide users with a consistently effective learning experience.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users enter learning requests using their devices. This information includes the topics they want to learn and their specific goals.

[0049] Step 2:

[0050] The server receives the user's request and analyzes the input using natural language processing technology. This allows it to extract relevant keywords.

[0051] Step 3:

[0052] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. This process gathers data from diverse sources.

[0053] Step 4:

[0054] The server filters the collected information using AI algorithms and evaluates it based on reliability and usefulness. The selected information is then ranked according to its importance.

[0055] Step 5:

[0056] The server integrates the evaluated information and generates learning content optimized for user requests. This includes various formats such as text, video, and audio.

[0057] Step 6:

[0058] The generated learning content is provided to the device, and the user progresses through the learning process by viewing it. During this process, the user can input questions or points of confusion into the server.

[0059] Step 7:

[0060] The server uses AI to respond to user questions in real time. It may provide additional explanations or supplementary information as needed.

[0061] Step 8:

[0062] The device automatically records the user's learning progress and sends that data to the server. This allows the user's performance and learning status to be reflected.

[0063] Step 9:

[0064] The server analyzes the acquired learning data and user feedback to make suggestions and customizations for the next learning session. This continuously improves the learning experience.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In modern education systems, it is difficult to quickly provide useful learning materials that meet the individual needs of learners. Finding appropriate materials from the vast amount of online information is time-consuming, and there are challenges in adequately developing mechanisms for utilizing learning progress and feedback in education.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for receiving learning requests from users, means for collecting information from networks and online education platforms, and means for classifying and evaluating the collected information. This makes it possible to comprehensively provide a learning experience tailored to the individual needs of users and support effective learning.

[0070] "Means for receiving learning requests from users" refers to a function that provides an interface for learners to communicate to the system the content and themes they wish to learn.

[0071] "Means of gathering information from networks and online education platforms" refers to the function of obtaining necessary data from information sources and learning resources accessible via the internet.

[0072] "Means for classifying and evaluating collected information" refers to algorithms and processes for organizing and selecting acquired information based on its effectiveness, relevance, and importance.

[0073] "Means for creating integrated educational materials based on the user's learning objectives" refers to a function for generating and presenting learning content optimized according to the learner's needs.

[0074] "Means of providing created materials to users interactively" refers to functions that interactively present generated learning content to learners and respond to feedback and questions.

[0075] "A means of recording users' learning progress and reflecting it in future lessons" refers to a function that records the content and achievements of learners and uses this information to inform future learning plans and content.

[0076] "Means for collecting user feedback and using it to improve the system" refers to a function that collects feedback and evaluations from learners and contributes to the improvement and optimization of the system.

[0077] The present invention aims to provide learners with educational materials tailored to their needs by utilizing networks and terminals. The system begins with the user inputting a learning request via a terminal. For example, a possible input might be, "I want to learn the basics of machine learning." This request is then sent from the terminal to the server.

[0078] The server uses natural language processing software (e.g., a common language processing library) to analyze user requests and extract key keywords. The server then collects relevant information from internet sources and online education platforms. Common web crawler software is used for this information collection.

[0079] The collected information is analyzed and filtered using AI algorithms (e.g., generative AI models) on the server. Here, the information is classified based on its usefulness and relevance. The selected information is then integrated into educational materials optimized for learning purposes. For example, it might be provided as a combination of documents explaining machine learning theory, video lectures, and practice problems.

[0080] The generated educational materials are sent to the device and provided interactively to the user. The user can progress through their learning based on this content and can obtain supplementary information from the server in real time by entering additional questions or requests.

[0081] Learning progress is automatically recorded on the device and sent to the server. The server uses this data to customize the next learning plan and provide feedback. It also collects user feedback and uses it to improve the system.

[0082] For example, a user might enter a prompt such as, "I want to learn the basics of machine learning. Please provide detailed explanations of linear regression and decision trees, along with practical exercises." The system can respond quickly and effectively to such specific requests, meeting the learner's needs.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user inputs the content or topic they want to learn via their device. This input is done using a keyboard or voice recognition. When the user inputs "I want to learn the basics of machine learning," this is recorded as a prompt on the device and sent to the server. The input is sent as text data.

[0086] Step 2:

[0087] The server analyzes the received prompt message using natural language processing software. This analysis extracts important keywords such as "machine learning" and "basics." Data processing involves tokenization of the text data and keyword extraction. As a result, the analyzed keywords are output.

[0088] Step 3:

[0089] The server crawls the internet and online education platforms based on the analyzed keywords. The crawling process collects relevant information and data. Keywords are required as input, and the collected information data is obtained as output. Web crawler software is used in this process.

[0090] Step 4:

[0091] The server filters the collected information using an AI algorithm. Here, data calculations are performed to evaluate relevance and usefulness, selecting the necessary information. The input is the collected information, and the output is filtered, effective learning content.

[0092] Step 5:

[0093] The server uses the selected information to generate optimal educational materials for the user. This data processing involves integrating various forms of information (text, videos, exercises, etc.). The input is filtered information, and the output is the completed educational material.

[0094] Step 6:

[0095] The server sends the generated educational materials back to the terminal. The terminal provides this to the user in an interactive format to support learning. The output is content that is visually displayed to the user, and the user uses this to progress in their learning.

[0096] Step 7:

[0097] The user's progress as they learn is recorded by their device. The device sends this information to a server, which then analyzes it to inform the next learning plan. The input is learning progress data, and the output is customized learning feedback and a plan.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] In modern times, the amount of information has increased due to the development of information and communication technology, but it is difficult for learners to find the most suitable learning content from among it. Furthermore, in order to maximize learning effectiveness, it is necessary to clearly indicate the next learning step according to the learner's progress, but there are limited systems that can do this effectively. The objective of this invention is to solve these problems and provide an efficient and effective learning process.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for receiving learning objectives from the user, means for collecting information from information and communication networks and online education platforms, and means for sorting and evaluating the collected information. This makes it possible to generate optimal educational content based on the user's learning intentions and to suggest appropriate learning steps according to the user's learning progress.

[0103] "Means for receiving learning objectives from users" refers to a device or method that performs the process of clearly inputting the content or field that a user wants to learn into a server.

[0104] "Means of collecting information from information and communication networks and online education platforms" refers to technologies or mechanisms for obtaining content from diverse online learning resources and platforms via the internet.

[0105] "Means for sorting and evaluating collected information" refers to methods of analyzing various acquired data and selecting and evaluating content based on importance and relevance.

[0106] "Means for generating integrated educational content based on the user's learning intent" refers to technology that combines information according to the user's learning needs and creates customized educational materials to maximize learning efficiency.

[0107] "Means of providing generated educational content to users in an interactive format" refers to a method or device for presenting educational materials interactively in a way that is easy for users to understand.

[0108] "Means for recording a user's learning progress and reflecting it in the next learning session" refers to a process or system that automatically saves the progress of learning and uses the results to inform the next learning plan.

[0109] "A means of suggesting the next optimal learning plan according to the learning progress" refers to a technology or system that suggests the next learning items and methods to proceed with based on the user's current learning status.

[0110] This invention is a system that utilizes information and communication technology to provide learners with optimized educational content. The system is centered around a server, and the process begins when the user inputs the content they wish to learn.

[0111] The server first receives the learning objectives sent from the user's terminal. Using natural language processing technology, it analyzes the user's input regarding areas of interest and automatically collects necessary information. During this process, servers running on AWS® or Google® Cloud Platform crawl the internet and online education platforms.

[0112] The analyzed data is filtered using AI algorithms built with TENSORFLOW® and PyTorch to generate highly relevant content for the user. The generated educational content is presented interactively on the user's smartphone or tablet, allowing the user to progress through their learning.

[0113] Furthermore, learning progress is recorded in the cloud and reflected in the next learning session. Based on the user's progress, the server recommends the next most suitable learning step. In this way, it is possible to always maximize the user's learning effectiveness.

[0114] For example, if a user requests to "learn about data analysis," the system selects relevant lecture videos and practice problems and presents them to the user. The user then proceeds with their learning based on the provided content and can request to "also understand the basics of machine learning." The server responds to this request in real time and provides appropriate supplementary materials.

[0115] An example of a prompt using a generative AI model is: "Generate optimal learning content based on the user's desired learning theme. Specific theme: Fundamentals of Data Science." This allows users to acquire new knowledge more efficiently and effectively.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user enters their learning objectives via their device. This input information is then transferred to a language analysis process. When specific fields or keywords are entered, the information is sent to the server and the analysis begins.

[0119] Step 2:

[0120] The server analyzes the received learning objectives using natural language processing (NLP) techniques. This analysis extracts relevant keywords and important information, and uses this information to prepare for data collection. Specifically, it applies an NLP (Natural Language Processing) model to clarify the user's intent.

[0121] Step 3:

[0122] The server crawls information and communication networks and online education platforms to collect relevant information. It searches a wide range of data from the internet and temporarily stores the results. At this point, it establishes a connection with the education platform using an API and collects the necessary data.

[0123] Step 4:

[0124] The collected data is filtered by an AI algorithm on the server. The specific input is the data obtained in the previous step, and the output is important information that matches the user's learning objectives. Machine learning models using TensorFlow or PyTorch are responsible for evaluating and organizing the data.

[0125] Step 5:

[0126] The server integrates filtered data and generates user-optimized educational content. The generated content consists of various forms, including videos, documents, and quizzes. The output directly impacts the user's desired learning outcomes.

[0127] Step 6:

[0128] The device provides the user with generated educational content. The user can then interact with the provided content and progress through their learning. Here, an interactive user interface acts as a bridge between the user and the content.

[0129] Step 7:

[0130] The user's learning progress is recorded by the device and sent to the server. This progress data is used to inform the next learning plan and to evaluate the user's performance. New learning steps are also recommended based on the progress.

[0131] This entire process allows users to gain a more efficient and personalized learning experience.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention enhances the user experience by combining an emotion engine with a learning support system. When a user enters a learning request through a terminal, the system starts operating, and the server analyzes the request. Based on the analysis results, the server crawls relevant information from the internet and online learning platforms and collects the necessary data.

[0134] The collected data is filtered, evaluated, and integrated by AI algorithms on the server. This integrated learning content is optimized to meet the user's needs. For example, a learning curriculum is created that includes everything from basic information to advanced content on a specific learning topic.

[0135] A key feature of this invention is the incorporation of an emotion engine. The server uses camera and microphone input acquired from the user's terminal to analyze the user's emotions from their facial expressions and voice tone. Based on the results of the emotion analysis, the server adjusts the learning content in real time. For example, if the user is losing interest, more interactive content or game elements can be included.

[0136] As the user progresses through the learning process, the emotion engine continuously monitors the user's state and accumulates changes in their emotions as data. This data is analyzed by the server and used to optimize future learning plans. Based on the user's emotions, personalized curricula are proposed that are tailored not only to learning progress but also to learning motivation and satisfaction.

[0137] In this way, users can always receive a learning experience that is adapted to their emotional state. For example, when a user is tired, the content is simplified, and when their concentration is high, more challenging tasks are provided. This allows users to continue learning effectively and sustainably.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users use their devices to submit requests, entering the content they want to learn and the topics they are interested in. These requests include specific keywords and detailed subject preferences.

[0141] Step 2:

[0142] The server receives the user's request and analyzes the request content using natural language processing technology. This analysis automatically extracts relevant keywords.

[0143] Step 3:

[0144] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. During this process, it searches for necessary information from a variety of databases and educational content.

[0145] Step 4:

[0146] The server filters the collected information and selects it according to usefulness and evaluation criteria. This extracts high-quality content that aligns with the user's learning intentions.

[0147] Step 5:

[0148] The server integrates the selected information and generates customized learning content for the user. This process constructs the chosen content into a logical learning path, for example, designing a curriculum that progresses from foundational knowledge to more specialized content.

[0149] Step 6:

[0150] The device provides the user with generated learning content, while the emotion engine analyzes the user's emotional state in real time. The device's camera and microphone are used to capture the user's facial expressions and voice, and this data is sent to the server.

[0151] Step 7:

[0152] The server analyzes emotional data received from the device and dynamically adjusts the difficulty level and format of the learning content based on the results. For example, if the user is feeling stressed, the content will be changed to something more relaxing.

[0153] Step 8:

[0154] Users continue their activities, deepening their understanding and solving problems according to the tailored learning content. They can ask questions via their device as needed.

[0155] Step 9:

[0156] The device records the user's learning progress and emotional changes, and sends this data to a server. This data is used for planning the next learning session and providing feedback for improvement.

[0157] Step 10:

[0158] The server integrates all data and continuously optimizes the user's long-term learning plan, ensuring a consistently effective learning experience.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In modern online education, typical educational systems provide uniform educational content without considering the emotional state of learners, making it difficult to sustain user motivation. This results in problems such as decreased learning efficiency and satisfaction. Furthermore, because the system does not reflect learning progress or results, it is difficult to provide a curriculum optimized for individual users.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for receiving information processing requests from users, means for collecting data from computer communication networks and online education infrastructure, and means for selecting and evaluating the collected data. This enables the provision of optimized educational content based on the user's individual learning needs and real-time content adjustment according to the user's emotional state.

[0164] An "information processing request" is a request regarding specific learning content or themes that a user sends to the system in order to advance their learning.

[0165] A "computer communication network" refers to an online information network where data is transmitted electronically, and includes various networks such as the Internet.

[0166] An "online education infrastructure" is a system or platform for providing educational content and services in a digital environment.

[0167] "Means of data collection" refers to the technical processes or devices used to acquire necessary information from external sources.

[0168] "Means for selecting and evaluating data" refers to a technical process or device that classifies collected information according to its importance and relevance, and determines its quality and usefulness.

[0169] "Means for generating educational materials" refers to a technical process or apparatus for creating content necessary for learning based on selected and evaluated data.

[0170] "Means for adjusting educational materials" refers to a technical process or device that modifies or optimizes the content provided based on the user's learning progress and emotional state.

[0171] "Means of collecting opinions" refers to a technical process or device for obtaining feedback from users.

[0172] This invention is an information processing system specifically designed for learning support, which analyzes the learner's emotional state in real time and provides an individually optimized learning experience.

[0173] The user inputs an information processing request from their device, indicating the content or topic they wish to learn about. The device then sends this request to the server. For example, suppose the user inputs, "I want to learn about important historical revolutions." The server receives this request via the computer network and uses an online education platform to collect relevant data. This process utilizes web crawling libraries and APIs.

[0174] Next, an AI algorithm on the server sorts the collected data and evaluates its relevance and reliability. Specific technologies used include data analysis libraries. Based on the sorted and evaluated data, the server generates educational materials that align with the user's learning style. These generated educational materials are provided to the user via a terminal, facilitating interactive learning.

[0175] Furthermore, a key feature of this invention is that the server can analyze the user's emotional state through the camera and microphone of the terminal. For example, it can use OpenCV or a speech analysis library to evaluate the user's level of interest from their facial expressions and voice tone. Based on the analysis results, it then adjusts the educational materials in real time. If it is determined that the user is bored, adjustments such as adding a game-style quiz are made.

[0176] This system can record the user's learning progress and incorporate feedback into future learning sessions. When a user provides feedback on their learning from their device, the server can store this information and use it to improve the system.

[0177] As a concrete example of a prompt, a request such as "Please provide detailed information and related multimedia content about important historical revolutions" can be used. This allows users to effectively acquire knowledge and maintain their interest as they progress through the learning process.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The user uses a terminal to input the topic or content they want to learn about. This input is sent to the server as an information processing request. Specifically, the user might input a prompt such as, "I want to learn about important historical revolutions." This prompt becomes the input data sent to the server.

[0181] Step 2:

[0182] The server analyzes the received information processing request. Using natural language processing techniques, it extracts relevant keywords and themes. The extracted keywords are used as foundational information for data collection. Specifically, the server identifies the keywords "history" and "revolution" and prepares for the data collection process.

[0183] Step 3:

[0184] The server collects data using computer communication networks and online education infrastructure. It utilizes web crawling technology to retrieve relevant materials from the internet. In this process, relevant literature and multimedia materials are stored on the server. The input is extracted keywords, and the output is the collected dataset.

[0185] Step 4:

[0186] The server sorts and evaluates the collected data. It uses data analysis libraries to determine the reliability and relevance of the information. Specifically, it removes inappropriate or duplicate data, leaving only highly reliable information. The input is the collected dataset, and the output is the filtered data.

[0187] Step 5:

[0188] The server generates educational materials using the selected data. A generation AI model is used to integrate the content into a format suitable for learners. Specifically, it constructs an educational curriculum that encompasses everything from basic knowledge to applied concepts. The input is filtered data, and the output is educational materials.

[0189] Step 6:

[0190] The server analyzes the user's emotional state through the camera and microphone on the terminal. Using AI technology, it infers the user's interest and concentration level from their facial expressions and voice tone. Based on this analysis, it adjusts educational materials in real time. The input is data of the user's facial expressions and voice, and the output is the adjusted educational materials.

[0191] Step 7:

[0192] As the user progresses through the learning process, the server records their progress and saves it in a database as feedback for the next session. User feedback is also collected and used to improve the system. Inputs are performance data during learning and user feedback, while outputs are learning history and improvement data.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] Traditional learning systems lacked the ability to dynamically adjust the learning experience based on user emotions, making it difficult to effectively improve user motivation and satisfaction. Furthermore, they lacked real-time content recommendation features that responded to user emotions, thus failing to provide an appropriate learning experience that met user expectations.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0197] In this invention, the server includes a device for receiving user requests, a device for collecting information from networks and digital education platforms, and a device for analyzing the user's emotions and dynamically adjusting the information according to those emotions. This makes it possible to provide an optimal learning experience based on the user's emotions.

[0198] A "device for receiving user requests" is a device that receives requests from users regarding learning or content.

[0199] A "device for collecting information from networks and digital education platforms" is a device for gathering necessary information from the internet and online learning platforms.

[0200] A "device for selecting and evaluating accumulated information" is a device that filters collected information as needed and determines its value.

[0201] A "device that generates integrated educational information based on the user's learning objectives" is a device for creating learning content that matches the user's wishes and goals.

[0202] A "device that provides generated information to the user in an interactive format" is a device that provides created content to the user in an interactive manner.

[0203] A "device that records user progress and uses it for future learning" is a device that records the progress of learning and uses that information to improve future learning opportunities.

[0204] A "device that analyzes user emotions and dynamically adjusts information according to those emotions" is a device that analyzes the user's facial expressions and voice and changes the learning content in real time based on their emotions at that moment.

[0205] This invention is a learning support system that dynamically provides a learning experience in response to the user's emotions. The server receives requests from users via a network. Users can use smart devices or computers to input requests regarding learning and content through an interface.

[0206] When a request is received, the server collects relevant information from the network and digital education platform. Specifically, it performs web crawling using Python libraries to extract necessary information from a vast dataset. This information is then filtered and evaluated using machine learning algorithms, based on the user's learning needs. Based on the collected information, the server generates content tailored to the user's learning objectives. It uses a generative AI model to create prompts that respond to user requests and customize the content accordingly. For example, it can use prompts such as, "What content would you recommend when the user's current mood is 'happy'?"

[0207] Furthermore, the server monitors the user's emotional state in real time and dynamically adjusts the content it provides. For example, if a user becomes bored with a video they are watching, the server incorporates highly entertaining elements into the video streaming app based on the emotional analysis data. OpenCV and natural language processing toolkits are used to analyze the user's facial expressions and tone of voice. In this way, users can always enjoy content optimized for their current emotions, improving learning efficiency and increasing satisfaction.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The terminal receives a request from the user. When the user enters a learning request through the interface, that information is passed to the terminal. This input includes specific learning topics and interests. The terminal sends this request to the server as a data packet.

[0211] Step 2:

[0212] The server collects information from networks and digital education platforms. Here, the server performs web crawling using received user requests. It leverages Python libraries to explore relevant resources and extract the necessary information. The input is the user's request, and the output is a list of relevant learning resources.

[0213] Step 3:

[0214] The server sorts and evaluates the collected information. It uses machine learning algorithms to select the information best suited to the user's requirements from the numerous collected learning resources. The input is the list of learning resources obtained in step 2, and the output is a subset of the most relevant information.

[0215] Step 4:

[0216] The server generates integrated educational information based on the user's learning objectives. This utilizes a generative AI model along with personalized prompts. Specifically, it might create a prompt such as, "Please provide content that will help the user relax." The input is selected learning information, and the output is personalized educational content.

[0217] Step 5:

[0218] The server provides generated information to the terminal in an interactive format. The user's terminal displays this information, allowing the user to interact with the content. The input is integrated educational information, and the output is the user's learning experience itself.

[0219] Step 6:

[0220] The system dynamically adjusts content based on the user's emotions. The server analyzes facial and voice data acquired from the device to determine the user's emotional state in real time. Content is added or modified as needed to maintain the user's interest. The input is emotion analysis data, and the output is the adjusted learning content.

[0221] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0237] The system of this invention primarily utilizes a server and a terminal to provide learning support to users. First, the system starts operating when the user requests what they want to learn. The user inputs their interests and the fields they want to learn into the terminal. For example, they might input, "I want to learn the basics of machine learning."

[0238] In response, the server analyzes the user's intent and extracts relevant keywords. Based on these keywords, the server crawls the internet and online learning platforms to collect necessary information. In this process, data is obtained from a wide range of sources to find content that matches the user's learning needs.

[0239] The collected information is filtered by an AI algorithm on the server to select only the most important and useful information. Then, based on this information, user-optimized learning content is integrated. For example, this might include materials explaining the fundamental theories of machine learning, video lectures, and practice problems.

[0240] The generated learning content is provided to the device and made available to the user. The user can begin learning based on this provided content and can ask questions or make requests to the server as needed. The server responds in real time, providing explanations and supplementary information to aid the user's understanding.

[0241] Learning progress and specific results are automatically recorded through the device. This data is sent to the server and used to create personalized feedback and future learning plans for each user. User feedback is also collected and used to improve the entire system. In this way, the system can continue to provide users with a consistently effective learning experience.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users enter learning requests using their devices. This information includes the topics they want to learn and their specific goals.

[0245] Step 2:

[0246] The server receives the user's request and analyzes the input using natural language processing technology. This allows it to extract relevant keywords.

[0247] Step 3:

[0248] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. This process gathers data from diverse sources.

[0249] Step 4:

[0250] The server filters the collected information using AI algorithms and evaluates it based on reliability and usefulness. The selected information is then ranked according to its importance.

[0251] Step 5:

[0252] The server integrates the evaluated information and generates learning content optimized for user requests. This includes various formats such as text, video, and audio.

[0253] Step 6:

[0254] The generated learning content is provided to the device, and the user progresses through the learning process by viewing it. During this process, the user can input questions or points of confusion into the server.

[0255] Step 7:

[0256] The server uses AI to respond to user questions in real time. It may provide additional explanations or supplementary information as needed.

[0257] Step 8:

[0258] The device automatically records the user's learning progress and sends that data to the server. This allows the user's performance and learning status to be reflected.

[0259] Step 9:

[0260] The server analyzes the acquired learning data and user feedback to make suggestions and customizations for the next learning session. This continuously improves the learning experience.

[0261] (Example 1)

[0262] Next, we will describe Example 1. 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."

[0263] In modern education systems, it is difficult to quickly provide useful learning materials that meet the individual needs of learners. Finding appropriate materials from the vast amount of online information is time-consuming, and there are challenges in adequately developing mechanisms for utilizing learning progress and feedback in education.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes means for receiving learning requests from users, means for collecting information from networks and online education platforms, and means for classifying and evaluating the collected information. This makes it possible to comprehensively provide a learning experience tailored to the individual needs of users and support effective learning.

[0266] "Means for receiving learning requests from users" refers to a function that provides an interface for learners to communicate to the system the content and themes they wish to learn.

[0267] "Means of gathering information from networks and online education platforms" refers to the function of obtaining necessary data from information sources and learning resources accessible via the internet.

[0268] "Means for classifying and evaluating collected information" refers to algorithms and processes for organizing and selecting acquired information based on its effectiveness, relevance, and importance.

[0269] "Means for creating integrated educational materials based on the user's learning objectives" refers to a function for generating and presenting learning content optimized according to the learner's needs.

[0270] "Means of providing created materials to users interactively" refers to functions that interactively present generated learning content to learners and respond to feedback and questions.

[0271] "A means of recording users' learning progress and reflecting it in future lessons" refers to a function that records the content and achievements of learners and uses this information to inform future learning plans and content.

[0272] "Means for collecting user feedback and using it to improve the system" refers to a function that collects feedback and evaluations from learners and contributes to the improvement and optimization of the system.

[0273] The present invention aims to provide learners with educational materials tailored to their needs by utilizing networks and terminals. The system begins with the user inputting a learning request via a terminal. For example, a possible input might be, "I want to learn the basics of machine learning." This request is then sent from the terminal to the server.

[0274] The server uses natural language processing software (e.g., a common language processing library) to analyze user requests and extract key keywords. The server then collects relevant information from internet sources and online education platforms. Common web crawler software is used for this information collection.

[0275] The collected information is analyzed and filtered using AI algorithms (e.g., generative AI models) on the server. Here, the information is classified based on its usefulness and relevance. The selected information is then integrated into educational materials optimized for learning purposes. For example, it might be provided as a combination of documents explaining machine learning theory, video lectures, and practice problems.

[0276] The generated educational materials are sent to the device and provided interactively to the user. The user can progress through their learning based on this content and can obtain supplementary information from the server in real time by entering additional questions or requests.

[0277] Learning progress is automatically recorded on the device and sent to the server. The server uses this data to customize the next learning plan and provide feedback. It also collects user feedback and uses it to improve the system.

[0278] For example, a user might enter a prompt such as, "I want to learn the basics of machine learning. Please provide detailed explanations of linear regression and decision trees, along with practical exercises." The system can respond quickly and effectively to such specific requests, meeting the learner's needs.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user inputs the content or topic they want to learn via their device. This input is done using a keyboard or voice recognition. When the user inputs "I want to learn the basics of machine learning," this is recorded as a prompt on the device and sent to the server. The input is sent as text data.

[0282] Step 2:

[0283] The server analyzes the received prompt text using natural language processing software. Through this analysis, important keywords such as "machine learning" and "basic" are extracted. As data processing, tokenization of text data and keyword extraction are performed. As a result, the analyzed keywords are output.

[0284] Step 3:

[0285] The server crawls the Internet and online education platforms based on the analyzed keywords. Through the crawling process, relevant information and data are collected. As input, keywords are required, and as output, the collected information data is obtained. In this process, web crawler software is used.

[0286] Step 4:

[0287] The server filters the collected information using an AI algorithm. Here, data operations are performed to evaluate relevance and usefulness, and necessary information is selected. The input is the collected information, and the output is the filtered effective learning content.

[0288] Step 5:

[0289] The server generates the optimal educational materials for the user using the selected information. Data processing here includes integrating various forms of information (text, video, exercise questions, etc.). The input is the filtered information, and the output is the completed educational materials.

[0290] Step 6:

[0291] The server sends back the generated educational materials to the terminal. The terminal provides this to the user in an interactive format to support learning. The output is the content visually displayed to the user, and the user proceeds with learning by viewing this.

[0292] Step 7:

[0293] The user's progress as they learn is recorded by their device. The device sends this information to a server, which then analyzes it to inform the next learning plan. The input is learning progress data, and the output is customized learning feedback and a plan.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] In modern times, the amount of information has increased due to the development of information and communication technology, but it is difficult for learners to find the most suitable learning content from among it. Furthermore, in order to maximize learning effectiveness, it is necessary to clearly indicate the next learning step according to the learner's progress, but there are limited systems that can do this effectively. The objective of this invention is to solve these problems and provide an efficient and effective learning process.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for receiving learning objectives from the user, means for collecting information from information and communication networks and online education platforms, and means for sorting and evaluating the collected information. This makes it possible to generate optimal educational content based on the user's learning intentions and to suggest appropriate learning steps according to the user's learning progress.

[0299] "Means for receiving learning objectives from users" refers to a device or method that performs the process of clearly inputting the content or field that a user wants to learn into a server.

[0300] "Means of collecting information from information and communication networks and online education platforms" refers to technologies or mechanisms for obtaining content from diverse online learning resources and platforms via the internet.

[0301] "Means for sorting and evaluating collected information" refers to methods of analyzing various acquired data and selecting and evaluating content based on importance and relevance.

[0302] "Means for generating integrated educational content based on the user's learning intent" refers to technology that combines information according to the user's learning needs and creates customized educational materials to maximize learning efficiency.

[0303] "Means of providing generated educational content to users in an interactive format" refers to a method or device for presenting educational materials interactively in a way that is easy for users to understand.

[0304] "Means for recording a user's learning progress and reflecting it in the next learning session" refers to a process or system that automatically saves the progress of learning and uses the results to inform the next learning plan.

[0305] "A means of suggesting the next optimal learning plan according to the learning progress" refers to a technology or system that suggests the next learning items and methods to proceed with based on the user's current learning status.

[0306] This invention is a system that utilizes information and communication technology to provide learners with optimized educational content. The system is centered around a server, and the process begins when the user inputs the content they wish to learn.

[0307] The server first receives the learning objective sent from the user's terminal. For the fields that the user is interested in, it analyzes the user input using natural language processing technology and automatically collects the necessary information. At this time, servers on AWS or Google Cloud Platform are operating to crawl the Internet and online education platforms.

[0308] The analyzed data is filtered by AI algorithms built using TensorFlow or PyTorch to generate content highly relevant to the user. The generated educational content is presented in an interactive format on the user's smartphone or tablet terminal, through which the user can proceed with learning.

[0309] Furthermore, the learning progress is recorded on the cloud and a mechanism is implemented to reflect it in the next learning content. Based on the user's progress, the server recommends the next most suitable learning step. In this way, it is always possible to maximize the user's learning effect.

[0310] As a specific example, when the user requests "want to learn the basics of data analysis", the system selects relevant lecture videos and exercise questions and presents them to the user. The user proceeds with learning based on the provided content and can additionally request "also want to understand the basics of machine learning". The server responds to that request in real time and provides appropriate supplementary materials.

[0311] Examples of prompt texts using the generated AI model include the following. "Please generate the optimal learning content based on the learning theme desired by the user. Specific theme: Basics of data science". In this way, the user can acquire new knowledge more efficiently and effectively.

[0312] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0313] Step 1:

[0314] The user enters their learning objectives via their device. This input information is then transferred to a language analysis process. When specific fields or keywords are entered, the information is sent to the server and the analysis begins.

[0315] Step 2:

[0316] The server analyzes the received learning objectives using natural language processing (NLP) techniques. This analysis extracts relevant keywords and important information, and uses this information to prepare for data collection. Specifically, it applies an NLP (Natural Language Processing) model to clarify the user's intent.

[0317] Step 3:

[0318] The server crawls information and communication networks and online education platforms to collect relevant information. It searches a wide range of data from the internet and temporarily stores the results. At this point, it establishes a connection with the education platform using an API and collects the necessary data.

[0319] Step 4:

[0320] The collected data is filtered by an AI algorithm on the server. The specific input is the data obtained in the previous step, and the output is important information that matches the user's learning objectives. Machine learning models using TensorFlow or PyTorch are responsible for evaluating and organizing the data.

[0321] Step 5:

[0322] The server integrates filtered data and generates user-optimized educational content. The generated content consists of various forms, including videos, documents, and quizzes. The output directly impacts the user's desired learning outcomes.

[0323] Step 6:

[0324] The device provides the user with generated educational content. The user can then interact with the provided content and progress through their learning. Here, an interactive user interface acts as a bridge between the user and the content.

[0325] Step 7:

[0326] The user's learning progress is recorded by the device and sent to the server. This progress data is used to inform the next learning plan and to evaluate the user's performance. New learning steps are also recommended based on the progress.

[0327] This entire process allows users to gain a more efficient and personalized learning experience.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] This invention enhances the user experience by combining an emotion engine with a learning support system. When a user enters a learning request through a terminal, the system starts operating, and the server analyzes the request. Based on the analysis results, the server crawls relevant information from the internet and online learning platforms and collects the necessary data.

[0330] The collected data is filtered, evaluated, and integrated by AI algorithms on the server. This integrated learning content is optimized to meet the user's needs. For example, a learning curriculum is created that includes everything from basic information to advanced content on a specific learning topic.

[0331] A key feature of this invention is the incorporation of an emotion engine. The server uses camera and microphone input acquired from the user's terminal to analyze the user's emotions from their facial expressions and voice tone. Based on the results of the emotion analysis, the server adjusts the learning content in real time. For example, if the user is losing interest, more interactive content or game elements can be included.

[0332] As the user progresses through the learning process, the emotion engine continuously monitors the user's state and accumulates changes in their emotions as data. This data is analyzed by the server and used to optimize future learning plans. Based on the user's emotions, personalized curricula are proposed that are tailored not only to learning progress but also to learning motivation and satisfaction.

[0333] In this way, users can always receive a learning experience that is adapted to their emotional state. For example, when a user is tired, the content is simplified, and when their concentration is high, more challenging tasks are provided. This allows users to continue learning effectively and sustainably.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] Users use their devices to submit requests, entering the content they want to learn and the topics they are interested in. These requests include specific keywords and detailed subject preferences.

[0337] Step 2:

[0338] The server receives the user's request and analyzes the request content using natural language processing technology. This analysis automatically extracts relevant keywords.

[0339] Step 3:

[0340] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. During this process, it searches for necessary information from a variety of databases and educational content.

[0341] Step 4:

[0342] The server filters the collected information and selects it according to usefulness and evaluation criteria. This extracts high-quality content that aligns with the user's learning intentions.

[0343] Step 5:

[0344] The server integrates the selected information and generates customized learning content for the user. This process constructs the chosen content into a logical learning path, for example, designing a curriculum that progresses from foundational knowledge to more specialized content.

[0345] Step 6:

[0346] The device provides the user with generated learning content, while the emotion engine analyzes the user's emotional state in real time. The device's camera and microphone are used to capture the user's facial expressions and voice, and this data is sent to the server.

[0347] Step 7:

[0348] The server analyzes emotional data received from the device and dynamically adjusts the difficulty level and format of the learning content based on the results. For example, if the user is feeling stressed, the content will be changed to something more relaxing.

[0349] Step 8:

[0350] Users continue their activities, deepening their understanding and solving problems according to the tailored learning content. They can ask questions via their device as needed.

[0351] Step 9:

[0352] The device records the user's learning progress and emotional changes, and sends this data to a server. This data is used for planning the next learning session and providing feedback for improvement.

[0353] Step 10:

[0354] The server integrates all data and continuously optimizes the user's long-term learning plan, ensuring a consistently effective learning experience.

[0355] (Example 2)

[0356] Next, we will describe Example 2. 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".

[0357] In modern online education, typical educational systems provide uniform educational content without considering the emotional state of learners, making it difficult to sustain user motivation. This results in problems such as decreased learning efficiency and satisfaction. Furthermore, because the system does not reflect learning progress or results, it is difficult to provide a curriculum optimized for individual users.

[0358] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0359] In this invention, the server includes means for receiving information processing requests from users, means for collecting data from computer communication networks and online education infrastructure, and means for selecting and evaluating the collected data. This enables the provision of optimized educational content based on the user's individual learning needs and real-time content adjustment according to the user's emotional state.

[0360] An "information processing request" is a request regarding specific learning content or themes that a user sends to the system in order to advance their learning.

[0361] A "computer communication network" refers to an online information network where data is transmitted electronically, and includes various networks such as the Internet.

[0362] An "online education infrastructure" is a system or platform for providing educational content and services in a digital environment.

[0363] "Means of data collection" refers to the technical processes or devices used to acquire necessary information from external sources.

[0364] "Means for selecting and evaluating data" refers to a technical process or device that classifies collected information according to its importance and relevance, and determines its quality and usefulness.

[0365] "Means for generating educational materials" refers to a technical process or apparatus for creating content necessary for learning based on selected and evaluated data.

[0366] "Means for adjusting educational materials" refers to a technical process or device that modifies or optimizes the content provided based on the user's learning progress and emotional state.

[0367] "Means of collecting opinions" refers to a technical process or device for obtaining feedback from users.

[0368] This invention is an information processing system specifically designed for learning support, which analyzes the learner's emotional state in real time and provides an individually optimized learning experience.

[0369] The user inputs an information processing request from their device, indicating the content or topic they wish to learn about. The device then sends this request to the server. For example, suppose the user inputs, "I want to learn about important historical revolutions." The server receives this request via the computer network and uses an online education platform to collect relevant data. This process utilizes web crawling libraries and APIs.

[0370] Next, an AI algorithm on the server sorts the collected data and evaluates its relevance and reliability. Specific technologies used include data analysis libraries. Based on the sorted and evaluated data, the server generates educational materials that align with the user's learning style. These generated educational materials are provided to the user via a terminal, facilitating interactive learning.

[0371] Furthermore, a key feature of this invention is that the server can analyze the user's emotional state through the camera and microphone of the terminal. For example, it can use OpenCV or a speech analysis library to evaluate the user's level of interest from their facial expressions and voice tone. Based on the analysis results, it then adjusts the educational materials in real time. If it is determined that the user is bored, adjustments such as adding a game-style quiz are made.

[0372] This system can record the user's learning progress and incorporate feedback into future learning sessions. When a user provides feedback on their learning from their device, the server can store this information and use it to improve the system.

[0373] As a concrete example of a prompt, a request such as "Please provide detailed information and related multimedia content about important historical revolutions" can be used. This allows users to effectively acquire knowledge and maintain their interest as they progress through the learning process.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The user uses a terminal to input the topic or content they want to learn about. This input is sent to the server as an information processing request. Specifically, the user might input a prompt such as, "I want to learn about important historical revolutions." This prompt becomes the input data sent to the server.

[0377] Step 2:

[0378] The server analyzes the received information processing request. Using natural language processing techniques, it extracts relevant keywords and themes. The extracted keywords are used as foundational information for data collection. Specifically, the server identifies the keywords "history" and "revolution" and prepares for the data collection process.

[0379] Step 3:

[0380] The server collects data using computer communication networks and online education infrastructure. It utilizes web crawling technology to retrieve relevant materials from the internet. In this process, relevant literature and multimedia materials are stored on the server. The input is extracted keywords, and the output is the collected dataset.

[0381] Step 4:

[0382] The server sorts and evaluates the collected data. It uses data analysis libraries to determine the reliability and relevance of the information. Specifically, it removes inappropriate or duplicate data, leaving only highly reliable information. The input is the collected dataset, and the output is the filtered data.

[0383] Step 5:

[0384] The server generates educational materials using the selected data. A generation AI model is used to integrate the content into a format suitable for learners. Specifically, it constructs an educational curriculum that encompasses everything from basic knowledge to applied concepts. The input is filtered data, and the output is educational materials.

[0385] Step 6:

[0386] The server analyzes the user's emotional state through the camera and microphone on the terminal. Using AI technology, it infers the user's interest and concentration level from their facial expressions and voice tone. Based on this analysis, it adjusts educational materials in real time. The input is data of the user's facial expressions and voice, and the output is the adjusted educational materials.

[0387] Step 7:

[0388] As the user progresses through the learning process, the server records their progress and saves it in a database as feedback for the next session. User feedback is also collected and used to improve the system. Inputs are performance data during learning and user feedback, while outputs are learning history and improvement data.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0391] Traditional learning systems lacked the ability to dynamically adjust the learning experience based on user emotions, making it difficult to effectively improve user motivation and satisfaction. Furthermore, they lacked real-time content recommendation features that responded to user emotions, thus failing to provide an appropriate learning experience that met user expectations.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes a device for receiving user requests, a device for collecting information from networks and digital education platforms, and a device for analyzing the user's emotions and dynamically adjusting the information according to those emotions. This makes it possible to provide an optimal learning experience based on the user's emotions.

[0394] A "device for receiving user requests" is a device that receives requests from users regarding learning or content.

[0395] A "device for collecting information from networks and digital education platforms" is a device for gathering necessary information from the internet and online learning platforms.

[0396] A "device for selecting and evaluating accumulated information" is a device that filters collected information as needed and determines its value.

[0397] A "device that generates integrated educational information based on the user's learning objectives" is a device for creating learning content that matches the user's wishes and goals.

[0398] A "device that provides generated information to the user in an interactive format" is a device that provides created content to the user in an interactive manner.

[0399] A "device that records user progress and uses it for future learning" is a device that records the progress of learning and uses that information to improve future learning opportunities.

[0400] A "device that analyzes user emotions and dynamically adjusts information according to those emotions" is a device that analyzes the user's facial expressions and voice and changes the learning content in real time based on their emotions at that moment.

[0401] This invention is a learning support system that dynamically provides a learning experience in response to the user's emotions. The server receives requests from users via a network. Users can use smart devices or computers to input requests regarding learning and content through an interface.

[0402] When a request is received, the server collects relevant information from the network and digital education platform. Specifically, it performs web crawling using Python libraries to extract necessary information from a vast dataset. This information is then filtered and evaluated using machine learning algorithms, based on the user's learning needs. Based on the collected information, the server generates content tailored to the user's learning objectives. It uses a generative AI model to create prompts that respond to user requests and customize the content accordingly. For example, it can use prompts such as, "What content would you recommend when the user's current mood is 'happy'?"

[0403] Furthermore, the server monitors the user's emotional state in real time and dynamically adjusts the content it provides. For example, if a user becomes bored with a video they are watching, the server incorporates highly entertaining elements into the video streaming app based on the emotional analysis data. OpenCV and natural language processing toolkits are used to analyze the user's facial expressions and tone of voice. In this way, users can always enjoy content optimized for their current emotions, improving learning efficiency and increasing satisfaction.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The terminal receives a request from the user. When the user enters a learning request through the interface, that information is passed to the terminal. This input includes specific learning topics and interests. The terminal sends this request to the server as a data packet.

[0407] Step 2:

[0408] The server collects information from networks and digital education platforms. Here, the server performs web crawling using received user requests. It leverages Python libraries to explore relevant resources and extract the necessary information. The input is the user's request, and the output is a list of relevant learning resources.

[0409] Step 3:

[0410] The server sorts and evaluates the collected information. It uses machine learning algorithms to select the information best suited to the user's requirements from the numerous collected learning resources. The input is the list of learning resources obtained in step 2, and the output is a subset of the most relevant information.

[0411] Step 4:

[0412] The server generates integrated educational information based on the user's learning objectives. This utilizes a generative AI model along with personalized prompts. Specifically, it might create a prompt such as, "Please provide content that will help the user relax." The input is selected learning information, and the output is personalized educational content.

[0413] Step 5:

[0414] The server provides generated information to the terminal in an interactive format. The user's terminal displays this information, allowing the user to interact with the content. The input is integrated educational information, and the output is the user's learning experience itself.

[0415] Step 6:

[0416] The system dynamically adjusts content based on the user's emotions. The server analyzes facial and voice data acquired from the device to determine the user's emotional state in real time. Content is added or modified as needed to maintain the user's interest. The input is emotion analysis data, and the output is the adjusted learning content.

[0417] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0419] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0423] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0424] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0425] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0427] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0428] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0429] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0430] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0431] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0432] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0433] The system of this invention primarily utilizes a server and a terminal to provide learning support to users. First, the system starts operating when the user requests what they want to learn. The user inputs their interests and the fields they want to learn into the terminal. For example, they might input, "I want to learn the basics of machine learning."

[0434] In response, the server analyzes the user's intent and extracts relevant keywords. Based on these keywords, the server crawls the internet and online learning platforms to collect necessary information. In this process, data is obtained from a wide range of sources to find content that matches the user's learning needs.

[0435] The collected information is filtered by an AI algorithm on the server to select only the most important and useful information. Then, based on this information, user-optimized learning content is integrated. For example, this might include materials explaining the fundamental theories of machine learning, video lectures, and practice problems.

[0436] The generated learning content is provided to the device and made available to the user. The user can begin learning based on this provided content and can ask questions or make requests to the server as needed. The server responds in real time, providing explanations and supplementary information to aid the user's understanding.

[0437] Learning progress and specific results are automatically recorded through the device. This data is sent to the server and used to create personalized feedback and future learning plans for each user. User feedback is also collected and used to improve the entire system. In this way, the system can continue to provide users with a consistently effective learning experience.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] Users enter learning requests using their devices. This information includes the topics they want to learn and their specific goals.

[0441] Step 2:

[0442] The server receives the user's request and analyzes the input using natural language processing technology. This allows it to extract relevant keywords.

[0443] Step 3:

[0444] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. This process gathers data from diverse sources.

[0445] Step 4:

[0446] The server filters the collected information using AI algorithms and evaluates it based on reliability and usefulness. The selected information is then ranked according to its importance.

[0447] Step 5:

[0448] The server integrates the evaluated information and generates learning content optimized for user requests. This includes various formats such as text, video, and audio.

[0449] Step 6:

[0450] The generated learning content is provided to the device, and the user progresses through the learning process by viewing it. During this process, the user can input questions or points of confusion into the server.

[0451] Step 7:

[0452] The server uses AI to respond to user questions in real time. It may provide additional explanations or supplementary information as needed.

[0453] Step 8:

[0454] The device automatically records the user's learning progress and sends that data to the server. This allows the user's performance and learning status to be reflected.

[0455] Step 9:

[0456] The server analyzes the acquired learning data and user feedback to make suggestions and customizations for the next learning session. This continuously improves the learning experience.

[0457] (Example 1)

[0458] Next, we will describe Example 1. 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."

[0459] In modern education systems, it is difficult to quickly provide useful learning materials that meet the individual needs of learners. Finding appropriate materials from the vast amount of online information is time-consuming, and there are challenges in adequately developing mechanisms for utilizing learning progress and feedback in education.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for receiving learning requests from users, means for collecting information from networks and online education platforms, and means for classifying and evaluating the collected information. This makes it possible to comprehensively provide a learning experience tailored to the individual needs of users and support effective learning.

[0462] "Means for receiving learning requests from users" refers to a function that provides an interface for learners to communicate to the system the content and themes they wish to learn.

[0463] "Means of gathering information from networks and online education platforms" refers to the function of obtaining necessary data from information sources and learning resources accessible via the internet.

[0464] "Means for classifying and evaluating collected information" refers to algorithms and processes for organizing and selecting acquired information based on its effectiveness, relevance, and importance.

[0465] "Means for creating integrated educational materials based on the user's learning objectives" refers to a function for generating and presenting learning content optimized according to the learner's needs.

[0466] "Means of providing created materials to users interactively" refers to functions that interactively present generated learning content to learners and respond to feedback and questions.

[0467] "A means of recording users' learning progress and reflecting it in future lessons" refers to a function that records the content and achievements of learners and uses this information to inform future learning plans and content.

[0468] "Means for collecting user feedback and using it to improve the system" refers to a function that collects feedback and evaluations from learners and contributes to the improvement and optimization of the system.

[0469] The present invention aims to provide learners with educational materials tailored to their needs by utilizing networks and terminals. The system begins with the user inputting a learning request via a terminal. For example, a possible input might be, "I want to learn the basics of machine learning." This request is then sent from the terminal to the server.

[0470] The server uses natural language processing software (e.g., a common language processing library) to analyze user requests and extract key keywords. The server then collects relevant information from internet sources and online education platforms. Common web crawler software is used for this information collection.

[0471] The collected information is analyzed and filtered using AI algorithms (e.g., generative AI models) on the server. Here, the information is classified based on its usefulness and relevance. The selected information is then integrated into educational materials optimized for learning purposes. For example, it might be provided as a combination of documents explaining machine learning theory, video lectures, and practice problems.

[0472] The generated educational materials are sent to the device and provided interactively to the user. The user can progress through their learning based on this content and can obtain supplementary information from the server in real time by entering additional questions or requests.

[0473] Learning progress is automatically recorded on the device and sent to the server. The server uses this data to customize the next learning plan and provide feedback. It also collects user feedback and uses it to improve the system.

[0474] For example, a user might enter a prompt such as, "I want to learn the basics of machine learning. Please provide detailed explanations of linear regression and decision trees, along with practical exercises." The system can respond quickly and effectively to such specific requests, meeting the learner's needs.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The user inputs the content or topic they want to learn via their device. This input is done using a keyboard or voice recognition. When the user inputs "I want to learn the basics of machine learning," this is recorded as a prompt on the device and sent to the server. The input is sent as text data.

[0478] Step 2:

[0479] The server analyzes the received prompt message using natural language processing software. This analysis extracts important keywords such as "machine learning" and "basics." Data processing involves tokenization of the text data and keyword extraction. As a result, the analyzed keywords are output.

[0480] Step 3:

[0481] The server crawls the internet and online education platforms based on the analyzed keywords. The crawling process collects relevant information and data. Keywords are required as input, and the collected information data is obtained as output. Web crawler software is used in this process.

[0482] Step 4:

[0483] The server filters the collected information using an AI algorithm. Here, data calculations are performed to evaluate relevance and usefulness, selecting the necessary information. The input is the collected information, and the output is filtered, effective learning content.

[0484] Step 5:

[0485] The server uses the selected information to generate optimal educational materials for the user. This data processing involves integrating various forms of information (text, videos, exercises, etc.). The input is filtered information, and the output is the completed educational material.

[0486] Step 6:

[0487] The server sends the generated educational materials back to the terminal. The terminal provides this to the user in an interactive format to support learning. The output is content that is visually displayed to the user, and the user uses this to progress in their learning.

[0488] Step 7:

[0489] The user's progress as they learn is recorded by their device. The device sends this information to a server, which then analyzes it to inform the next learning plan. The input is learning progress data, and the output is customized learning feedback and a plan.

[0490] (Application Example 1)

[0491] Next, we will explain Application Example 1. In the following explanation, 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."

[0492] In modern times, the amount of information has increased due to the development of information and communication technology, but it is difficult for learners to find the most suitable learning content from among it. Furthermore, in order to maximize learning effectiveness, it is necessary to clearly indicate the next learning step according to the learner's progress, but there are limited systems that can do this effectively. The objective of this invention is to solve these problems and provide an efficient and effective learning process.

[0493] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0494] In this invention, the server includes means for receiving learning objectives from the user, means for collecting information from information and communication networks and online education platforms, and means for sorting and evaluating the collected information. This makes it possible to generate optimal educational content based on the user's learning intentions and to suggest appropriate learning steps according to the user's learning progress.

[0495] "Means for receiving learning objectives from users" refers to a device or method that performs the process of clearly inputting the content or field that a user wants to learn into a server.

[0496] "Means of collecting information from information and communication networks and online education platforms" refers to technologies or mechanisms for obtaining content from diverse online learning resources and platforms via the internet.

[0497] "Means for sorting and evaluating collected information" refers to methods of analyzing various acquired data and selecting and evaluating content based on importance and relevance.

[0498] "Means for generating integrated educational content based on the user's learning intent" refers to technology that combines information according to the user's learning needs and creates customized educational materials to maximize learning efficiency.

[0499] "Means of providing generated educational content to users in an interactive format" refers to a method or device for presenting educational materials interactively in a way that is easy for users to understand.

[0500] "Means for recording a user's learning progress and reflecting it in the next learning session" refers to a process or system that automatically saves the progress of learning and uses the results to inform the next learning plan.

[0501] "A means of suggesting the next optimal learning plan according to the learning progress" refers to a technology or system that suggests the next learning items and methods to proceed with based on the user's current learning status.

[0502] This invention is a system that utilizes information and communication technology to provide learners with optimized educational content. The system is centered around a server, and the process begins when the user inputs the content they wish to learn.

[0503] The server first receives the learning objectives sent from the user's terminal. Using natural language processing technology, it analyzes the user's input regarding areas of interest and automatically collects necessary information. During this process, servers running on AWS or Google Cloud Platform crawl the internet and online education platforms.

[0504] The analyzed data is filtered using AI algorithms built with TensorFlow and PyTorch to generate highly relevant content for the user. This generated educational content is presented interactively on the user's smartphone or tablet, allowing them to progress through their learning.

[0505] Furthermore, learning progress is recorded in the cloud and reflected in the next learning session. Based on the user's progress, the server recommends the next most suitable learning step. In this way, it is possible to always maximize the user's learning effectiveness.

[0506] For example, if a user requests to "learn about data analysis," the system selects relevant lecture videos and practice problems and presents them to the user. The user then proceeds with their learning based on the provided content and can request to "also understand the basics of machine learning." The server responds to this request in real time and provides appropriate supplementary materials.

[0507] An example of a prompt using a generative AI model is: "Generate optimal learning content based on the user's desired learning theme. Specific theme: Fundamentals of Data Science." This allows users to acquire new knowledge more efficiently and effectively.

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The user enters their learning objectives via their device. This input information is then transferred to a language analysis process. When specific fields or keywords are entered, the information is sent to the server and the analysis begins.

[0511] Step 2:

[0512] The server analyzes the received learning objectives using natural language processing (NLP) techniques. This analysis extracts relevant keywords and important information, and uses this information to prepare for data collection. Specifically, it applies an NLP (Natural Language Processing) model to clarify the user's intent.

[0513] Step 3:

[0514] The server crawls information and communication networks and online education platforms to collect relevant information. It searches a wide range of data from the internet and temporarily stores the results. At this point, it establishes a connection with the education platform using an API and collects the necessary data.

[0515] Step 4:

[0516] The collected data is filtered by an AI algorithm on the server. The specific input is the data obtained in the previous step, and the output is important information that matches the user's learning objectives. Machine learning models using TensorFlow or PyTorch are responsible for evaluating and organizing the data.

[0517] Step 5:

[0518] The server integrates filtered data and generates user-optimized educational content. The generated content consists of various forms, including videos, documents, and quizzes. The output directly impacts the user's desired learning outcomes.

[0519] Step 6:

[0520] The device provides the user with generated educational content. The user can then interact with the provided content and progress through their learning. Here, an interactive user interface acts as a bridge between the user and the content.

[0521] Step 7:

[0522] The user's learning progress is recorded by the device and sent to the server. This progress data is used to inform the next learning plan and to evaluate the user's performance. New learning steps are also recommended based on the progress.

[0523] This entire process allows users to gain a more efficient and personalized learning experience.

[0524] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0525] This invention enhances the user experience by combining an emotion engine with a learning support system. When a user enters a learning request through a terminal, the system starts operating, and the server analyzes the request. Based on the analysis results, the server crawls relevant information from the internet and online learning platforms and collects the necessary data.

[0526] The collected data is filtered, evaluated, and integrated by AI algorithms on the server. This integrated learning content is optimized to meet the user's needs. For example, a learning curriculum is created that includes everything from basic information to advanced content on a specific learning topic.

[0527] A key feature of this invention is the incorporation of an emotion engine. The server uses camera and microphone input acquired from the user's terminal to analyze the user's emotions from their facial expressions and voice tone. Based on the results of the emotion analysis, the server adjusts the learning content in real time. For example, if the user is losing interest, more interactive content or game elements can be included.

[0528] As the user progresses through the learning process, the emotion engine continuously monitors the user's state and accumulates changes in their emotions as data. This data is analyzed by the server and used to optimize future learning plans. Based on the user's emotions, personalized curricula are proposed that are tailored not only to learning progress but also to learning motivation and satisfaction.

[0529] In this way, users can always receive a learning experience that is adapted to their emotional state. For example, when a user is tired, the content is simplified, and when their concentration is high, more challenging tasks are provided. This allows users to continue learning effectively and sustainably.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users use their devices to submit requests, entering the content they want to learn and the topics they are interested in. These requests include specific keywords and detailed subject preferences.

[0533] Step 2:

[0534] The server receives the user's request and analyzes the request content using natural language processing technology. This analysis automatically extracts relevant keywords.

[0535] Step 3:

[0536] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. During this process, it searches for necessary information from a variety of databases and educational content.

[0537] Step 4:

[0538] The server filters the collected information and selects it according to usefulness and evaluation criteria. This extracts high-quality content that aligns with the user's learning intentions.

[0539] Step 5:

[0540] The server integrates the selected information and generates customized learning content for the user. This process constructs the chosen content into a logical learning path, for example, designing a curriculum that progresses from foundational knowledge to more specialized content.

[0541] Step 6:

[0542] The device provides the user with generated learning content, while the emotion engine analyzes the user's emotional state in real time. The device's camera and microphone are used to capture the user's facial expressions and voice, and this data is sent to the server.

[0543] Step 7:

[0544] The server analyzes emotional data received from the device and dynamically adjusts the difficulty level and format of the learning content based on the results. For example, if the user is feeling stressed, the content will be changed to something more relaxing.

[0545] Step 8:

[0546] Users continue their activities, deepening their understanding and solving problems according to the tailored learning content. They can ask questions via their device as needed.

[0547] Step 9:

[0548] The device records the user's learning progress and emotional changes, and sends this data to a server. This data is used for planning the next learning session and providing feedback for improvement.

[0549] Step 10:

[0550] The server integrates all data and continuously optimizes the user's long-term learning plan, ensuring a consistently effective learning experience.

[0551] (Example 2)

[0552] Next, we will describe Example 2. 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."

[0553] In modern online education, typical educational systems provide uniform educational content without considering the emotional state of learners, making it difficult to sustain user motivation. This results in problems such as decreased learning efficiency and satisfaction. Furthermore, because the system does not reflect learning progress or results, it is difficult to provide a curriculum optimized for individual users.

[0554] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0555] In this invention, the server includes means for receiving information processing requests from users, means for collecting data from computer communication networks and online education infrastructure, and means for selecting and evaluating the collected data. This enables the provision of optimized educational content based on the user's individual learning needs and real-time content adjustment according to the user's emotional state.

[0556] An "information processing request" is a request regarding specific learning content or themes that a user sends to the system in order to advance their learning.

[0557] A "computer communication network" refers to an online information network where data is transmitted electronically, and includes various networks such as the Internet.

[0558] An "online education infrastructure" is a system or platform for providing educational content and services in a digital environment.

[0559] "Means of data collection" refers to the technical processes or devices used to acquire necessary information from external sources.

[0560] "Means for selecting and evaluating data" refers to a technical process or device that classifies collected information according to its importance and relevance, and determines its quality and usefulness.

[0561] "Means for generating educational materials" refers to a technical process or apparatus for creating content necessary for learning based on selected and evaluated data.

[0562] "Means for adjusting educational materials" refers to a technical process or device that modifies or optimizes the content provided based on the user's learning progress and emotional state.

[0563] "Means of collecting opinions" refers to a technical process or device for obtaining feedback from users.

[0564] This invention is an information processing system specifically designed for learning support, which analyzes the learner's emotional state in real time and provides an individually optimized learning experience.

[0565] The user inputs an information processing request from their device, indicating the content or topic they wish to learn about. The device then sends this request to the server. For example, suppose the user inputs, "I want to learn about important historical revolutions." The server receives this request via the computer network and uses an online education platform to collect relevant data. This process utilizes web crawling libraries and APIs.

[0566] Next, an AI algorithm on the server sorts the collected data and evaluates its relevance and reliability. Specific technologies used include data analysis libraries. Based on the sorted and evaluated data, the server generates educational materials that align with the user's learning style. These generated educational materials are provided to the user via a terminal, facilitating interactive learning.

[0567] Furthermore, a key feature of this invention is that the server can analyze the user's emotional state through the camera and microphone of the terminal. For example, it can use OpenCV or a speech analysis library to evaluate the user's level of interest from their facial expressions and voice tone. Based on the analysis results, it then adjusts the educational materials in real time. If it is determined that the user is bored, adjustments such as adding a game-style quiz are made.

[0568] This system can record the user's learning progress and incorporate feedback into future learning sessions. When a user provides feedback on their learning from their device, the server can store this information and use it to improve the system.

[0569] As a concrete example of a prompt, a request such as "Please provide detailed information and related multimedia content about important historical revolutions" can be used. This allows users to effectively acquire knowledge and maintain their interest as they progress through the learning process.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user uses a terminal to input the topic or content they want to learn about. This input is sent to the server as an information processing request. Specifically, the user might input a prompt such as, "I want to learn about important historical revolutions." This prompt becomes the input data sent to the server.

[0573] Step 2:

[0574] The server analyzes the received information processing request. Using natural language processing techniques, it extracts relevant keywords and themes. The extracted keywords are used as foundational information for data collection. Specifically, the server identifies the keywords "history" and "revolution" and prepares for the data collection process.

[0575] Step 3:

[0576] The server collects data using computer communication networks and online education infrastructure. It utilizes web crawling technology to retrieve relevant materials from the internet. In this process, relevant literature and multimedia materials are stored on the server. The input is extracted keywords, and the output is the collected dataset.

[0577] Step 4:

[0578] The server sorts and evaluates the collected data. It uses data analysis libraries to determine the reliability and relevance of the information. Specifically, it removes inappropriate or duplicate data, leaving only highly reliable information. The input is the collected dataset, and the output is the filtered data.

[0579] Step 5:

[0580] The server generates educational materials using the selected data. A generation AI model is used to integrate the content into a format suitable for learners. Specifically, it constructs an educational curriculum that encompasses everything from basic knowledge to applied concepts. The input is filtered data, and the output is educational materials.

[0581] Step 6:

[0582] The server analyzes the user's emotional state through the camera and microphone on the terminal. Using AI technology, it infers the user's interest and concentration level from their facial expressions and voice tone. Based on this analysis, it adjusts educational materials in real time. The input is data of the user's facial expressions and voice, and the output is the adjusted educational materials.

[0583] Step 7:

[0584] As the user progresses through the learning process, the server records their progress and saves it in a database as feedback for the next session. User feedback is also collected and used to improve the system. Inputs are performance data during learning and user feedback, while outputs are learning history and improvement data.

[0585] (Application Example 2)

[0586] Next, we will explain application example 2. In the following explanation, 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."

[0587] Traditional learning systems lacked the ability to dynamically adjust the learning experience based on user emotions, making it difficult to effectively improve user motivation and satisfaction. Furthermore, they lacked real-time content recommendation features that responded to user emotions, thus failing to provide an appropriate learning experience that met user expectations.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes a device for receiving user requests, a device for collecting information from networks and digital education platforms, and a device for analyzing the user's emotions and dynamically adjusting the information according to those emotions. This makes it possible to provide an optimal learning experience based on the user's emotions.

[0590] A "device for receiving user requests" is a device that receives requests from users regarding learning or content.

[0591] A "device for collecting information from networks and digital education platforms" is a device for gathering necessary information from the internet and online learning platforms.

[0592] A "device for selecting and evaluating accumulated information" is a device that filters collected information as needed and determines its value.

[0593] A "device that generates integrated educational information based on the user's learning objectives" is a device for creating learning content that matches the user's wishes and goals.

[0594] A "device that provides generated information to the user in an interactive format" is a device that provides created content to the user in an interactive manner.

[0595] A "device that records user progress and uses it for future learning" is a device that records the progress of learning and uses that information to improve future learning opportunities.

[0596] A "device that analyzes user emotions and dynamically adjusts information according to those emotions" is a device that analyzes the user's facial expressions and voice and changes the learning content in real time based on their emotions at that moment.

[0597] This invention is a learning support system that dynamically provides a learning experience in response to the user's emotions. The server receives requests from users via a network. Users can use smart devices or computers to input requests regarding learning and content through an interface.

[0598] When a request is received, the server collects relevant information from the network and digital education platform. Specifically, it performs web crawling using Python libraries to extract necessary information from a vast dataset. This information is then filtered and evaluated using machine learning algorithms, based on the user's learning needs. Based on the collected information, the server generates content tailored to the user's learning objectives. It uses a generative AI model to create prompts that respond to user requests and customize the content accordingly. For example, it can use prompts such as, "What content would you recommend when the user's current mood is 'happy'?"

[0599] Furthermore, the server monitors the user's emotional state in real time and dynamically adjusts the content it provides. For example, if a user becomes bored with a video they are watching, the server incorporates highly entertaining elements into the video streaming app based on the emotional analysis data. OpenCV and natural language processing toolkits are used to analyze the user's facial expressions and tone of voice. In this way, users can always enjoy content optimized for their current emotions, improving learning efficiency and increasing satisfaction.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The terminal receives a request from the user. When the user enters a learning request through the interface, that information is passed to the terminal. This input includes specific learning topics and interests. The terminal sends this request to the server as a data packet.

[0603] Step 2:

[0604] The server collects information from networks and digital education platforms. Here, the server performs web crawling using received user requests. It leverages Python libraries to explore relevant resources and extract the necessary information. The input is the user's request, and the output is a list of relevant learning resources.

[0605] Step 3:

[0606] The server sorts and evaluates the collected information. It uses machine learning algorithms to select the information best suited to the user's requirements from the numerous collected learning resources. The input is the list of learning resources obtained in step 2, and the output is a subset of the most relevant information.

[0607] Step 4:

[0608] The server generates integrated educational information based on the user's learning objectives. This utilizes a generative AI model along with personalized prompts. Specifically, it might create a prompt such as, "Please provide content that will help the user relax." The input is selected learning information, and the output is personalized educational content.

[0609] Step 5:

[0610] The server provides generated information to the terminal in an interactive format. The user's terminal displays this information, allowing the user to interact with the content. The input is integrated educational information, and the output is the user's learning experience itself.

[0611] Step 6:

[0612] The system dynamically adjusts content based on the user's emotions. The server analyzes facial and voice data acquired from the device to determine the user's emotional state in real time. Content is added or modified as needed to maintain the user's interest. The input is emotion analysis data, and the output is the adjusted learning content.

[0613] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] As shown in Figure 7, the 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.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0624] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0626] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0627] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] The system of this invention primarily utilizes a server and a terminal to provide learning support to users. First, the system starts operating when the user requests what they want to learn. The user inputs their interests and the fields they want to learn into the terminal. For example, they might input, "I want to learn the basics of machine learning."

[0631] In response, the server analyzes the user's intent and extracts relevant keywords. Based on these keywords, the server crawls the internet and online learning platforms to collect necessary information. In this process, data is obtained from a wide range of sources to find content that matches the user's learning needs.

[0632] The collected information is filtered by an AI algorithm on the server to select only the most important and useful information. Then, based on this information, user-optimized learning content is integrated. For example, this might include materials explaining the fundamental theories of machine learning, video lectures, and practice problems.

[0633] The generated learning content is provided to the device and made available to the user. The user can begin learning based on this provided content and can ask questions or make requests to the server as needed. The server responds in real time, providing explanations and supplementary information to aid the user's understanding.

[0634] Learning progress and specific results are automatically recorded through the device. This data is sent to the server and used to create personalized feedback and future learning plans for each user. User feedback is also collected and used to improve the entire system. In this way, the system can continue to provide users with a consistently effective learning experience.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] Users enter learning requests using their devices. This information includes the topics they want to learn and their specific goals.

[0638] Step 2:

[0639] The server receives the user's request and analyzes the input using natural language processing technology. This allows it to extract relevant keywords.

[0640] Step 3:

[0641] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. This process gathers data from diverse sources.

[0642] Step 4:

[0643] The server filters the collected information using AI algorithms and evaluates it based on reliability and usefulness. The selected information is then ranked according to its importance.

[0644] Step 5:

[0645] The server integrates the evaluated information and generates learning content optimized for user requests. This includes various formats such as text, video, and audio.

[0646] Step 6:

[0647] The generated learning content is provided to the device, and the user progresses through the learning process by viewing it. During this process, the user can input questions or points of confusion into the server.

[0648] Step 7:

[0649] The server uses AI to respond to user questions in real time. It may provide additional explanations or supplementary information as needed.

[0650] Step 8:

[0651] The device automatically records the user's learning progress and sends that data to the server. This allows the user's performance and learning status to be reflected.

[0652] Step 9:

[0653] The server analyzes the acquired learning data and user feedback to make suggestions and customizations for the next learning session. This continuously improves the learning experience.

[0654] (Example 1)

[0655] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] In modern education systems, it is difficult to quickly provide useful learning materials that meet the individual needs of learners. Finding appropriate materials from the vast amount of online information is time-consuming, and there are challenges in adequately developing mechanisms for utilizing learning progress and feedback in education.

[0657] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0658] In this invention, the server includes means for receiving learning requests from users, means for collecting information from networks and online education platforms, and means for classifying and evaluating the collected information. This makes it possible to comprehensively provide a learning experience tailored to the individual needs of users and support effective learning.

[0659] "Means for receiving learning requests from users" refers to a function that provides an interface for learners to communicate to the system the content and themes they wish to learn.

[0660] "Means of gathering information from networks and online education platforms" refers to the function of obtaining necessary data from information sources and learning resources accessible via the internet.

[0661] "Means for classifying and evaluating collected information" refers to algorithms and processes for organizing and selecting acquired information based on its effectiveness, relevance, and importance.

[0662] "Means for creating integrated educational materials based on the user's learning objectives" refers to a function for generating and presenting learning content optimized according to the learner's needs.

[0663] "Means of providing created materials to users interactively" refers to functions that interactively present generated learning content to learners and respond to feedback and questions.

[0664] "A means of recording users' learning progress and reflecting it in future lessons" refers to a function that records the content and achievements of learners and uses this information to inform future learning plans and content.

[0665] "Means for collecting user feedback and using it to improve the system" refers to a function that collects feedback and evaluations from learners and contributes to the improvement and optimization of the system.

[0666] The present invention aims to provide learners with educational materials tailored to their needs by utilizing networks and terminals. The system begins with the user inputting a learning request via a terminal. For example, a possible input might be, "I want to learn the basics of machine learning." This request is then sent from the terminal to the server.

[0667] The server uses natural language processing software (e.g., a common language processing library) to analyze user requests and extract key keywords. The server then collects relevant information from internet sources and online education platforms. Common web crawler software is used for this information collection.

[0668] The collected information is analyzed and filtered using AI algorithms (e.g., generative AI models) on the server. Here, the information is classified based on its usefulness and relevance. The selected information is then integrated into educational materials optimized for learning purposes. For example, it might be provided as a combination of documents explaining machine learning theory, video lectures, and practice problems.

[0669] The generated educational materials are sent to the device and provided interactively to the user. The user can progress through their learning based on this content and can obtain supplementary information from the server in real time by entering additional questions or requests.

[0670] Learning progress is automatically recorded on the device and sent to the server. The server uses this data to customize the next learning plan and provide feedback. It also collects user feedback and uses it to improve the system.

[0671] For example, a user might enter a prompt such as, "I want to learn the basics of machine learning. Please provide detailed explanations of linear regression and decision trees, along with practical exercises." The system can respond quickly and effectively to such specific requests, meeting the learner's needs.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] The user inputs the content or topic they want to learn via their device. This input is done using a keyboard or voice recognition. When the user inputs "I want to learn the basics of machine learning," this is recorded as a prompt on the device and sent to the server. The input is sent as text data.

[0675] Step 2:

[0676] The server analyzes the received prompt message using natural language processing software. This analysis extracts important keywords such as "machine learning" and "basics." Data processing involves tokenization of the text data and keyword extraction. As a result, the analyzed keywords are output.

[0677] Step 3:

[0678] The server crawls the internet and online education platforms based on the analyzed keywords. The crawling process collects relevant information and data. Keywords are required as input, and the collected information data is obtained as output. Web crawler software is used in this process.

[0679] Step 4:

[0680] The server filters the collected information using an AI algorithm. Here, data calculations are performed to evaluate relevance and usefulness, selecting the necessary information. The input is the collected information, and the output is filtered, effective learning content.

[0681] Step 5:

[0682] The server uses the selected information to generate optimal educational materials for the user. This data processing involves integrating various forms of information (text, videos, exercises, etc.). The input is filtered information, and the output is the completed educational material.

[0683] Step 6:

[0684] The server sends the generated educational materials back to the terminal. The terminal provides this to the user in an interactive format to support learning. The output is content that is visually displayed to the user, and the user uses this to progress in their learning.

[0685] Step 7:

[0686] The user's progress as they learn is recorded by their device. The device sends this information to a server, which then analyzes it to inform the next learning plan. The input is learning progress data, and the output is customized learning feedback and a plan.

[0687] (Application Example 1)

[0688] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0689] In modern times, the amount of information has increased due to the development of information and communication technology, but it is difficult for learners to find the most suitable learning content from among it. Furthermore, in order to maximize learning effectiveness, it is necessary to clearly indicate the next learning step according to the learner's progress, but there are limited systems that can do this effectively. The objective of this invention is to solve these problems and provide an efficient and effective learning process.

[0690] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0691] In this invention, the server includes means for receiving learning objectives from the user, means for collecting information from information and communication networks and online education platforms, and means for sorting and evaluating the collected information. This makes it possible to generate optimal educational content based on the user's learning intentions and to suggest appropriate learning steps according to the user's learning progress.

[0692] "Means for receiving learning objectives from users" refers to a device or method that performs the process of clearly inputting the content or field that a user wants to learn into a server.

[0693] "Means of collecting information from information and communication networks and online education platforms" refers to technologies or mechanisms for obtaining content from diverse online learning resources and platforms via the internet.

[0694] "Means for sorting and evaluating collected information" refers to methods of analyzing various acquired data and selecting and evaluating content based on importance and relevance.

[0695] "Means for generating integrated educational content based on the user's learning intent" refers to technology that combines information according to the user's learning needs and creates customized educational materials to maximize learning efficiency.

[0696] "Means of providing generated educational content to users in an interactive format" refers to a method or device for presenting educational materials interactively in a way that is easy for users to understand.

[0697] "Means for recording a user's learning progress and reflecting it in the next learning session" refers to a process or system that automatically saves the progress of learning and uses the results to inform the next learning plan.

[0698] "A means of suggesting the next optimal learning plan according to the learning progress" refers to a technology or system that suggests the next learning items and methods to proceed with based on the user's current learning status.

[0699] This invention is a system that utilizes information and communication technology to provide learners with optimized educational content. The system is centered around a server, and the process begins when the user inputs the content they wish to learn.

[0700] The server first receives the learning objectives sent from the user's terminal. Using natural language processing technology, it analyzes the user's input regarding areas of interest and automatically collects necessary information. During this process, servers running on AWS or Google Cloud Platform crawl the internet and online education platforms.

[0701] The analyzed data is filtered using AI algorithms built with TensorFlow and PyTorch to generate highly relevant content for the user. This generated educational content is presented interactively on the user's smartphone or tablet, allowing them to progress through their learning.

[0702] Furthermore, learning progress is recorded in the cloud and reflected in the next learning session. Based on the user's progress, the server recommends the next most suitable learning step. In this way, it is possible to always maximize the user's learning effectiveness.

[0703] For example, if a user requests to "learn about data analysis," the system selects relevant lecture videos and practice problems and presents them to the user. The user then proceeds with their learning based on the provided content and can request to "also understand the basics of machine learning." The server responds to this request in real time and provides appropriate supplementary materials.

[0704] An example of a prompt using a generative AI model is: "Generate optimal learning content based on the user's desired learning theme. Specific theme: Fundamentals of Data Science." This allows users to acquire new knowledge more efficiently and effectively.

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The user enters their learning objectives via their device. This input information is then transferred to a language analysis process. When specific fields or keywords are entered, the information is sent to the server and the analysis begins.

[0708] Step 2:

[0709] The server analyzes the received learning objectives using natural language processing (NLP) techniques. This analysis extracts relevant keywords and important information, and uses this information to prepare for data collection. Specifically, it applies an NLP (Natural Language Processing) model to clarify the user's intent.

[0710] Step 3:

[0711] The server crawls information and communication networks and online education platforms to collect relevant information. It searches a wide range of data from the internet and temporarily stores the results. At this point, it establishes a connection with the education platform using an API and collects the necessary data.

[0712] Step 4:

[0713] The collected data is filtered by an AI algorithm on the server. The specific input is the data obtained in the previous step, and the output is important information that matches the user's learning objectives. Machine learning models using TensorFlow or PyTorch are responsible for evaluating and organizing the data.

[0714] Step 5:

[0715] The server integrates filtered data and generates user-optimized educational content. The generated content consists of various forms, including videos, documents, and quizzes. The output directly impacts the user's desired learning outcomes.

[0716] Step 6:

[0717] The device provides the user with generated educational content. The user can then interact with the provided content and progress through their learning. Here, an interactive user interface acts as a bridge between the user and the content.

[0718] Step 7:

[0719] The user's learning progress is recorded by the device and sent to the server. This progress data is used to inform the next learning plan and to evaluate the user's performance. New learning steps are also recommended based on the progress.

[0720] This entire process allows users to gain a more efficient and personalized learning experience.

[0721] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0722] This invention enhances the user experience by combining an emotion engine with a learning support system. When a user enters a learning request through a terminal, the system starts operating, and the server analyzes the request. Based on the analysis results, the server crawls relevant information from the internet and online learning platforms and collects the necessary data.

[0723] The collected data is filtered, evaluated, and integrated by AI algorithms on the server. This integrated learning content is optimized to meet the user's needs. For example, a learning curriculum is created that includes everything from basic information to advanced content on a specific learning topic.

[0724] A key feature of this invention is the incorporation of an emotion engine. The server uses camera and microphone input acquired from the user's terminal to analyze the user's emotions from their facial expressions and voice tone. Based on the results of the emotion analysis, the server adjusts the learning content in real time. For example, if the user is losing interest, more interactive content or game elements can be included.

[0725] As the user progresses through the learning process, the emotion engine continuously monitors the user's state and accumulates changes in their emotions as data. This data is analyzed by the server and used to optimize future learning plans. Based on the user's emotions, personalized curricula are proposed that are tailored not only to learning progress but also to learning motivation and satisfaction.

[0726] In this way, users can always receive a learning experience that is adapted to their emotional state. For example, when a user is tired, the content is simplified, and when their concentration is high, more challenging tasks are provided. This allows users to continue learning effectively and sustainably.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] Users use their devices to submit requests, entering the content they want to learn and the topics they are interested in. These requests include specific keywords and detailed subject preferences.

[0730] Step 2:

[0731] The server receives the user's request and analyzes the request content using natural language processing technology. This analysis automatically extracts relevant keywords.

[0732] Step 3:

[0733] The server uses extracted keywords to crawl the internet and online learning platforms to collect information. During this process, it searches for necessary information from a variety of databases and educational content.

[0734] Step 4:

[0735] The server filters the collected information and selects it according to usefulness and evaluation criteria. This extracts high-quality content that aligns with the user's learning intentions.

[0736] Step 5:

[0737] The server integrates the selected information and generates customized learning content for the user. This process constructs the chosen content into a logical learning path, for example, designing a curriculum that progresses from foundational knowledge to more specialized content.

[0738] Step 6:

[0739] The device provides the user with generated learning content, while the emotion engine analyzes the user's emotional state in real time. The device's camera and microphone are used to capture the user's facial expressions and voice, and this data is sent to the server.

[0740] Step 7:

[0741] The server analyzes emotional data received from the device and dynamically adjusts the difficulty level and format of the learning content based on the results. For example, if the user is feeling stressed, the content will be changed to something more relaxing.

[0742] Step 8:

[0743] Users continue their activities, deepening their understanding and solving problems according to the tailored learning content. They can ask questions via their device as needed.

[0744] Step 9:

[0745] The device records the user's learning progress and emotional changes, and sends this data to a server. This data is used for planning the next learning session and providing feedback for improvement.

[0746] Step 10:

[0747] The server integrates all data and continuously optimizes the user's long-term learning plan, ensuring a consistently effective learning experience.

[0748] (Example 2)

[0749] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] In modern online education, typical educational systems provide uniform educational content without considering the emotional state of learners, making it difficult to sustain user motivation. This results in problems such as decreased learning efficiency and satisfaction. Furthermore, because the system does not reflect learning progress or results, it is difficult to provide a curriculum optimized for individual users.

[0751] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0752] In this invention, the server includes means for receiving information processing requests from users, means for collecting data from computer communication networks and online education infrastructure, and means for selecting and evaluating the collected data. This enables the provision of optimized educational content based on the user's individual learning needs and real-time content adjustment according to the user's emotional state.

[0753] An "information processing request" is a request regarding specific learning content or themes that a user sends to the system in order to advance their learning.

[0754] A "computer communication network" refers to an online information network where data is transmitted electronically, and includes various networks such as the Internet.

[0755] An "online education infrastructure" is a system or platform for providing educational content and services in a digital environment.

[0756] "Means of data collection" refers to the technical processes or devices used to acquire necessary information from external sources.

[0757] "Means for selecting and evaluating data" refers to a technical process or device that classifies collected information according to its importance and relevance, and determines its quality and usefulness.

[0758] "Means for generating educational materials" refers to a technical process or apparatus for creating content necessary for learning based on selected and evaluated data.

[0759] "Means for adjusting educational materials" refers to a technical process or device that modifies or optimizes the content provided based on the user's learning progress and emotional state.

[0760] "Means of collecting opinions" refers to a technical process or device for obtaining feedback from users.

[0761] This invention is an information processing system specifically designed for learning support, which analyzes the learner's emotional state in real time and provides an individually optimized learning experience.

[0762] The user inputs an information processing request from their device, indicating the content or topic they wish to learn about. The device then sends this request to the server. For example, suppose the user inputs, "I want to learn about important historical revolutions." The server receives this request via the computer network and uses an online education platform to collect relevant data. This process utilizes web crawling libraries and APIs.

[0763] Next, an AI algorithm on the server sorts the collected data and evaluates its relevance and reliability. Specific technologies used include data analysis libraries. Based on the sorted and evaluated data, the server generates educational materials that align with the user's learning style. These generated educational materials are provided to the user via a terminal, facilitating interactive learning.

[0764] Furthermore, a key feature of this invention is that the server can analyze the user's emotional state through the camera and microphone of the terminal. For example, it can use OpenCV or a speech analysis library to evaluate the user's level of interest from their facial expressions and voice tone. Based on the analysis results, it then adjusts the educational materials in real time. If it is determined that the user is bored, adjustments such as adding a game-style quiz are made.

[0765] This system can record the user's learning progress and incorporate feedback into future learning sessions. When a user provides feedback on their learning from their device, the server can store this information and use it to improve the system.

[0766] As a concrete example of a prompt, a request such as "Please provide detailed information and related multimedia content about important historical revolutions" can be used. This allows users to effectively acquire knowledge and maintain their interest as they progress through the learning process.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] The user uses a terminal to input the topic or content they want to learn about. This input is sent to the server as an information processing request. Specifically, the user might input a prompt such as, "I want to learn about important historical revolutions." This prompt becomes the input data sent to the server.

[0770] Step 2:

[0771] The server analyzes the received information processing request. Using natural language processing techniques, it extracts relevant keywords and themes. The extracted keywords are used as foundational information for data collection. Specifically, the server identifies the keywords "history" and "revolution" and prepares for the data collection process.

[0772] Step 3:

[0773] The server collects data using computer communication networks and online education infrastructure. It utilizes web crawling technology to retrieve relevant materials from the internet. In this process, relevant literature and multimedia materials are stored on the server. The input is extracted keywords, and the output is the collected dataset.

[0774] Step 4:

[0775] The server sorts and evaluates the collected data. It uses data analysis libraries to determine the reliability and relevance of the information. Specifically, it removes inappropriate or duplicate data, leaving only highly reliable information. The input is the collected dataset, and the output is the filtered data.

[0776] Step 5:

[0777] The server generates educational materials using the selected data. A generation AI model is used to integrate the content into a format suitable for learners. Specifically, it constructs an educational curriculum that encompasses everything from basic knowledge to applied concepts. The input is filtered data, and the output is educational materials.

[0778] Step 6:

[0779] The server analyzes the user's emotional state through the camera and microphone on the terminal. Using AI technology, it infers the user's interest and concentration level from their facial expressions and voice tone. Based on this analysis, it adjusts educational materials in real time. The input is data of the user's facial expressions and voice, and the output is the adjusted educational materials.

[0780] Step 7:

[0781] As the user progresses through the learning process, the server records their progress and saves it in a database as feedback for the next session. User feedback is also collected and used to improve the system. Inputs are performance data during learning and user feedback, while outputs are learning history and improvement data.

[0782] (Application Example 2)

[0783] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0784] Traditional learning systems lacked the ability to dynamically adjust the learning experience based on user emotions, making it difficult to effectively improve user motivation and satisfaction. Furthermore, they lacked real-time content recommendation features that responded to user emotions, thus failing to provide an appropriate learning experience that met user expectations.

[0785] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0786] In this invention, the server includes a device for receiving user requests, a device for collecting information from networks and digital education platforms, and a device for analyzing the user's emotions and dynamically adjusting the information according to those emotions. This makes it possible to provide an optimal learning experience based on the user's emotions.

[0787] A "device for receiving user requests" is a device that receives requests from users regarding learning or content.

[0788] A "device for collecting information from networks and digital education platforms" is a device for gathering necessary information from the internet and online learning platforms.

[0789] A "device for selecting and evaluating accumulated information" is a device that filters collected information as needed and determines its value.

[0790] A "device that generates integrated educational information based on the user's learning objectives" is a device for creating learning content that matches the user's wishes and goals.

[0791] A "device that provides generated information to the user in an interactive format" is a device that provides created content to the user in an interactive manner.

[0792] A "device that records user progress and uses it for future learning" is a device that records the progress of learning and uses that information to improve future learning opportunities.

[0793] A "device that analyzes user emotions and dynamically adjusts information according to those emotions" is a device that analyzes the user's facial expressions and voice and changes the learning content in real time based on their emotions at that moment.

[0794] This invention is a learning support system that dynamically provides a learning experience in response to the user's emotions. The server receives requests from users via a network. Users can use smart devices or computers to input requests regarding learning and content through an interface.

[0795] When a request is received, the server collects relevant information from the network and digital education platform. Specifically, it performs web crawling using Python libraries to extract necessary information from a vast dataset. This information is then filtered and evaluated using machine learning algorithms, based on the user's learning needs. Based on the collected information, the server generates content tailored to the user's learning objectives. It uses a generative AI model to create prompts that respond to user requests and customize the content accordingly. For example, it can use prompts such as, "What content would you recommend when the user's current mood is 'happy'?"

[0796] Furthermore, the server monitors the user's emotional state in real time and dynamically adjusts the content it provides. For example, if a user becomes bored with a video they are watching, the server incorporates highly entertaining elements into the video streaming app based on the emotional analysis data. OpenCV and natural language processing toolkits are used to analyze the user's facial expressions and tone of voice. In this way, users can always enjoy content optimized for their current emotions, improving learning efficiency and increasing satisfaction.

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The terminal receives a request from the user. When the user enters a learning request through the interface, that information is passed to the terminal. This input includes specific learning topics and interests. The terminal sends this request to the server as a data packet.

[0800] Step 2:

[0801] The server collects information from networks and digital education platforms. Here, the server performs web crawling using received user requests. It leverages Python libraries to explore relevant resources and extract the necessary information. The input is the user's request, and the output is a list of relevant learning resources.

[0802] Step 3:

[0803] The server sorts and evaluates the collected information. It uses machine learning algorithms to select the information best suited to the user's requirements from the numerous collected learning resources. The input is the list of learning resources obtained in step 2, and the output is a subset of the most relevant information.

[0804] Step 4:

[0805] The server generates integrated educational information based on the user's learning objectives. This utilizes a generative AI model along with personalized prompts. Specifically, it might create a prompt such as, "Please provide content that will help the user relax." The input is selected learning information, and the output is personalized educational content.

[0806] Step 5:

[0807] The server provides generated information to the terminal in an interactive format. The user's terminal displays this information, allowing the user to interact with the content. The input is integrated educational information, and the output is the user's learning experience itself.

[0808] Step 6:

[0809] The system dynamically adjusts content based on the user's emotions. The server analyzes facial and voice data acquired from the device to determine the user's emotional state in real time. Content is added or modified as needed to maintain the user's interest. The input is emotion analysis data, and the output is the adjusted learning content.

[0810] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0811] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0812] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0813] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0814] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0815] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0816] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0817] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0818] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0819] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0820] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0821] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0822] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0823] 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.

[0824] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0825] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0826] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0827] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0828] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0829] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0830] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] A means of receiving learning requests from users,

[0834] Means for crawling information from the internet and online learning platforms,

[0835] A means of filtering and evaluating the collected information,

[0836] A means for generating integrated learning content based on the user's learning intent,

[0837] A means of providing the generated content to the user in an interactive format,

[0838] A system that includes a means to record the user's learning progress and reflect it in the next learning session.

[0839] (Claim 2)

[0840] The system according to claim 1, comprising means for providing explanations and supplementary information in real time based on the generated learning content.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising means for collecting user feedback and using it to improve the system.

[0843] "Example 1"

[0844] (Claim 1)

[0845] A means of receiving learning requests from users,

[0846] Means for collecting information from networks and online education platforms,

[0847] A means of classifying and evaluating the collected information,

[0848] A means of creating integrated educational materials based on the user's learning objectives,

[0849] A means of providing the created materials to users in an interactive manner,

[0850] A means to record the user's learning progress and reflect it in the next lesson,

[0851] A means of providing explanations and supplementary information as needed in response to additional requests from users,

[0852] A system that includes means for recording interactions with users and conducting analyses that will be useful for future learning.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for providing explanations and supplementary information as needed based on the educational materials created.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for collecting user feedback and using it to improve the system.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means of receiving learning objectives from users,

[0860] Means for collecting information from information and communication networks and online education platforms,

[0861] A means of sorting and evaluating the collected information,

[0862] A means for generating integrated educational content based on the user's learning intent,

[0863] A means of providing the generated educational content to the user in an interactive format,

[0864] A means to record the user's learning progress and reflect it in the next learning session,

[0865] A system that includes means for suggesting the next optimal learning plan according to the learning progress.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for immediately providing explanations and supplementary information based on the generated educational content.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising means for collecting user feedback and using it to improve the system.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means for receiving information processing requests from users,

[0873] Means for collecting data from computer communication networks and online education infrastructure,

[0874] A means of selecting and evaluating the collected data,

[0875] A means for generating integrated educational materials based on the user's learning objectives,

[0876] A means of providing the generated materials to the user in an interactive format,

[0877] A means of analyzing the user's emotional state,

[0878] A means of adjusting educational materials based on the analysis results,

[0879] A system that includes a means to record the user's learning progress and reflect it in the next learning session.

[0880] (Claim 2)

[0881] The system according to claim 1, comprising means for providing explanations and additional information in real time based on generated educational materials.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising means for collecting user feedback and using it to improve the system.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] A device that receives requests from users,

[0887] A device for collecting information from networks and digital education platforms,

[0888] A device for sorting and evaluating the collected information,

[0889] A device that generates integrated educational information based on the user's learning objectives,

[0890] A device that provides generated information to the user in an interactive format,

[0891] A device that records the user's progress and uses it for the next learning session,

[0892] A system that includes a device that analyzes the user's emotions and dynamically adjusts information according to those emotions.

[0893] (Claim 2)

[0894] The system according to claim 1, comprising a device that provides explanations and additional information in real time based on the generated educational information.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising a device for collecting user feedback and using it to improve the system. [Explanation of symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving learning requests from users, Means for crawling information from the internet and online learning platforms, A means of filtering and evaluating the collected information, A means for generating integrated learning content based on the user's learning intent, A means of providing the generated content to the user in an interactive format, A system that includes a means to record the user's learning progress and reflect it in the next learning session.

2. The system according to claim 1, comprising means for providing explanations and supplementary information in real time based on the generated learning content.

3. The system according to claim 1, further comprising means for collecting user feedback and using it to improve the system.

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