System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting information from worldwide word-of-mouth information and examination experiences; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing characteristics of individual learners; means for recommending teaching materials and reference books suitable for each learner based on the analysis result; means for generating an examination countermeasure schedule according to the progress of the learner; and means for providing an individual class and a gathering class online to provide a place where learners can interact with each other.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The environment surrounding junior high school entrance exams is becoming increasingly competitive every year. However, the cost of cram schools is high, placing a financial burden on many families. On the other hand, while there is a large amount of high-quality lesson content and useful exam information on the Internet, it is difficult to effectively utilize this information because it is not consolidated or organized. The objective of this invention is to improve this situation and reduce the financial burden on families by providing an efficient learning environment without the expensive cram school fees. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system including the following means.

[0006] 1. We will provide a means of collecting information from word-of-mouth information and test-taking experiences across the country. This will allow us to efficiently gather useful information for each test-taker.

[0007] 2. Establish a means to analyze collected information and aggregate useful information, thereby providing only reliable information.

[0008] 3. Provide a means for inputting and analyzing the characteristics of individual learners, and based on that, recommend teaching materials and reference books that are appropriate for each learner.

[0009] 4. Provide a means to generate exam preparation schedules based on the learner's progress, thereby supporting planned and efficient learning.

[0010] 5. The system will provide a means to automatically answer questions from learners using AI, allowing them to instantly resolve their doubts.

[0011] 6. We will provide individual lessons and training camps online, and provide a forum for students to interact with each other, thereby increasing motivation to learn and providing an environment where students can exchange information.

[0012] By combining these measures, we can achieve effective exam preparation while reducing the burden on families.

[0013] "Nationwide word-of-mouth information" refers to information collected nationwide, including reputations and evaluations by third parties about schools, cram schools, exams, etc.

[0014] "Exam Experiences" are documents in the form of blogs or articles in which individuals record their experiences, study methods, results, etc. regarding junior high school entrance exams.

[0015] "Collection means" refers to the devices and software processes that automatically retrieve the required information from websites and databases on the Internet.

[0016] "Means of analysis" refers to the process of devices or software that use natural language processing and machine learning to understand and analyze collected information, and then classify and evaluate its content.

[0017] "Means of aggregating useful information" refers to the process of using equipment or software to select particularly valuable information from the analyzed information, and list and store it in an easy-to-use format.

[0018] "Learner characteristics" refer to the individual characteristics of each learner, such as their learning method and style, and their strengths and weaknesses in subjects.

[0019] "Recommendation means" refers to the process of devices or software that select the most appropriate teaching materials and reference books based on the analysis results and the characteristics of the learner, and provide them to the learner.

[0020] An "exam preparation schedule" is a plan for efficiently preparing by systematically arranging study content and study volume according to the time until the exam date.

[0021] "Means for automatically answering questions" refers to devices or software processes that use AI to instantly generate and provide answers to questions entered by learners.

[0022] "Online individual and group instruction delivery means" means devices and software processes that allow learners to receive instruction individually or in groups over the Internet.

[0023] A "place where learners can interact" is an online or offline space where learners with the same goals can encourage and learn from each other through the exchange of information and interactions. [Brief explanation of the drawings]

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

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

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

[0034] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0041] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system includes the following components and functions:

[0046] System Configuration

[0047] 1. Information collection module

[0048] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[0049] 2. Information Analysis Module

[0050] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[0051] 3. User information input module

[0052] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[0053] 4. Teaching material recommendation module

[0054] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[0055] 5. Study Schedule Generation Module

[0056] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[0057] 6. Question Answering Module

[0058] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[0059] 7. Online Class Module

[0060] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[0061] Program processing explanation

[0062] Information collection and analysis

[0063] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[0064] Enter and submit user information

[0065] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[0066] Recommended teaching materials

[0067] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[0068] Generate a study schedule

[0069] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0070] Question and Answering

[0071] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[0072] Online classes

[0073] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[0074] Specific examples

[0075] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0076] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[0077] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[0078] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[0079] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0080] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[0081] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[0082] The processing flow will be explained below.

[0083] Information collection and analysis

[0084] Step 1:

[0085] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[0086] Step 2:

[0087] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[0088] Step 3:

[0089] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[0090] Step 4:

[0091] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[0092] Step 5:

[0093] The server stores the analysis results and reliability scores in a database.

[0094] Enter and submit user information

[0095] Step 1:

[0096] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[0097] Step 2:

[0098] The terminal converts the input user information into a data package and transmits it to the server.

[0099] Step 3:

[0100] The server passes the received user information to the analysis module and starts the analysis.

[0101] Recommended teaching materials

[0102] Step 1:

[0103] The server uses an AI model to analyze the received user information.

[0104] Step 2:

[0105] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[0106] Step 3:

[0107] The server transmits the listed educational material information to the terminal.

[0108] Step 4:

[0109] The terminal displays the transmitted educational material information to the user.

[0110] Generate a study schedule

[0111] Step 1:

[0112] The user enters their current learning progress and target exam date into the application.

[0113] Step 2:

[0114] The terminal transmits the input data to the server.

[0115] Step 3:

[0116] The server operates a study schedule generation module based on the received study progress data and target exam date.

[0117] Step 4:

[0118] The server transmits the generated study schedule to the terminal.

[0119] Step 5:

[0120] The terminal displays the schedule to the user and periodically sends reminder notifications.

[0121] Question and Answering

[0122] Step 1:

[0123] Users enter questions they have about their studies into the application's question form.

[0124] Step 2:

[0125] The terminal converts the entered question into a data package and sends it to the server.

[0126] Step 3:

[0127] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[0128] Step 4:

[0129] The server sends the generated answers and explanations to the terminal.

[0130] Step 5:

[0131] The terminal displays the answer to the user.

[0132] Online classes

[0133] Step 1:

[0134] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[0135] Step 2:

[0136] The terminal transmits the reservation information to the server.

[0137] Step 3:

[0138] The server stores this reservation information in a database and sets a reminder notification before the class date.

[0139] Step 4:

[0140] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[0141] Step 5:

[0142] The terminal displays a notification to the user and provides a join link.

[0143] Step 6:

[0144] The user clicks on the class participation link in the reminder notification to participate in the online class.

[0145] Example 1

[0146] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] Conventional learning support systems have difficulty in recommending learning plans and materials that meet the individual needs of learners, and in managing progress, resulting in the inability to provide an effective learning environment. Furthermore, the reliability of exam information and prompt responses to learners' questions are also insufficient. This places a heavy burden on test takers and their families.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0149] In this invention, the server includes: means for collecting information from nationwide assessment information and test experience stories; means for analyzing the collected information using natural language processing technology and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the generated analysis results; means for generating an effective study schedule based on the learner's learning progress data and target dates and times; means for automatically answering questions from learners using a large-scale language model; and means for providing online individual lessons and training camps and for providing a place where learners can interact with each other. This enables efficient and personalized study support and significantly reduces the burden on test takers and their families.

[0150] "National evaluation information" refers to information posted on the Internet based on test evaluations and test-taking experiences.

[0151] "Exam Experiences" are detailed reports and impressions about the exam written by test takers based on their own experiences.

[0152] "Natural language processing technology" is an artificial intelligence technology for understanding, analyzing, and generating human language.

[0153] A "large-scale language model" is an artificial intelligence model trained using massive amounts of text data, and is a technology that has the ability to understand and generate sentences like humans (e.g., GPT-4).

[0154] "Characteristics of individual learners" refers to information about characteristics related to individual learning, such as each learner's learning style, strong and weak subjects, and learning progress.

[0155] "Teaching materials and reference materials" refers to educational resources such as textbooks, workbooks, online courses, and video materials that learners use to advance their studies.

[0156] "Study progress data" is information that indicates how far a learner has progressed in their studies, and includes the content learned, the level of achievement, and the amount of time spent studying.

[0157] An "effective study schedule" is a study plan that is optimized based on the learner's goals and progress.

[0158] "Online private lessons and training camps" are private instruction and special group lessons provided via the Internet.

[0159] A "place where learners can interact" is an online platform where learners can communicate with each other, exchange information, and engage in collaborative learning.

[0160] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system is implemented as follows.

[0161] System Configuration

[0162] The system consists of the following hardware and software elements:

[0163] Server: A central management system that collects, analyzes, stores, and analyzes data using AI models.

[0164] Device: The device used by the user (computer, tablet, smartphone, etc.).

[0165] Generative AI models (e.g., GPT-4, BERT, etc.): Used to analyze collected data and user input information and provide optimal learning resources.

[0166] Program processing explanation

[0167] Information collection and analysis

[0168] The server collects data from major exam blogs and review sites on the Internet. This process uses web scraping tools such as Python's Beautiful Soup and Scrapy. The collected data is analyzed using natural language processing techniques (e.g., Google's BERT model) and a reliability score is applied. The school name, study methods, and key points about exam preparation for each post are then stored in a database.

[0169] Enter and submit user information

[0170] Users launch the application, log in, and enter their child's learning style and characteristics (e.g., attention span, preferred learning methods). The device then sends this information to the server. Communication is via the HTTPS protocol.

[0171] Recommended teaching materials

[0172] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and creates a list of learning materials and reference books that are best suited to each learner. This list is sent to the device and presented to the user. For example, the server may recommend the "Math Masters" YouTube channel.

[0173] Generate a study schedule

[0174] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0175] Question and Answering

[0176] When a user has a question they don't understand while studying, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[0177] Online classes

[0178] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[0179] Examples of concrete examples and prompts

[0180] Specific examples

[0181] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0182] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[0183] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[0184] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[0185] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0186] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[0187] Prompt Sentence Examples

[0188] "How can I effectively manage my child's learning style?"

[0189] "Please explain why the sum of the interior angles of the following triangle is 180 degrees."

[0190] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[0191] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0192] Step 1: Start gathering information

[0193] The server accesses designated exam blogs and review sites at specific time intervals. A list of URLs of the sites to be collected is used as input. HTML data obtained from each site is obtained as output. Specifically, the data is collected using web scraping tools such as Python's Beautiful Soup and Scrapy.

[0194] Step 2: Data collection

[0195] The server analyzes the collected HTML data and extracts information related to the exam. The collected HTML data is used as input. Exam information (school name, study methods, exam preparation information, etc.) is obtained as output. Specifically, the server parses the HTML data to obtain the necessary text information and stores it in a database.

[0196] Step 3: Data analysis

[0197] The server analyzes the extracted exam information using natural language processing technology (e.g., Google's BERT model). The extracted exam information is used as input. The analyzed information and a reliability score are obtained as output. Specifically, the text information is input into the BERT model, which performs semantic analysis of the information and reliability evaluation.

[0198] Step 4: Reliability Scoring

[0199] The server scores the credibility of each post based on the analysis results and assigns a tag to each one. The analyzed information and the results of the credibility assessment are used as input. The output is the test information with a credibility score. The specific operation is to add the credibility score to each entry in the database.

[0200] Step 5: Enter user information

[0201] The user launches the application, logs in, and enters information about their child's learning style and characteristics. The learning style information entered by the user is used as input. This information is sent from the device to the server as output. The specific operation requires the user to enter information into a form and press the submit button.

[0202] Step 6: Send user information

[0203] The terminal sends the entered user information to the server. As input, the learning style information entered by the user is used. As output, this information is sent to the server. As a specific operation, data is sent securely using the HTTPS protocol.

[0204] Step 7: Recommending materials

[0205] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and lists the most suitable learning materials and reference books. The user information is used as input. The output is a list of recommended learning materials and reference books. Specifically, the server inputs the user information into the AI ​​model and stores the generated recommendation list in a database.

[0206] Step 8: Submit your recommendation list

[0207] The server sends the generated teaching material recommendation list to the terminal. The generated recommendation list is used as input. This list is sent to the terminal as output. As a specific operation, the recommendation list is sent to the terminal via HTTPS protocol.

[0208] Step 9: Generate a study schedule

[0209] The server generates an efficient study schedule based on the study progress data and exam dates. The study progress data and exam dates are used as input. The generated study schedule is obtained as output. Specifically, the server calculates the optimal schedule using a scheduling algorithm (e.g., linear programming).

[0210] Step 10: Schedule Sending

[0211] The server sends the generated learning schedule to the terminal. The generated learning schedule is used as input. This schedule is sent to the terminal as output. As a specific operation, the learning schedule is sent to the terminal via the HTTPS protocol.

[0212] Step 11: Question Answering

[0213] When a user is studying, they enter a question they don't understand into a question form within the application. The question entered by the user is used as input. The question is sent from the device to the server as output. The specific operation requires the user to enter a question into the form and press the send button.

[0214] Step 12: Submit your question

[0215] The terminal sends the entered question to the server. The question entered by the user is used as input. The question is sent to the server as output. Specifically, the data is sent securely using the HTTPS protocol.

[0216] Step 13: Answer Generation

[0217] The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question. The user's question is used as input. The generated answer and explanation are obtained as output. Specifically, the question is input into the AI ​​model, and the generated answer is stored in a database.

[0218] Step 14: Submit your answers

[0219] The server sends the generated answer to the terminal. The generated answer is used as input. This answer is sent to the terminal as output. As a specific operation, the answer is sent to the terminal via the HTTPS protocol.

[0220] Step 15: Book an online class

[0221] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The lesson information reserved by the user is used as input. This reservation information is sent from the terminal to the server as output. The specific operation requires the user to select a class and press the reservation button.

[0222] Step 16: Reservation Information Management

[0223] The server saves and manages reservation information in a database. The user's reservation information is used as input. The saved reservation information is obtained as output. Specific operations include saving the reservation information in the database and displaying it on the management screen.

[0224] Step 17: Send reminder notifications

[0225] The server sends a reminder notification to the terminal before the start of the class. The reservation information and the start time of the class are used as input. The reminder notification is sent to the terminal as output. Specifically, the server generates a notification before the start time of the class and sends it to the terminal via HTTPS protocol.

[0226] Step 18: Send class participation link

[0227] The server sends a participation link to the terminal just before the class starts. The class reservation information and participation link are used as input. The participation link is sent to the terminal as output. Specifically, the participation link is generated and sent to the terminal via the HTTPS protocol.

[0228] (Application example 1)

[0229] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0230] The diverse learning environments required for junior high school entrance exams place a significant burden on students and their families. However, finding the optimal learning methods and materials for each individual student is not easy and requires time and effort. Students also need to be able to quickly and accurately respond to any questions they may have. Furthermore, because it is difficult to maximize the effectiveness of online classes and self-study tools, a system that can solve all of these issues at once is needed.

[0231] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0232] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and a place for learners to interact with each other; means for generating optimal answers using a generative AI model based on the learner's learning data; and means for providing answer prompts generated by the AI ​​model. This makes it possible to efficiently and effectively manage learner progress and quickly resolve learner questions.

[0233] "Word of mouth" refers to the sharing of opinions and experiences published by individuals or groups on the Internet.

[0234] "Exam experience stories" are records of the experiences of test takers and their families in past exams, including the preparation process and results.

[0235] "Information gathering means" refers to devices or programs that have the function of automatically obtaining word-of-mouth information and test-taking experience stories from the Internet.

[0236] "Information analysis means" refers to technologies and programs for analyzing collected information and evaluating its usefulness and reliability.

[0237] "User information input means" refers to a device or interface for inputting learner characteristic information (for example, learning style, strong subjects, weak subjects, etc.).

[0238] "Materials recommendation methods" refer to technologies and algorithms that select and recommend the most appropriate materials and reference books for each learner based on collected and analyzed information.

[0239] "Study schedule generation means" refers to a device or program that creates an efficient study schedule based on the learner's progress data and target schedule.

[0240] A "question-answering tool" is a device or program that accepts questions from learners and automatically generates answers using technology such as AI.

[0241] "Online class delivery means" refers to a system or interface for providing and facilitating individual lessons and training camps via the Internet.

[0242] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to generate and provide optimal solutions to specific tasks.

[0243] A "prompt" is a sentence or phrase that serves as a question or instruction to be input into a generative AI model.

[0244] This invention is an online learning support system that reduces the burden on junior high school entrance exam students and their families. This system includes modules for information collection, information analysis, user information input, teaching material recommendation, study schedule generation, question answering, and online classes.

[0245] System Configuration

[0246] Information collection and analysis

[0247] The server periodically collects user reviews and test-taking experiences from major online test-taking blogs and review sites. This data is analyzed using natural language processing technology and a reliability score is assigned. The analysis results are stored in a database that includes the usefulness of each post and detailed information (e.g., school name, study method, test preparation). The system primarily uses the Python programming language, with SpaCy and Transformers (Hugging Face) as natural language processing libraries.

[0248] Enter and submit user information

[0249] After launching the application, users enter information about their child's learning style and characteristics, including attention span, preferred learning methods, and favorite and least favorite subjects, on their smartphone screen. The entered data is then sent from the device to a server.

[0250] Recommended teaching materials

[0251] The server selects the most suitable learning materials and reference books for each user based on the information received from the user. During this process, it analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of recommended learning materials. The generated list is sent to the device and provided to the user.

[0252] Generate a study schedule

[0253] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate a customized learning schedule for each user, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0254] Question and Answering

[0255] When a user is studying, they enter a question they don't understand into the question form within the application. The question is sent from the device to the server. The server uses a generative AI model to generate the optimal answer and explanation for the question and sends it back to the device. The user can check the answer in real time and continue studying. As a specific example, if a user asks "Why is the sum of the interior angles of a triangle 180 degrees?" the server sends the following prompt to the AI ​​model to generate an answer.

[0256] Input: Why the sum of the interior angles of a triangle is 180 degrees

[0257] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[0258] Online classes

[0259] Users can use the application to reserve online individual lessons or online training camps. The server manages reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link so the user can join the class.

[0260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0261] Step 1: Gather information

[0262] The server automatically collects test-taking experience information and reviews from major test-taking blogs and review sites on the Internet. This process involves periodically accessing specific websites and obtaining data in HTML or JSON format. Specifically, data is extracted using scraping tools and APIs. The input is a URL or search query, and the output is the collected raw data.

[0263] Step 2: Information analysis

[0264] The server analyzes the information collected in step 1 using natural language processing technology. It uses Python's SpaCy and Transformers libraries to extract useful information from the text data and score its reliability. The input is raw data, and the output is analyzed information and its reliability score. Specifically, it performs text summarization, keyword extraction, sentiment analysis, etc.

[0265] Step 3: Enter user information

[0266] The user launches the application and enters information about their child's learning style and characteristics, including attention span, preferred learning methods, and strong and weak subjects. The input is the data provided in each information form, and the output is structured user information that the device sends to the server.

[0267] Step 4: Recommending materials

[0268] The server performs analysis based on the user information acquired in step 3 to recommend optimal learning materials and reference books. It analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of appropriate learning materials for the user. The input is user information, and the output is a list of recommended learning materials. Specifically, it selects the most appropriate learning materials based on past data and the AI ​​model.

[0269] Step 5: Create a study schedule

[0270] The user inputs their current learning progress and exam date. The device sends this information to the server. The server generates a customized learning schedule based on this data and periodically updates and adjusts it. The input is learning progress data and exam date, and the output is the learning schedule. Specifically, it generates a Gantt chart and allocates daily learning tasks.

[0271] Step 6: Question-answering

[0272] During learning, users enter questions they don't understand into a question form within the application. This question is sent from the device to the server. The server uses a generative AI model (such as GPT-3) to generate the optimal answer and explanation for the question. The input is the user's question, and the output is the generated answer and explanation. Specifically, it generates an appropriate answer for the following prompt:

[0273] Input: Why the sum of the interior angles of a triangle is 180 degrees

[0274] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[0275] Step 7: Online classes

[0276] Users use the application to book online individual lessons or training camp classes. The server manages the user's reservation information and sends a reminder notification and a participation link to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link, allowing the user to join the class. The input is the class reservation date and time and user information, and the output is a reminder notification and a participation link. Specifically, the calendar API is used to set up notifications and generate a link for the video conferencing system.

[0277] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0278] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[0279] System Configuration

[0280] 1. Information collection module

[0281] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[0282] 2. Information Analysis Module

[0283] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[0284] 3. User information input module

[0285] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[0286] 4. Teaching material recommendation module

[0287] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[0288] 5. Study Schedule Generation Module

[0289] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[0290] 6. Question Answering Module

[0291] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[0292] 7. Online Class Module

[0293] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[0294] 8. Emotion Recognition Engine

[0295] The server is equipped with an emotion recognition engine that recognizes the user's emotions and analyzes the user's emotional state during learning in real time.

[0296] Program processing explanation

[0297] Information collection and analysis

[0298] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[0299] Enter and submit user information

[0300] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[0301] Recommended teaching materials

[0302] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[0303] Generate a study schedule

[0304] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0305] Question and Answering

[0306] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[0307] Online classes

[0308] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[0309] How the emotion recognition engine works

[0310] The server analyzes the user's facial expressions and tone of voice captured through the camera and microphone, and evaluates the user's emotional state in real time. The emotion recognition engine determines the user's state, such as "concentrated," "tired," or "stressed."

[0311] 1. Collecting Emotional Data

[0312] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[0313] 2. Emotion analysis

[0314] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[0315] 3. Feedback and Adjustments

[0316] The server adjusts the learning content and schedule based on the user's emotional state. For example, if the server recognizes that the user is tired, it will suggest that the user take a break. If the user is concentrating, it will give feedback to continue learning.

[0317] 4. Sending encouraging messages

[0318] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device. For example, if the user has been concentrating for a long time, a positive message such as "Keep it up!" will be displayed.

[0319] Specific examples

[0320] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0321] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[0322] 2. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable teaching material, and sends this to the device.

[0323] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[0324] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0325] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[0326] 6. While learning, the device captures the user's facial expressions and voice using a camera and microphone and sends them to the server. The server's emotion recognition engine determines that the user is "concentrating" and instructs the user to continue with the recommended learning material.

[0327] 7. If the user begins to feel tired while studying, the emotion recognition engine will determine that they are tired, and the server will display a message on the device saying, "Take a break."

[0328] In this way, by combining an emotion recognition engine, flexible support according to the user's condition becomes possible, maximizing learning efficiency and effectiveness.

[0329] The processing flow will be explained below.

[0330] Information collection and analysis

[0331] Step 1:

[0332] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[0333] Step 2:

[0334] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[0335] Step 3:

[0336] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[0337] Step 4:

[0338] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[0339] Step 5:

[0340] The server stores the analysis results and reliability scores in a database.

[0341] Enter and submit user information

[0342] Step 1:

[0343] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[0344] Step 2:

[0345] The terminal converts the input user information into a data package and transmits it to the server.

[0346] Step 3:

[0347] The server passes the received user information to the analysis module and starts the analysis.

[0348] Recommended teaching materials

[0349] Step 1:

[0350] The server uses an AI model to analyze the received user information.

[0351] Step 2:

[0352] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[0353] Step 3:

[0354] The server transmits the listed educational material information to the terminal.

[0355] Step 4:

[0356] The terminal displays the transmitted educational material information to the user.

[0357] Generate a study schedule

[0358] Step 1:

[0359] The user enters their current learning progress and target exam date into the application.

[0360] Step 2:

[0361] The terminal transmits the input data to the server.

[0362] Step 3:

[0363] The server operates a study schedule generation module based on the received study progress data and target exam date.

[0364] Step 4:

[0365] The server transmits the generated study schedule to the terminal.

[0366] Step 5:

[0367] The terminal displays the schedule to the user and periodically sends reminder notifications.

[0368] Question and Answering

[0369] Step 1:

[0370] Users enter questions they have about their studies into the application's question form.

[0371] Step 2:

[0372] The terminal converts the entered question into a data package and sends it to the server.

[0373] Step 3:

[0374] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[0375] Step 4:

[0376] The server sends the generated answers and explanations to the terminal.

[0377] Step 5:

[0378] The terminal displays the answer to the user.

[0379] Online classes

[0380] Step 1:

[0381] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[0382] Step 2:

[0383] The terminal transmits the reservation information to the server.

[0384] Step 3:

[0385] The server stores this reservation information in a database and sets a reminder notification before the class date.

[0386] Step 4:

[0387] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[0388] Step 5:

[0389] The terminal displays a notification to the user and provides a join link.

[0390] Step 6:

[0391] The user clicks on the class participation link in the reminder notification to participate in the online class.

[0392] How the emotion recognition engine works

[0393] Step 1:

[0394] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[0395] Step 2:

[0396] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[0397] Step 3:

[0398] The server adjusts learning content and schedules based on the user's emotional state. For example, if the server recognizes that the user is tired, it will display a suggestion such as "Take a break."

[0399] Step 4:

[0400] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device.

[0401] Step 5:

[0402] The device will then display the message to the user, for example, if they have been concentrating for a long time, it will display a positive message such as "Keep it up!"

[0403] Example 2

[0404] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0405] Current online learning support systems lack personalization based on learners' characteristics and progress, making it difficult to provide optimal learning content and schedules for individual learners. Furthermore, they are unable to assess learners' emotional states in real time and provide appropriate feedback, resulting in insufficient support when learners become tired or lose concentration. This can lead to reduced learning effectiveness.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0407] In this invention, the server includes means for collecting information from word-of-mouth information and test-taking experiences across the country, means for analyzing the collected information and aggregating useful information, means for inputting and analyzing the characteristics of individual learners, means for recommending learning materials and reference books suitable for each learner based on the analysis results, means for generating test-taking preparation schedules according to the learner's progress, means for automatically answering questions from learners, means for offering individual lessons and training camps online and providing a place for learners to interact with each other, and means for evaluating the learner's emotional state in real time and providing feedback and support. This makes it possible to provide optimal learning content and schedules based on the learner's characteristics and emotional state, maximizing the learner's learning efficiency and effectiveness.

[0408] "Means for collecting information" refers to modules or software for obtaining data from online exam blogs and review sites.

[0409] "Means for analyzing information" refers to a module or software that evaluates acquired data using natural language processing technology and extracts useful information.

[0410] The "means for inputting and analyzing characteristics" refers to a module or software for inputting characteristic information such as a learner's learning style, strong subjects, and weak subjects into a server and analyzing this information.

[0411] "Means for recommending learning materials and reference books" refers to a module or software that selects and recommends the most suitable learning materials and reference books to learners based on the analysis results.

[0412] The "means for generating an exam preparation schedule" is a module or software for automatically generating an efficient study schedule based on the learner's progress information and exam date.

[0413] An "automatic answering means" is a module or software that uses an artificial intelligence model to generate and provide appropriate answers and explanations to questions posted by learners.

[0414] "Means for providing individual lessons and training camps" means modules or software for managing and providing individual lessons and training camps that can be attended by learners online.

[0415] The "means for assessing the learner's emotional state in real time" is a module or software for analyzing data acquired through a camera or microphone and determining the learner's emotional state in real time.

[0416] "Means for providing feedback and support" refers to a module or software that automatically provides appropriate encouraging messages and adjustments to learning content according to the learner's emotional state.

[0417] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[0418] System Configuration

[0419] This system mainly consists of the following modules and hardware components.

[0420] 1. Information collection module

[0421] The server periodically collects online reviews and test-taking experiences using Python and the Beautiful Soup library, and stores the collected data in a database in CSV or JSON format.

[0422] 2. Information Analysis Module

[0423] The server analyzes the collected data using natural language processing tools (e.g., NLTK or SpaCy), and generates a reliability score and categorizes the results.

[0424] 3. User information input module

[0425] The device sends the learner's characteristics (learning style, strong and weak subjects, etc.) entered by the user to the server using the HTTPS protocol.

[0426] 4. Teaching material recommendation module

[0427] The server analyzes user information using AI models such as TensorFlow and PyTorch, and based on the analysis results, it creates a list of learning materials and reference books suitable for each learner and sends them to the device.

[0428] 5. Study Schedule Generation Module

[0429] The server generates an efficient study schedule based on the learner's progress data and target date (exam date). The generated schedule is updated in real time and sent to the device.

[0430] 6. Question Answering Module

[0431] The device sends the user-entered question to the server, which uses BERT or GPT models to generate an answer and explanation for the question and sends it back to the device.

[0432] 7. Online Class Module

[0433] The server manages the online individual lessons and camp lessons booked by users, provides links to join the lessons, and also sends reminders before the lessons start.

[0434] 8. Emotion Recognition Engine

[0435] The server analyzes the user's facial expressions and tone of voice captured through a camera and microphone, and evaluates the user's emotional state in real time.

[0436] Specific examples

[0437] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0438] After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the application to the server. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable learning material, and sends this to the device. Alternatively, if the child enters "Why is the sum of the interior angles of a triangle 180 degrees?" into a question form, the content is sent from the device to the server. The server generates an answer using an AI model, explaining that "The sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0439] Users can also reserve online individual lessons for the next month and receive reminder notifications from the server to ensure they don't miss them. They then click the class participation link at the specified time to attend the class. While studying, the device uses a camera and microphone to capture the user's facial expressions and voice and send them to the server. The server's emotion recognition engine determines that the user is "concentrating" and instructs the user to continue with the recommended learning materials. If the user begins to feel tired while studying, the emotion recognition engine determines that the user is "tired," and the server displays a message on the device saying, "Take a break."

[0440] Prompt Sentence Examples

[0441] As an example of a prompt, the following prompt is entered:

[0442] "Why does the sum of the interior angles of a triangle equal 180 degrees?"

[0443] "Please let me know the best study guide for exam preparation."

[0444] "Please tell me how to help my child concentrate on their studies."

[0445] The present invention allows users to be provided with optimal learning content and schedules based on the learner's characteristics and emotional state, thereby maximizing learning efficiency and effectiveness.

[0446] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0447] Step 1: Gather information

[0448] The server periodically collects data from online test-taking blogs and review sites using Python and the Beautiful Soup library. Specifically, the server creates a list of URLs and parses the HTML to extract text data. The input is the list of URLs to be collected, and the output is to save the extracted data in CSV or JSON format.

[0449] Step 2: Information analysis

[0450] The server analyzes the collected data using natural language processing tools (NLTK and SpaCy). Specifically, it filters unnecessary information from the text and calculates a reliability score. It also classifies the data into categories and extracts useful information. The input is the collected raw data, and the output is the reliability score and the data classified by category.

[0451] Step 3: Enter and submit user information

[0452] The user inputs learner characteristics (learning style, strong and weak subjects, etc.) through the application. The input information is sent to the server via the terminal using the HTTPS protocol. The input is the learner's characteristic data, and the output is the data sent to the server.

[0453] Step 4: Recommending materials

[0454] The server analyzes the received user information using an AI model such as TensorFlow or PyTorch. Specifically, the AI ​​model receives the learner's characteristics as input and lists the most suitable learning materials and reference books. The input is the learner's characteristic data, and the output is a list of recommended learning materials. The recommended list is presented to the user via their device.

[0455] Step 5: Generate a study schedule

[0456] The user inputs their current study progress and exam dates into the application. This information is sent to the server via the terminal. The server uses a Python scheduling library to generate an efficient study schedule. The input is study progress data and exam dates, and the output is the generated study schedule. The schedule is updated in real time and provided to the user via the terminal.

[0457] Step 6: Question-answering

[0458] Users enter questions they don't understand during their studies into a question form within the application. This question is sent to the server via the device. The server uses BERT or GPT models to generate an answer and explanation for the question. The input is the question, and the output is the answer and explanation. The answer is displayed to the user via the device.

[0459] Step 7: Book and attend online classes

[0460] A user reserves an online class using an application. The reservation information is sent to the server via the device. The server manages the reservation information and sends a reminder notification and a participation link to the device before the class starts. The input is the reservation information, and the output is the reminder notification and the participation link. When the class time arrives, the device sends a notification to the user, who clicks the participation link to join the class.

[0461] Step 8: Emotion Recognition

[0462] During learning, the device uses a camera and microphone to capture the user's facial expressions and voice. This data is sent to the server in real time. The server's emotion recognition engine analyzes this data and identifies the user's emotional state. The input is the captured facial and voice data, and the output is the user's emotional state. For example, states such as "concentrated" or "tired" are determined. Based on the emotional state, the server generates appropriate feedback or encouraging messages and sends them to the device.

[0463] (Application example 2)

[0464] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0465] Conventional online learning support systems have the problem of being unable to provide flexible support that responds to the learner's individual emotional state, preventing them from maximizing learning efficiency. The present invention aims to solve these problems and enable appropriate learning content and schedule adjustments based on the learner's emotional state. It also aims to increase the learner's persistence in learning by providing encouraging messages that maintain the learner's motivation and reduce fatigue and stress.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0467] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test-taking preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and providing a place for learners to interact with each other; means for recognizing the user's emotional state in real time and adjusting the learning content and schedule based on that state; and means for automatically generating and presenting encouraging messages based on the emotional state. This makes it possible to adjust the learning content and schedule appropriately according to the learner's emotional state, thereby improving learning efficiency and sustainability.

[0468] "Means of collecting information" refers to a function for collecting information from word-of-mouth information and test-taking experiences across the country.

[0469] "Means for analyzing information and aggregating useful information" refers to the function of analyzing collected information, selecting useful information, and aggregating it.

[0470] "Means for inputting and analyzing the characteristics of individual learners" is a function that inputs learner characteristics (learning style, strong and weak subjects, etc.) and analyzes them.

[0471] "Means for recommending teaching materials and reference books suitable for each learner" is a function that suggests the most suitable teaching materials and reference books for each learner based on the analysis results.

[0472] The "means for generating an exam preparation schedule" is a function that creates a study schedule necessary for exams based on the learner's progress.

[0473] "Means for automatically answering questions from learners" is a function that uses an AI model to automatically provide answers to questions submitted by learners.

[0474] "A means of offering individual lessons and training camps online, and providing a place for learners to interact with each other" refers to a function that holds individual lessons and group lessons online, and provides a place for learners to communicate with each other.

[0475] "Means for recognizing the user's emotional state in real time and adjusting the learning content and schedule based on that state" refers to a function that analyzes the user's emotional state in real time through a camera or microphone and flexibly changes the learning content and schedule based on the results.

[0476] The "means for automatically generating and presenting an encouraging message based on the emotional state" is a function for automatically creating and displaying an encouraging message in accordance with the emotional state of the user.

[0477] The present invention provides an online learning support system that recognizes the emotional state of a learner and adjusts the learning content and schedule based on that state. Specific embodiments of the present invention are described below.

[0478] System Overview

[0479] This system is composed of a server, terminals, and user interactions. The main modules include an information collection module, an information analysis module, a user information input module, a learning material recommendation module, a study schedule generation module, a question-answering module, an online class module, and an emotion recognition engine.

[0480] Hardware and Software

[0481] Hardware:

[0482] Smart glasses (with built-in camera and microphone)

[0483] server

[0484] User device (smartphone or tablet)

[0485] software:

[0486] Python programming language

[0487] OpenCV (image processing library)

[0488] DeepFace (emotion recognition library)

[0489] Playsound (audio playback library)

[0490] Program processing explanation

[0491] Information Collection Module

[0492] The server collects data in real time from major online blogs and review sites for entrance exams. This information is collected using web scraping technology. The collected data is then stored in a database for later analysis.

[0493] Information Analysis Module

[0494] The server analyzes the collected word-of-mouth information and test-taking experiences using natural language processing (NLP). Specifically, it uses text mining tools to extract useful information. It also evaluates the reliability of the information and extracts only valid data.

[0495] User information input module

[0496] Learners enter their characteristics, learning style, and current progress through their terminals. This information is sent to the server and used as the basis for analysis.

[0497] Material recommendation module

[0498] Based on the learner information sent, the server uses an AI model to recommend the most suitable learning materials and reference books. This recommendation list is sent to the user's device, allowing the user to proceed with their studies accordingly.

[0499] Study schedule generation module

[0500] The server generates an efficient study schedule taking into account the learner's progress and target date and time (e.g., exam date). The generated schedule is periodically updated and notified to the user's terminal.

[0501] Question and Answer Module

[0502] When users enter questions that arise during their studies into the application, the data is sent to the server, which uses an AI model to generate answers and detailed explanations and send them back to the user's device.

[0503] Online Class Module

[0504] Users can book individual or group lessons through the application. The server manages the reservation information and sends reminders and participation links before the lesson.

[0505] Emotion Recognition Engine

[0506] The server analyzes the user's facial expressions and voice in real time, captured through the smart glasses' camera and microphone, to assess the user's emotional state. The analysis uses the DeepFace library and performs the following steps:

[0507] Capture facial and voice data

[0508] Emotional state analysis

[0509] Adjusting learning content and schedules based on emotional state

[0510] Automatically generate and display encouraging messages

[0511] Specific examples

[0512] Consider a scenario where a user is preparing for a junior high school entrance exam. The user inputs their learning style information and receives recommended study materials, such as "math workbooks." While studying, if the user inputs a question about the sum of the interior angles of a triangle, the AI ​​model generates and notifies the user with an answer, such as "The sum of the interior angles of a triangle is 180 degrees." The smart glasses monitor the learner's level of concentration, prompting them to continue studying if they are focused, or encouraging them to take a break if they are tired.

[0513] Prompt Sentence Examples

[0514] "Generate positive messages to encourage stressed workers to take breaks."

[0515] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0516] Step 1:

[0517] Data collection by information collection module

[0518] The server collects data in real time from online exam blogs and review sites, specifically using web scraping technology to obtain exam-related information and store that data in a database.

[0519] Input: URL of an online exam blog or review site

[0520] Data processing: Extracting information through web scraping

[0521] Output: Collected exam information database

[0522] Step 2:

[0523] Data analysis using the information analysis module

[0524] The server analyzes the collected reviews and test-taking experiences using natural language processing (NLP) technology. Specifically, it uses text mining tools to extract useful information and evaluate its reliability.

[0525] Input: Collected exam information database

[0526] Data Computing: Text Analysis and Trust Assessment with NLP

[0527] Output: A database of useful analyzed information

[0528] Step 3:

[0529] Collecting characteristic information using the user information input module

[0530] Users use a smartphone or tablet to input information about their learner characteristics (such as their learning style, favorite subjects, and progress). The input information is then sent from the device to the server.

[0531] Input: Learner characteristics information

[0532] Data processing: Sending to the server

[0533] Output: Learner characteristics data stored on the server

[0534] Step 4:

[0535] Recommending materials using the material recommendation module

[0536] The server uses an AI model to recommend optimal learning materials and reference books based on the learner's characteristic data, and notifies the user of the generated recommendation list.

[0537] Input: Learner characteristics data, useful information database

[0538] Data calculation: Calculating optimal teaching materials using AI models

[0539] Output: Recommended learning materials list

[0540] Step 5:

[0541] Schedule creation using the study schedule generation module

[0542] The server generates an efficient study schedule based on the learner's progress and target date and time (e.g., exam date). The generated schedule is updated periodically and notified to the user's device.

[0543] Input: Learner progress, target date and time

[0544] Data Computation: Generating Efficient Schedules

[0545] Output: Study schedule

[0546] Step 6:

[0547] Answering questions using the question answering module

[0548] When a user has a question that arises during their study, they input it into their device and the data is sent to the server, which uses an AI model to generate an answer and detailed explanation, which is then sent back to the user's device.

[0549] Input: Question data from the user

[0550] Data Computation: Answer Generation with AI Models

[0551] Output: Answer and detailed explanation

[0552] Step 7:

[0553] Offering classes through online lesson modules

[0554] Users make reservations for individual or group lessons through the application. The server manages the reservation information and sends reminders and participation links before the lesson.

[0555] Input: Class reservation information

[0556] Data management: Reservation information management

[0557] Output: Reminder notification, participation link

[0558] Step 8:

[0559] Emotion analysis using an emotion recognition engine

[0560] The server analyzes the user's facial expressions and voice in real time, captured through the smart glasses' camera and microphone, and evaluates the user's emotional state. Based on the analysis results, the server adjusts the learning content and schedule and provides appropriate feedback.

[0561] Input: User's facial expression data, voice data

[0562] Data Computation: Emotion Analysis with DeepFace

[0563] Output: Adjustment of learning content and schedule based on emotional state, encouraging messages

[0564] Example processing steps

[0565] While the user is studying, the smart glasses analyze the user's facial expression and determine that the user is "highly stressed." In this case, the server automatically generates a message encouraging the user to "take a break" and notifies the user by voice and text. An example of a prompt sentence is "Please generate a positive message to suggest that highly stressed workers take a break."

[0566] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0567] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0568] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0569] [Second embodiment]

[0570] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0571] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0572] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0573] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0574] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0575] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0576] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0577] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0578] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0579] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0580] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0581] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0582] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system includes the following components and functions:

[0583] System Configuration

[0584] 1. Information collection module

[0585] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[0586] 2. Information Analysis Module

[0587] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[0588] 3. User information input module

[0589] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[0590] 4. Teaching material recommendation module

[0591] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[0592] 5. Study Schedule Generation Module

[0593] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[0594] 6. Question Answering Module

[0595] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[0596] 7. Online Class Module

[0597] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[0598] Program processing explanation

[0599] Information collection and analysis

[0600] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[0601] Enter and submit user information

[0602] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[0603] Recommended teaching materials

[0604] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[0605] Generate a study schedule

[0606] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0607] Question and Answering

[0608] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[0609] Online classes

[0610] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[0611] Specific examples

[0612] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0613] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[0614] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[0615] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[0616] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0617] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[0618] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[0619] The processing flow will be explained below.

[0620] Information collection and analysis

[0621] Step 1:

[0622] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[0623] Step 2:

[0624] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[0625] Step 3:

[0626] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[0627] Step 4:

[0628] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[0629] Step 5:

[0630] The server stores the analysis results and reliability scores in a database.

[0631] Enter and submit user information

[0632] Step 1:

[0633] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[0634] Step 2:

[0635] The terminal converts the input user information into a data package and transmits it to the server.

[0636] Step 3:

[0637] The server passes the received user information to the analysis module and starts the analysis.

[0638] Recommended teaching materials

[0639] Step 1:

[0640] The server uses an AI model to analyze the received user information.

[0641] Step 2:

[0642] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[0643] Step 3:

[0644] The server transmits the listed educational material information to the terminal.

[0645] Step 4:

[0646] The terminal displays the transmitted educational material information to the user.

[0647] Generate a study schedule

[0648] Step 1:

[0649] The user enters their current learning progress and target exam date into the application.

[0650] Step 2:

[0651] The terminal transmits the input data to the server.

[0652] Step 3:

[0653] The server operates a study schedule generation module based on the received study progress data and target exam date.

[0654] Step 4:

[0655] The server transmits the generated study schedule to the terminal.

[0656] Step 5:

[0657] The terminal displays the schedule to the user and periodically sends reminder notifications.

[0658] Question and Answering

[0659] Step 1:

[0660] Users enter questions they have about their studies into the application's question form.

[0661] Step 2:

[0662] The terminal converts the entered question into a data package and sends it to the server.

[0663] Step 3:

[0664] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[0665] Step 4:

[0666] The server sends the generated answers and explanations to the terminal.

[0667] Step 5:

[0668] The terminal displays the answer to the user.

[0669] Online classes

[0670] Step 1:

[0671] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[0672] Step 2:

[0673] The terminal transmits the reservation information to the server.

[0674] Step 3:

[0675] The server stores this reservation information in a database and sets a reminder notification before the class date.

[0676] Step 4:

[0677] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[0678] Step 5:

[0679] The terminal displays a notification to the user and provides a join link.

[0680] Step 6:

[0681] The user clicks on the class participation link in the reminder notification to participate in the online class.

[0682] Example 1

[0683] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0684] Conventional learning support systems have difficulty in recommending learning plans and materials that meet the individual needs of learners, and in managing progress, resulting in the inability to provide an effective learning environment. Furthermore, the reliability of exam information and prompt responses to learners' questions are also insufficient. This places a heavy burden on test takers and their families.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0686] In this invention, the server includes: means for collecting information from nationwide assessment information and test experience stories; means for analyzing the collected information using natural language processing technology and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the generated analysis results; means for generating an effective study schedule based on the learner's learning progress data and target dates and times; means for automatically answering questions from learners using a large-scale language model; and means for providing online individual lessons and training camps and for providing a place where learners can interact with each other. This enables efficient and personalized study support and significantly reduces the burden on test takers and their families.

[0687] "National evaluation information" refers to information posted on the Internet based on test evaluations and test-taking experiences.

[0688] "Exam Experiences" are detailed reports and impressions about the exam written by test takers based on their own experiences.

[0689] "Natural language processing technology" is an artificial intelligence technology for understanding, analyzing, and generating human language.

[0690] A "large-scale language model" is an artificial intelligence model trained using massive amounts of text data, and is a technology that has the ability to understand and generate sentences like humans (e.g., GPT-4).

[0691] "Characteristics of individual learners" refers to information about characteristics related to individual learning, such as each learner's learning style, strong and weak subjects, and learning progress.

[0692] "Teaching materials and reference materials" refers to educational resources such as textbooks, workbooks, online courses, and video materials that learners use to advance their studies.

[0693] "Study progress data" is information that indicates how far a learner has progressed in their studies, and includes the content learned, the level of achievement, and the amount of time spent studying.

[0694] An "effective study schedule" is a study plan that is optimized based on the learner's goals and progress.

[0695] "Online private lessons and training camps" are private instruction and special group lessons provided via the Internet.

[0696] A "place where learners can interact" is an online platform where learners can communicate with each other, exchange information, and engage in collaborative learning.

[0697] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system is implemented as follows.

[0698] System Configuration

[0699] The system consists of the following hardware and software elements:

[0700] Server: A central management system that collects, analyzes, stores, and analyzes data using AI models.

[0701] Device: The device used by the user (computer, tablet, smartphone, etc.).

[0702] Generative AI models (e.g., GPT-4, BERT, etc.): Used to analyze collected data and user input information and provide optimal learning resources.

[0703] Program processing explanation

[0704] Information collection and analysis

[0705] The server collects data from major exam blogs and review sites on the Internet. This process uses web scraping tools such as Python's Beautiful Soup and Scrapy. The collected data is analyzed using natural language processing techniques (e.g., Google's BERT model) and a reliability score is applied. The school name, study methods, and key points about exam preparation for each post are then stored in a database.

[0706] Enter and submit user information

[0707] Users launch the application, log in, and enter their child's learning style and characteristics (e.g., attention span, preferred learning methods). The device then sends this information to the server. Communication is via the HTTPS protocol.

[0708] Recommended teaching materials

[0709] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and creates a list of learning materials and reference books that are best suited to each learner. This list is sent to the device and presented to the user. For example, the server may recommend the "Math Masters" YouTube channel.

[0710] Generate a study schedule

[0711] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0712] Question and Answering

[0713] When a user has a question they don't understand while studying, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[0714] Online classes

[0715] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[0716] Examples of concrete examples and prompts

[0717] Specific examples

[0718] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0719] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[0720] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[0721] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[0722] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0723] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[0724] Prompt Sentence Examples

[0725] "How can I effectively manage my child's learning style?"

[0726] "Please explain why the sum of the interior angles of the following triangle is 180 degrees."

[0727] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[0728] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0729] Step 1: Start gathering information

[0730] The server accesses designated exam blogs and review sites at specific time intervals. A list of URLs of the sites to be collected is used as input. HTML data obtained from each site is obtained as output. Specifically, the data is collected using web scraping tools such as Python's Beautiful Soup and Scrapy.

[0731] Step 2: Data collection

[0732] The server analyzes the collected HTML data and extracts information related to the exam. The collected HTML data is used as input. Exam information (school name, study methods, exam preparation information, etc.) is obtained as output. Specifically, the server parses the HTML data to obtain the necessary text information and stores it in a database.

[0733] Step 3: Data analysis

[0734] The server analyzes the extracted exam information using natural language processing technology (e.g., Google's BERT model). The extracted exam information is used as input. The analyzed information and a reliability score are obtained as output. Specifically, the text information is input into the BERT model, which performs semantic analysis of the information and reliability evaluation.

[0735] Step 4: Reliability Scoring

[0736] The server scores the credibility of each post based on the analysis results and assigns a tag to each one. The analyzed information and the results of the credibility assessment are used as input. The output is the test information with a credibility score. The specific operation is to add the credibility score to each entry in the database.

[0737] Step 5: Enter user information

[0738] The user launches the application, logs in, and enters information about their child's learning style and characteristics. The learning style information entered by the user is used as input. This information is sent from the device to the server as output. The specific operation requires the user to enter information into a form and press the submit button.

[0739] Step 6: Send user information

[0740] The terminal sends the entered user information to the server. As input, the learning style information entered by the user is used. As output, this information is sent to the server. As a specific operation, data is sent securely using the HTTPS protocol.

[0741] Step 7: Recommending materials

[0742] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and lists the most suitable learning materials and reference books. The user information is used as input. The output is a list of recommended learning materials and reference books. Specifically, the server inputs the user information into the AI ​​model and stores the generated recommendation list in a database.

[0743] Step 8: Submit your recommendation list

[0744] The server sends the generated teaching material recommendation list to the terminal. The generated recommendation list is used as input. This list is sent to the terminal as output. As a specific operation, the recommendation list is sent to the terminal via HTTPS protocol.

[0745] Step 9: Generate a study schedule

[0746] The server generates an efficient study schedule based on the study progress data and exam dates. The study progress data and exam dates are used as input. The generated study schedule is obtained as output. Specifically, the server calculates the optimal schedule using a scheduling algorithm (e.g., linear programming).

[0747] Step 10: Schedule Sending

[0748] The server sends the generated learning schedule to the terminal. The generated learning schedule is used as input. This schedule is sent to the terminal as output. As a specific operation, the learning schedule is sent to the terminal via the HTTPS protocol.

[0749] Step 11: Question Answering

[0750] When a user is studying, they enter a question they don't understand into a question form within the application. The question entered by the user is used as input. The question is sent from the device to the server as output. The specific operation requires the user to enter a question into the form and press the send button.

[0751] Step 12: Submit your question

[0752] The terminal sends the entered question to the server. The question entered by the user is used as input. The question is sent to the server as output. Specifically, the data is sent securely using the HTTPS protocol.

[0753] Step 13: Answer Generation

[0754] The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question. The user's question is used as input. The generated answer and explanation are obtained as output. Specifically, the question is input into the AI ​​model, and the generated answer is stored in a database.

[0755] Step 14: Submit your answers

[0756] The server sends the generated answer to the terminal. The generated answer is used as input. This answer is sent to the terminal as output. As a specific operation, the answer is sent to the terminal via the HTTPS protocol.

[0757] Step 15: Book an online class

[0758] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The lesson information reserved by the user is used as input. This reservation information is sent from the terminal to the server as output. The specific operation requires the user to select a class and press the reservation button.

[0759] Step 16: Reservation Information Management

[0760] The server saves and manages reservation information in a database. The user's reservation information is used as input. The saved reservation information is obtained as output. Specific operations include saving the reservation information in the database and displaying it on the management screen.

[0761] Step 17: Send reminder notifications

[0762] The server sends a reminder notification to the terminal before the start of the class. The reservation information and the start time of the class are used as input. The reminder notification is sent to the terminal as output. Specifically, the server generates a notification before the start time of the class and sends it to the terminal via HTTPS protocol.

[0763] Step 18: Send class participation link

[0764] The server sends a participation link to the terminal just before the class starts. The class reservation information and participation link are used as input. The participation link is sent to the terminal as output. Specifically, the participation link is generated and sent to the terminal via the HTTPS protocol.

[0765] (Application example 1)

[0766] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0767] The diverse learning environments required for junior high school entrance exams place a significant burden on students and their families. However, finding the optimal learning methods and materials for each individual student is not easy and requires time and effort. Students also need to be able to quickly and accurately respond to any questions they may have. Furthermore, because it is difficult to maximize the effectiveness of online classes and self-study tools, a system that can solve all of these issues at once is needed.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0769] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and a place for learners to interact with each other; means for generating optimal answers using a generative AI model based on the learner's learning data; and means for providing answer prompts generated by the AI ​​model. This makes it possible to efficiently and effectively manage learner progress and quickly resolve learner questions.

[0770] "Word of mouth" refers to the sharing of opinions and experiences published by individuals or groups on the Internet.

[0771] "Exam experience stories" are records of the experiences of test takers and their families in past exams, including the preparation process and results.

[0772] "Information gathering means" refers to devices or programs that have the function of automatically obtaining word-of-mouth information and test-taking experience stories from the Internet.

[0773] "Information analysis means" refers to technologies and programs for analyzing collected information and evaluating its usefulness and reliability.

[0774] "User information input means" refers to a device or interface for inputting learner characteristic information (for example, learning style, strong subjects, weak subjects, etc.).

[0775] "Materials recommendation methods" refer to technologies and algorithms that select and recommend the most appropriate materials and reference books for each learner based on collected and analyzed information.

[0776] "Study schedule generation means" refers to a device or program that creates an efficient study schedule based on the learner's progress data and target schedule.

[0777] A "question-answering tool" is a device or program that accepts questions from learners and automatically generates answers using technology such as AI.

[0778] "Online class delivery means" refers to a system or interface for providing and facilitating individual lessons and training camps via the Internet.

[0779] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to generate and provide optimal solutions to specific tasks.

[0780] A "prompt" is a sentence or phrase that serves as a question or instruction to be input into a generative AI model.

[0781] This invention is an online learning support system that reduces the burden on junior high school entrance exam students and their families. This system includes modules for information collection, information analysis, user information input, teaching material recommendation, study schedule generation, question answering, and online classes.

[0782] System Configuration

[0783] Information collection and analysis

[0784] The server periodically collects user reviews and test-taking experiences from major online test-taking blogs and review sites. This data is analyzed using natural language processing technology and a reliability score is assigned. The analysis results are stored in a database that includes the usefulness of each post and detailed information (e.g., school name, study method, test preparation). The system primarily uses the Python programming language, with SpaCy and Transformers (Hugging Face) as natural language processing libraries.

[0785] Enter and submit user information

[0786] After launching the application, users enter information about their child's learning style and characteristics, including attention span, preferred learning methods, and favorite and least favorite subjects, on their smartphone screen. The entered data is then sent from the device to a server.

[0787] Recommended teaching materials

[0788] The server selects the most suitable learning materials and reference books for each user based on the information received from the user. During this process, it analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of recommended learning materials. The generated list is sent to the device and provided to the user.

[0789] Generate a study schedule

[0790] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate a customized learning schedule for each user, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0791] Question and Answering

[0792] When a user is studying, they enter a question they don't understand into the question form within the application. The question is sent from the device to the server. The server uses a generative AI model to generate the optimal answer and explanation for the question and sends it back to the device. The user can check the answer in real time and continue studying. As a specific example, if a user asks "Why is the sum of the interior angles of a triangle 180 degrees?" the server sends the following prompt to the AI ​​model to generate an answer.

[0793] Input: Why the sum of the interior angles of a triangle is 180 degrees

[0794] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[0795] Online classes

[0796] Users can use the application to reserve online individual lessons or online training camps. The server manages reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link so the user can join the class.

[0797] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0798] Step 1: Gather information

[0799] The server automatically collects test-taking experience information and reviews from major test-taking blogs and review sites on the Internet. This process involves periodically accessing specific websites and obtaining data in HTML or JSON format. Specifically, data is extracted using scraping tools and APIs. The input is a URL or search query, and the output is the collected raw data.

[0800] Step 2: Information analysis

[0801] The server analyzes the information collected in step 1 using natural language processing technology. It uses Python's SpaCy and Transformers libraries to extract useful information from the text data and score its reliability. The input is raw data, and the output is analyzed information and its reliability score. Specifically, it performs text summarization, keyword extraction, sentiment analysis, etc.

[0802] Step 3: Enter user information

[0803] The user launches the application and enters information about their child's learning style and characteristics, including attention span, preferred learning methods, and strong and weak subjects. The input is the data provided in each information form, and the output is structured user information that the device sends to the server.

[0804] Step 4: Recommending materials

[0805] The server performs analysis based on the user information acquired in step 3 to recommend optimal learning materials and reference books. It analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of appropriate learning materials for the user. The input is user information, and the output is a list of recommended learning materials. Specifically, it selects the most appropriate learning materials based on past data and the AI ​​model.

[0806] Step 5: Create a study schedule

[0807] The user inputs their current learning progress and exam date. The device sends this information to the server. The server generates a customized learning schedule based on this data and periodically updates and adjusts it. The input is learning progress data and exam date, and the output is the learning schedule. Specifically, it generates a Gantt chart and allocates daily learning tasks.

[0808] Step 6: Question-answering

[0809] During learning, users enter questions they don't understand into a question form within the application. This question is sent from the device to the server. The server uses a generative AI model (such as GPT-3) to generate the optimal answer and explanation for the question. The input is the user's question, and the output is the generated answer and explanation. Specifically, it generates an appropriate answer for the following prompt:

[0810] Input: Why the sum of the interior angles of a triangle is 180 degrees

[0811] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[0812] Step 7: Online classes

[0813] Users use the application to book online individual lessons or training camp classes. The server manages the user's reservation information and sends a reminder notification and a participation link to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link, allowing the user to join the class. The input is the class reservation date and time and user information, and the output is a reminder notification and a participation link. Specifically, the calendar API is used to set up notifications and generate a link for the video conferencing system.

[0814] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0815] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[0816] System Configuration

[0817] 1. Information collection module

[0818] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[0819] 2. Information Analysis Module

[0820] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[0821] 3. User information input module

[0822] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[0823] 4. Teaching material recommendation module

[0824] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[0825] 5. Study Schedule Generation Module

[0826] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[0827] 6. Question Answering Module

[0828] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[0829] 7. Online Class Module

[0830] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[0831] 8. Emotion Recognition Engine

[0832] The server is equipped with an emotion recognition engine that recognizes the user's emotions and analyzes the user's emotional state during learning in real time.

[0833] Program processing explanation

[0834] Information collection and analysis

[0835] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[0836] Enter and submit user information

[0837] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[0838] Recommended teaching materials

[0839] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[0840] Generate a study schedule

[0841] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[0842] Question and Answering

[0843] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[0844] Online classes

[0845] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[0846] How the emotion recognition engine works

[0847] The server analyzes the user's facial expressions and tone of voice captured through the camera and microphone, and evaluates the user's emotional state in real time. The emotion recognition engine determines the user's state, such as "concentrated," "tired," or "stressed."

[0848] 1. Collecting Emotional Data

[0849] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[0850] 2. Emotion analysis

[0851] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[0852] 3. Feedback and Adjustments

[0853] The server adjusts the learning content and schedule based on the user's emotional state. For example, if the server recognizes that the user is tired, it will suggest that the user take a break. If the user is concentrating, it will give feedback to continue learning.

[0854] 4. Sending encouraging messages

[0855] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device. For example, if the user has been concentrating for a long time, a positive message such as "Keep it up!" will be displayed.

[0856] Specific examples

[0857] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0858] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[0859] 2. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable teaching material, and sends this to the device.

[0860] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[0861] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0862] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[0863] 6. While learning, the device captures the user's facial expressions and voice using a camera and microphone and sends them to the server. The server's emotion recognition engine determines that the user is "concentrating" and instructs the user to continue with the recommended learning material.

[0864] 7. If the user begins to feel tired while studying, the emotion recognition engine will determine that they are tired, and the server will display a message on the device saying, "Take a break."

[0865] In this way, by combining an emotion recognition engine, flexible support according to the user's condition becomes possible, maximizing learning efficiency and effectiveness.

[0866] The processing flow will be explained below.

[0867] Information collection and analysis

[0868] Step 1:

[0869] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[0870] Step 2:

[0871] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[0872] Step 3:

[0873] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[0874] Step 4:

[0875] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[0876] Step 5:

[0877] The server stores the analysis results and reliability scores in a database.

[0878] Enter and submit user information

[0879] Step 1:

[0880] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[0881] Step 2:

[0882] The terminal converts the input user information into a data package and transmits it to the server.

[0883] Step 3:

[0884] The server passes the received user information to the analysis module and starts the analysis.

[0885] Recommended teaching materials

[0886] Step 1:

[0887] The server uses an AI model to analyze the received user information.

[0888] Step 2:

[0889] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[0890] Step 3:

[0891] The server transmits the listed educational material information to the terminal.

[0892] Step 4:

[0893] The terminal displays the transmitted educational material information to the user.

[0894] Generate a study schedule

[0895] Step 1:

[0896] The user enters their current learning progress and target exam date into the application.

[0897] Step 2:

[0898] The terminal transmits the input data to the server.

[0899] Step 3:

[0900] The server operates a study schedule generation module based on the received study progress data and target exam date.

[0901] Step 4:

[0902] The server transmits the generated study schedule to the terminal.

[0903] Step 5:

[0904] The terminal displays the schedule to the user and periodically sends reminder notifications.

[0905] Question and Answering

[0906] Step 1:

[0907] Users enter questions they have about their studies into the application's question form.

[0908] Step 2:

[0909] The terminal converts the entered question into a data package and sends it to the server.

[0910] Step 3:

[0911] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[0912] Step 4:

[0913] The server sends the generated answers and explanations to the terminal.

[0914] Step 5:

[0915] The terminal displays the answer to the user.

[0916] Online classes

[0917] Step 1:

[0918] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[0919] Step 2:

[0920] The terminal transmits the reservation information to the server.

[0921] Step 3:

[0922] The server stores this reservation information in a database and sets a reminder notification before the class date.

[0923] Step 4:

[0924] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[0925] Step 5:

[0926] The terminal displays a notification to the user and provides a join link.

[0927] Step 6:

[0928] The user clicks on the class participation link in the reminder notification to participate in the online class.

[0929] How the emotion recognition engine works

[0930] Step 1:

[0931] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[0932] Step 2:

[0933] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[0934] Step 3:

[0935] The server adjusts learning content and schedules based on the user's emotional state. For example, if the server recognizes that the user is tired, it will display a suggestion such as "Take a break."

[0936] Step 4:

[0937] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device.

[0938] Step 5:

[0939] The device will then display the message to the user, for example, if they have been concentrating for a long time, it will display a positive message such as "Keep it up!"

[0940] Example 2

[0941] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0942] Current online learning support systems lack personalization based on learners' characteristics and progress, making it difficult to provide optimal learning content and schedules for individual learners. Furthermore, they are unable to assess learners' emotional states in real time and provide appropriate feedback, resulting in insufficient support when learners become tired or lose concentration. This can lead to reduced learning effectiveness.

[0943] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0944] In this invention, the server includes means for collecting information from word-of-mouth information and test-taking experiences across the country, means for analyzing the collected information and aggregating useful information, means for inputting and analyzing the characteristics of individual learners, means for recommending learning materials and reference books suitable for each learner based on the analysis results, means for generating test-taking preparation schedules according to the learner's progress, means for automatically answering questions from learners, means for offering individual lessons and training camps online and providing a place for learners to interact with each other, and means for evaluating the learner's emotional state in real time and providing feedback and support. This makes it possible to provide optimal learning content and schedules based on the learner's characteristics and emotional state, maximizing the learner's learning efficiency and effectiveness.

[0945] "Means for collecting information" refers to modules or software for obtaining data from online exam blogs and review sites.

[0946] "Means for analyzing information" refers to a module or software that evaluates acquired data using natural language processing technology and extracts useful information.

[0947] The "means for inputting and analyzing characteristics" refers to a module or software for inputting characteristic information such as a learner's learning style, strong subjects, and weak subjects into a server and analyzing this information.

[0948] "Means for recommending learning materials and reference books" refers to a module or software that selects and recommends the most suitable learning materials and reference books to learners based on the analysis results.

[0949] The "means for generating an exam preparation schedule" is a module or software for automatically generating an efficient study schedule based on the learner's progress information and exam date.

[0950] An "automatic answering means" is a module or software that uses an artificial intelligence model to generate and provide appropriate answers and explanations to questions posted by learners.

[0951] "Means for providing individual lessons and training camps" means modules or software for managing and providing individual lessons and training camps that can be attended by learners online.

[0952] The "means for assessing the learner's emotional state in real time" is a module or software for analyzing data acquired through a camera or microphone and determining the learner's emotional state in real time.

[0953] "Means for providing feedback and support" refers to a module or software that automatically provides appropriate encouraging messages and adjustments to learning content according to the learner's emotional state.

[0954] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[0955] System Configuration

[0956] This system mainly consists of the following modules and hardware components.

[0957] 1. Information collection module

[0958] The server periodically collects online reviews and test-taking experiences using Python and the Beautiful Soup library, and stores the collected data in a database in CSV or JSON format.

[0959] 2. Information Analysis Module

[0960] The server analyzes the collected data using natural language processing tools (e.g., NLTK or SpaCy), and generates a reliability score and categorizes the results.

[0961] 3. User information input module

[0962] The device sends the learner's characteristics (learning style, strong and weak subjects, etc.) entered by the user to the server using the HTTPS protocol.

[0963] 4. Teaching material recommendation module

[0964] The server analyzes user information using AI models such as TensorFlow and PyTorch, and based on the analysis results, it creates a list of learning materials and reference books suitable for each learner and sends them to the device.

[0965] 5. Study Schedule Generation Module

[0966] The server generates an efficient study schedule based on the learner's progress data and target date (exam date). The generated schedule is updated in real time and sent to the device.

[0967] 6. Question Answering Module

[0968] The device sends the user-entered question to the server, which uses BERT or GPT models to generate an answer and explanation for the question and sends it back to the device.

[0969] 7. Online Class Module

[0970] The server manages the online individual lessons and camp lessons booked by users, provides links to join the lessons, and also sends reminders before the lessons start.

[0971] 8. Emotion Recognition Engine

[0972] The server analyzes the user's facial expressions and tone of voice captured through a camera and microphone, and evaluates the user's emotional state in real time.

[0973] Specific examples

[0974] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[0975] After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the application to the server. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable learning material, and sends this to the device. Alternatively, if the child enters "Why is the sum of the interior angles of a triangle 180 degrees?" into a question form, the content is sent from the device to the server. The server generates an answer using an AI model, explaining that "The sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[0976] Users can also reserve online individual lessons for the next month and receive reminder notifications from the server to ensure they don't miss them. They then click the class participation link at the specified time to attend the class. While studying, the device uses a camera and microphone to capture the user's facial expressions and voice and send them to the server. The server's emotion recognition engine determines that the user is "concentrating" and instructs the user to continue with the recommended learning materials. If the user begins to feel tired while studying, the emotion recognition engine determines that the user is "tired," and the server displays a message on the device saying, "Take a break."

[0977] Prompt Sentence Examples

[0978] As an example of a prompt, the following prompt is entered:

[0979] "Why does the sum of the interior angles of a triangle equal 180 degrees?"

[0980] "Please let me know the best study guide for exam preparation."

[0981] "Please tell me how to help my child concentrate on their studies."

[0982] The present invention allows users to be provided with optimal learning content and schedules based on the learner's characteristics and emotional state, thereby maximizing learning efficiency and effectiveness.

[0983] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0984] Step 1: Gather information

[0985] The server periodically collects data from online test-taking blogs and review sites using Python and the Beautiful Soup library. Specifically, the server creates a list of URLs and parses the HTML to extract text data. The input is the list of URLs to be collected, and the output is to save the extracted data in CSV or JSON format.

[0986] Step 2: Information analysis

[0987] The server analyzes the collected data using natural language processing tools (NLTK and SpaCy). Specifically, it filters unnecessary information from the text and calculates a reliability score. It also classifies the data into categories and extracts useful information. The input is the collected raw data, and the output is the reliability score and the data classified by category.

[0988] Step 3: Enter and submit user information

[0989] The user inputs learner characteristics (learning style, strong and weak subjects, etc.) through the application. The input information is sent to the server via the terminal using the HTTPS protocol. The input is the learner's characteristic data, and the output is the data sent to the server.

[0990] Step 4: Recommending materials

[0991] The server analyzes the received user information using an AI model such as TensorFlow or PyTorch. Specifically, the AI ​​model receives the learner's characteristics as input and lists the most suitable learning materials and reference books. The input is the learner's characteristic data, and the output is a list of recommended learning materials. The recommended list is presented to the user via their device.

[0992] Step 5: Generate a study schedule

[0993] The user inputs their current study progress and exam dates into the application. This information is sent to the server via the terminal. The server uses a Python scheduling library to generate an efficient study schedule. The input is study progress data and exam dates, and the output is the generated study schedule. The schedule is updated in real time and provided to the user via the terminal.

[0994] Step 6: Question-answering

[0995] Users enter questions they don't understand during their studies into a question form within the application. This question is sent to the server via the device. The server uses BERT or GPT models to generate an answer and explanation for the question. The input is the question, and the output is the answer and explanation. The answer is displayed to the user via the device.

[0996] Step 7: Book and attend online classes

[0997] A user reserves an online class using an application. The reservation information is sent to the server via the device. The server manages the reservation information and sends a reminder notification and a participation link to the device before the class starts. The input is the reservation information, and the output is the reminder notification and the participation link. When the class time arrives, the device sends a notification to the user, who clicks the participation link to join the class.

[0998] Step 8: Emotion Recognition

[0999] During learning, the device uses a camera and microphone to capture the user's facial expressions and voice. This data is sent to the server in real time. The server's emotion recognition engine analyzes this data and identifies the user's emotional state. The input is the captured facial and voice data, and the output is the user's emotional state. For example, states such as "concentrated" or "tired" are determined. Based on the emotional state, the server generates appropriate feedback or encouraging messages and sends them to the device.

[1000] (Application example 2)

[1001] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1002] Conventional online learning support systems have the problem of being unable to provide flexible support that responds to the learner's individual emotional state, preventing them from maximizing learning efficiency. The present invention aims to solve these problems and enable appropriate learning content and schedule adjustments based on the learner's emotional state. It also aims to increase the learner's persistence in learning by providing encouraging messages that maintain the learner's motivation and reduce fatigue and stress.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1004] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test-taking preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and providing a place for learners to interact with each other; means for recognizing the user's emotional state in real time and adjusting the learning content and schedule based on that state; and means for automatically generating and presenting encouraging messages based on the emotional state. This makes it possible to adjust the learning content and schedule appropriately according to the learner's emotional state, thereby improving learning efficiency and sustainability.

[1005] "Means of collecting information" refers to a function for collecting information from word-of-mouth information and test-taking experiences across the country.

[1006] "Means for analyzing information and aggregating useful information" refers to the function of analyzing collected information, selecting useful information, and aggregating it.

[1007] "Means for inputting and analyzing the characteristics of individual learners" is a function that inputs learner characteristics (learning style, strong and weak subjects, etc.) and analyzes them.

[1008] "Means for recommending teaching materials and reference books suitable for each learner" is a function that suggests the most suitable teaching materials and reference books for each learner based on the analysis results.

[1009] The "means for generating an exam preparation schedule" is a function that creates a study schedule necessary for exams based on the learner's progress.

[1010] "Means for automatically answering questions from learners" is a function that uses an AI model to automatically provide answers to questions submitted by learners.

[1011] "A means of offering individual lessons and training camps online, and providing a place for learners to interact with each other" refers to a function that holds individual lessons and group lessons online, and provides a place for learners to communicate with each other.

[1012] "Means for recognizing the user's emotional state in real time and adjusting the learning content and schedule based on that state" refers to a function that analyzes the user's emotional state in real time through a camera or microphone and flexibly changes the learning content and schedule based on the results.

[1013] The "means for automatically generating and presenting an encouraging message based on the emotional state" is a function for automatically creating and displaying an encouraging message in accordance with the emotional state of the user.

[1014] The present invention provides an online learning support system that recognizes the emotional state of a learner and adjusts the learning content and schedule based on that state. Specific embodiments of the present invention are described below.

[1015] System Overview

[1016] This system is composed of a server, terminals, and user interactions. The main modules include an information collection module, an information analysis module, a user information input module, a learning material recommendation module, a study schedule generation module, a question-answering module, an online class module, and an emotion recognition engine.

[1017] Hardware and Software

[1018] Hardware:

[1019] Smart glasses (with built-in camera and microphone)

[1020] server

[1021] User device (smartphone or tablet)

[1022] software:

[1023] Python programming language

[1024] OpenCV (image processing library)

[1025] DeepFace (emotion recognition library)

[1026] Playsound (audio playback library)

[1027] Program processing explanation

[1028] Information Collection Module

[1029] The server collects data in real time from major online blogs and review sites for entrance exams. This information is collected using web scraping technology. The collected data is then stored in a database for later analysis.

[1030] Information Analysis Module

[1031] The server analyzes the collected word-of-mouth information and test-taking experiences using natural language processing (NLP). Specifically, it uses text mining tools to extract useful information. It also evaluates the reliability of the information and extracts only valid data.

[1032] User information input module

[1033] Learners enter their characteristics, learning style, and current progress through their terminals. This information is sent to the server and used as the basis for analysis.

[1034] Material recommendation module

[1035] Based on the learner information sent, the server uses an AI model to recommend the most suitable learning materials and reference books. This recommendation list is sent to the user's device, allowing the user to proceed with their studies accordingly.

[1036] Study schedule generation module

[1037] The server generates an efficient study schedule taking into account the learner's progress and target date and time (e.g., exam date). The generated schedule is periodically updated and notified to the user's terminal.

[1038] Question and Answer Module

[1039] When users enter questions that arise during their studies into the application, the data is sent to the server, which uses an AI model to generate answers and detailed explanations and send them back to the user's device.

[1040] Online Class Module

[1041] Users can book individual or group lessons through the application. The server manages the reservation information and sends reminders and participation links before the lesson.

[1042] Emotion Recognition Engine

[1043] The server analyzes the user's facial expressions and voice in real time, captured through the smart glasses' camera and microphone, to assess the user's emotional state. The analysis uses the DeepFace library and performs the following steps:

[1044] Capture facial and voice data

[1045] Emotional state analysis

[1046] Adjusting learning content and schedules based on emotional state

[1047] Automatically generate and display encouraging messages

[1048] Specific examples

[1049] Consider a scenario where a user is preparing for a junior high school entrance exam. The user inputs their learning style information and receives recommended study materials, such as "math workbooks." While studying, if the user inputs a question about the sum of the interior angles of a triangle, the AI ​​model generates and notifies the user with an answer, such as "The sum of the interior angles of a triangle is 180 degrees." The smart glasses monitor the learner's level of concentration, prompting them to continue studying if they are focused, or encouraging them to take a break if they are tired.

[1050] Prompt Sentence Examples

[1051] "Generate positive messages to encourage stressed workers to take breaks."

[1052] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1053] Step 1:

[1054] Data collection by information collection module

[1055] The server collects data in real time from online exam blogs and review sites, specifically using web scraping technology to obtain exam-related information and store that data in a database.

[1056] Input: URL of an online exam blog or review site

[1057] Data processing: Extracting information through web scraping

[1058] Output: Collected exam information database

[1059] Step 2:

[1060] Data analysis using the information analysis module

[1061] The server analyzes the collected reviews and test-taking experiences using natural language processing (NLP) technology. Specifically, it uses text mining tools to extract useful information and evaluate its reliability.

[1062] Input: Collected exam information database

[1063] Data Computing: Text Analysis and Trust Assessment with NLP

[1064] Output: A database of useful analyzed information

[1065] Step 3:

[1066] Collecting characteristic information using the user information input module

[1067] Users use a smartphone or tablet to input information about their learner characteristics (such as their learning style, favorite subjects, and progress). The input information is then sent from the device to the server.

[1068] Input: Learner characteristics information

[1069] Data processing: Sending to the server

[1070] Output: Learner characteristics data stored on the server

[1071] Step 4:

[1072] Recommending materials using the material recommendation module

[1073] The server uses an AI model to recommend optimal learning materials and reference books based on the learner's characteristic data, and notifies the user of the generated recommendation list.

[1074] Input: Learner characteristics data, useful information database

[1075] Data calculation: Calculating optimal teaching materials using AI models

[1076] Output: Recommended learning materials list

[1077] Step 5:

[1078] Schedule creation using the study schedule generation module

[1079] The server generates an efficient study schedule based on the learner's progress and target date and time (e.g., exam date). The generated schedule is updated periodically and notified to the user's device.

[1080] Input: Learner progress, target date and time

[1081] Data Computation: Generating Efficient Schedules

[1082] Output: Study schedule

[1083] Step 6:

[1084] Answering questions using the question answering module

[1085] When a user has a question that arises during their study, they input it into their device and the data is sent to the server, which uses an AI model to generate an answer and detailed explanation, which is then sent back to the user's device.

[1086] Input: Question data from the user

[1087] Data Computation: Answer Generation with AI Models

[1088] Output: Answer and detailed explanation

[1089] Step 7:

[1090] Offering classes through online lesson modules

[1091] Users make reservations for individual or group lessons through the application. The server manages the reservation information and sends reminders and participation links before the lesson.

[1092] Input: Class reservation information

[1093] Data management: Reservation information management

[1094] Output: Reminder notification, participation link

[1095] Step 8:

[1096] Emotion analysis using an emotion recognition engine

[1097] The server analyzes the user's facial expressions and voice in real time, captured through the smart glasses' camera and microphone, and evaluates the user's emotional state. Based on the analysis results, the server adjusts the learning content and schedule and provides appropriate feedback.

[1098] Input: User's facial expression data, voice data

[1099] Data Computation: Emotion Analysis with DeepFace

[1100] Output: Adjustment of learning content and schedule based on emotional state, encouraging messages

[1101] Example processing steps

[1102] While the user is studying, the smart glasses analyze the user's facial expression and determine that the user is "highly stressed." In this case, the server automatically generates a message encouraging the user to "take a break" and notifies the user by voice and text. An example of a prompt sentence is "Please generate a positive message to suggest that highly stressed workers take a break."

[1103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1105] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1106] [Third embodiment]

[1107] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1111] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1115] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1117] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1118] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1119] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system includes the following components and functions:

[1120] System Configuration

[1121] 1. Information collection module

[1122] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[1123] 2. Information Analysis Module

[1124] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[1125] 3. User information input module

[1126] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[1127] 4. Teaching material recommendation module

[1128] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[1129] 5. Study Schedule Generation Module

[1130] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[1131] 6. Question Answering Module

[1132] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[1133] 7. Online Class Module

[1134] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[1135] Program processing explanation

[1136] Information collection and analysis

[1137] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[1138] Enter and submit user information

[1139] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[1140] Recommended teaching materials

[1141] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[1142] Generate a study schedule

[1143] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1144] Question and Answering

[1145] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[1146] Online classes

[1147] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[1148] Specific examples

[1149] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1150] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[1151] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[1152] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[1153] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[1154] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[1155] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[1156] The processing flow will be explained below.

[1157] Information collection and analysis

[1158] Step 1:

[1159] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[1160] Step 2:

[1161] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[1162] Step 3:

[1163] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[1164] Step 4:

[1165] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[1166] Step 5:

[1167] The server stores the analysis results and reliability scores in a database.

[1168] Enter and submit user information

[1169] Step 1:

[1170] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[1171] Step 2:

[1172] The terminal converts the input user information into a data package and transmits it to the server.

[1173] Step 3:

[1174] The server passes the received user information to the analysis module and starts the analysis.

[1175] Recommended teaching materials

[1176] Step 1:

[1177] The server uses an AI model to analyze the received user information.

[1178] Step 2:

[1179] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[1180] Step 3:

[1181] The server transmits the listed educational material information to the terminal.

[1182] Step 4:

[1183] The terminal displays the transmitted educational material information to the user.

[1184] Generate a study schedule

[1185] Step 1:

[1186] The user enters their current learning progress and target exam date into the application.

[1187] Step 2:

[1188] The terminal transmits the input data to the server.

[1189] Step 3:

[1190] The server operates a study schedule generation module based on the received study progress data and target exam date.

[1191] Step 4:

[1192] The server transmits the generated study schedule to the terminal.

[1193] Step 5:

[1194] The terminal displays the schedule to the user and periodically sends reminder notifications.

[1195] Question and Answering

[1196] Step 1:

[1197] Users enter questions they have about their studies into the application's question form.

[1198] Step 2:

[1199] The terminal converts the entered question into a data package and sends it to the server.

[1200] Step 3:

[1201] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[1202] Step 4:

[1203] The server sends the generated answers and explanations to the terminal.

[1204] Step 5:

[1205] The terminal displays the answer to the user.

[1206] Online classes

[1207] Step 1:

[1208] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[1209] Step 2:

[1210] The terminal transmits the reservation information to the server.

[1211] Step 3:

[1212] The server stores this reservation information in a database and sets a reminder notification before the class date.

[1213] Step 4:

[1214] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[1215] Step 5:

[1216] The terminal displays a notification to the user and provides a join link.

[1217] Step 6:

[1218] The user clicks on the class participation link in the reminder notification to participate in the online class.

[1219] Example 1

[1220] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1221] Conventional learning support systems have difficulty in recommending learning plans and materials that meet the individual needs of learners, and in managing progress, resulting in the inability to provide an effective learning environment. Furthermore, the reliability of exam information and prompt responses to learners' questions are also insufficient. This places a heavy burden on test takers and their families.

[1222] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1223] In this invention, the server includes: means for collecting information from nationwide assessment information and test experience stories; means for analyzing the collected information using natural language processing technology and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the generated analysis results; means for generating an effective study schedule based on the learner's learning progress data and target dates and times; means for automatically answering questions from learners using a large-scale language model; and means for providing online individual lessons and training camps and for providing a place where learners can interact with each other. This enables efficient and personalized study support and significantly reduces the burden on test takers and their families.

[1224] "National evaluation information" refers to information posted on the Internet based on test evaluations and test-taking experiences.

[1225] "Exam Experiences" are detailed reports and impressions about the exam written by test takers based on their own experiences.

[1226] "Natural language processing technology" is an artificial intelligence technology for understanding, analyzing, and generating human language.

[1227] A "large-scale language model" is an artificial intelligence model trained using massive amounts of text data, and is a technology that has the ability to understand and generate sentences like humans (e.g., GPT-4).

[1228] "Characteristics of individual learners" refers to information about characteristics related to individual learning, such as each learner's learning style, strong and weak subjects, and learning progress.

[1229] "Teaching materials and reference materials" refers to educational resources such as textbooks, workbooks, online courses, and video materials that learners use to advance their studies.

[1230] "Study progress data" is information that indicates how far a learner has progressed in their studies, and includes the content learned, the level of achievement, and the amount of time spent studying.

[1231] An "effective study schedule" is a study plan that is optimized based on the learner's goals and progress.

[1232] "Online private lessons and training camps" are private instruction and special group lessons provided via the Internet.

[1233] A "place where learners can interact" is an online platform where learners can communicate with each other, exchange information, and engage in collaborative learning.

[1234] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system is implemented as follows.

[1235] System Configuration

[1236] The system consists of the following hardware and software elements:

[1237] Server: A central management system that collects, analyzes, stores, and analyzes data using AI models.

[1238] Device: The device used by the user (computer, tablet, smartphone, etc.).

[1239] Generative AI models (e.g., GPT-4, BERT, etc.): Used to analyze collected data and user input information and provide optimal learning resources.

[1240] Program processing explanation

[1241] Information collection and analysis

[1242] The server collects data from major exam blogs and review sites on the Internet. This process uses web scraping tools such as Python's Beautiful Soup and Scrapy. The collected data is analyzed using natural language processing techniques (e.g., Google's BERT model) and a reliability score is applied. The school name, study methods, and key points about exam preparation for each post are then stored in a database.

[1243] Enter and submit user information

[1244] Users launch the application, log in, and enter their child's learning style and characteristics (e.g., attention span, preferred learning methods). The device then sends this information to the server. Communication is via the HTTPS protocol.

[1245] Recommended teaching materials

[1246] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and creates a list of learning materials and reference books that are best suited to each learner. This list is sent to the device and presented to the user. For example, the server may recommend the "Math Masters" YouTube channel.

[1247] Generate a study schedule

[1248] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1249] Question and Answering

[1250] When a user has a question they don't understand while studying, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[1251] Online classes

[1252] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[1253] Examples of concrete examples and prompts

[1254] Specific examples

[1255] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1256] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[1257] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[1258] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[1259] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[1260] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[1261] Prompt Sentence Examples

[1262] "How can I effectively manage my child's learning style?"

[1263] "Please explain why the sum of the interior angles of the following triangle is 180 degrees."

[1264] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[1265] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1266] Step 1: Start gathering information

[1267] The server accesses designated exam blogs and review sites at specific time intervals. A list of URLs of the sites to be collected is used as input. HTML data obtained from each site is obtained as output. Specifically, the data is collected using web scraping tools such as Python's Beautiful Soup and Scrapy.

[1268] Step 2: Data collection

[1269] The server analyzes the collected HTML data and extracts information related to the exam. The collected HTML data is used as input. Exam information (school name, study methods, exam preparation information, etc.) is obtained as output. Specifically, the server parses the HTML data to obtain the necessary text information and stores it in a database.

[1270] Step 3: Data analysis

[1271] The server analyzes the extracted exam information using natural language processing technology (e.g., Google's BERT model). The extracted exam information is used as input. The analyzed information and a reliability score are obtained as output. Specifically, the text information is input into the BERT model, which performs semantic analysis of the information and reliability evaluation.

[1272] Step 4: Reliability Scoring

[1273] The server scores the credibility of each post based on the analysis results and assigns a tag to each one. The analyzed information and the results of the credibility assessment are used as input. The output is the test information with a credibility score. The specific operation is to add the credibility score to each entry in the database.

[1274] Step 5: Enter user information

[1275] The user launches the application, logs in, and enters information about their child's learning style and characteristics. The learning style information entered by the user is used as input. This information is sent from the device to the server as output. The specific operation requires the user to enter information into a form and press the submit button.

[1276] Step 6: Send user information

[1277] The terminal sends the entered user information to the server. As input, the learning style information entered by the user is used. As output, this information is sent to the server. As a specific operation, data is sent securely using the HTTPS protocol.

[1278] Step 7: Recommending materials

[1279] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and lists the most suitable learning materials and reference books. The user information is used as input. The output is a list of recommended learning materials and reference books. Specifically, the server inputs the user information into the AI ​​model and stores the generated recommendation list in a database.

[1280] Step 8: Submit your recommendation list

[1281] The server sends the generated teaching material recommendation list to the terminal. The generated recommendation list is used as input. This list is sent to the terminal as output. As a specific operation, the recommendation list is sent to the terminal via HTTPS protocol.

[1282] Step 9: Generate a study schedule

[1283] The server generates an efficient study schedule based on the study progress data and exam dates. The study progress data and exam dates are used as input. The generated study schedule is obtained as output. Specifically, the server calculates the optimal schedule using a scheduling algorithm (e.g., linear programming).

[1284] Step 10: Schedule Sending

[1285] The server sends the generated learning schedule to the terminal. The generated learning schedule is used as input. This schedule is sent to the terminal as output. As a specific operation, the learning schedule is sent to the terminal via the HTTPS protocol.

[1286] Step 11: Question Answering

[1287] When a user is studying, they enter a question they don't understand into a question form within the application. The question entered by the user is used as input. The question is sent from the device to the server as output. The specific operation requires the user to enter a question into the form and press the send button.

[1288] Step 12: Submit your question

[1289] The terminal sends the entered question to the server. The question entered by the user is used as input. The question is sent to the server as output. Specifically, the data is sent securely using the HTTPS protocol.

[1290] Step 13: Answer Generation

[1291] The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question. The user's question is used as input. The generated answer and explanation are obtained as output. Specifically, the question is input into the AI ​​model, and the generated answer is stored in a database.

[1292] Step 14: Submit your answers

[1293] The server sends the generated answer to the terminal. The generated answer is used as input. This answer is sent to the terminal as output. As a specific operation, the answer is sent to the terminal via the HTTPS protocol.

[1294] Step 15: Book an online class

[1295] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The lesson information reserved by the user is used as input. This reservation information is sent from the terminal to the server as output. The specific operation requires the user to select a class and press the reservation button.

[1296] Step 16: Reservation Information Management

[1297] The server saves and manages reservation information in a database. The user's reservation information is used as input. The saved reservation information is obtained as output. Specific operations include saving the reservation information in the database and displaying it on the management screen.

[1298] Step 17: Send reminder notifications

[1299] The server sends a reminder notification to the terminal before the start of the class. The reservation information and the start time of the class are used as input. The reminder notification is sent to the terminal as output. Specifically, the server generates a notification before the start time of the class and sends it to the terminal via HTTPS protocol.

[1300] Step 18: Send class participation link

[1301] The server sends a participation link to the terminal just before the class starts. The class reservation information and participation link are used as input. The participation link is sent to the terminal as output. Specifically, the participation link is generated and sent to the terminal via the HTTPS protocol.

[1302] (Application example 1)

[1303] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1304] The diverse learning environments required for junior high school entrance exams place a significant burden on students and their families. However, finding the optimal learning methods and materials for each individual student is not easy and requires time and effort. Students also need to be able to quickly and accurately respond to any questions they may have. Furthermore, because it is difficult to maximize the effectiveness of online classes and self-study tools, a system that can solve all of these issues at once is needed.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1306] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and a place for learners to interact with each other; means for generating optimal answers using a generative AI model based on the learner's learning data; and means for providing answer prompts generated by the AI ​​model. This makes it possible to efficiently and effectively manage learner progress and quickly resolve learner questions.

[1307] "Word of mouth" refers to the sharing of opinions and experiences published by individuals or groups on the Internet.

[1308] "Exam experience stories" are records of the experiences of test takers and their families in past exams, including the preparation process and results.

[1309] "Information gathering means" refers to devices or programs that have the function of automatically obtaining word-of-mouth information and test-taking experience stories from the Internet.

[1310] "Information analysis means" refers to technologies and programs for analyzing collected information and evaluating its usefulness and reliability.

[1311] "User information input means" refers to a device or interface for inputting learner characteristic information (for example, learning style, strong subjects, weak subjects, etc.).

[1312] "Materials recommendation methods" refer to technologies and algorithms that select and recommend the most appropriate materials and reference books for each learner based on collected and analyzed information.

[1313] "Study schedule generation means" refers to a device or program that creates an efficient study schedule based on the learner's progress data and target schedule.

[1314] A "question-answering tool" is a device or program that accepts questions from learners and automatically generates answers using technology such as AI.

[1315] "Online class delivery means" refers to a system or interface for providing and facilitating individual lessons and training camps via the Internet.

[1316] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to generate and provide optimal solutions to specific tasks.

[1317] A "prompt" is a sentence or phrase that serves as a question or instruction to be input into a generative AI model.

[1318] This invention is an online learning support system that reduces the burden on junior high school entrance exam students and their families. This system includes modules for information collection, information analysis, user information input, teaching material recommendation, study schedule generation, question answering, and online classes.

[1319] System Configuration

[1320] Information collection and analysis

[1321] The server periodically collects user reviews and test-taking experiences from major online test-taking blogs and review sites. This data is analyzed using natural language processing technology and a reliability score is assigned. The analysis results are stored in a database that includes the usefulness of each post and detailed information (e.g., school name, study method, test preparation). The system primarily uses the Python programming language, with SpaCy and Transformers (Hugging Face) as natural language processing libraries.

[1322] Enter and submit user information

[1323] After launching the application, users enter information about their child's learning style and characteristics, including attention span, preferred learning methods, and favorite and least favorite subjects, on their smartphone screen. The entered data is then sent from the device to a server.

[1324] Recommended teaching materials

[1325] The server selects the most suitable learning materials and reference books for each user based on the information received from the user. During this process, it analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of recommended learning materials. The generated list is sent to the device and provided to the user.

[1326] Generate a study schedule

[1327] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate a customized learning schedule for each user, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1328] Question and Answering

[1329] When a user is studying, they enter a question they don't understand into the question form within the application. The question is sent from the device to the server. The server uses a generative AI model to generate the optimal answer and explanation for the question and sends it back to the device. The user can check the answer in real time and continue studying. As a specific example, if a user asks "Why is the sum of the interior angles of a triangle 180 degrees?" the server sends the following prompt to the AI ​​model to generate an answer.

[1330] Input: Why the sum of the interior angles of a triangle is 180 degrees

[1331] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[1332] Online classes

[1333] Users can use the application to reserve online individual lessons or online training camps. The server manages reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link so the user can join the class.

[1334] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1335] Step 1: Gather information

[1336] The server automatically collects test-taking experience information and reviews from major test-taking blogs and review sites on the Internet. This process involves periodically accessing specific websites and obtaining data in HTML or JSON format. Specifically, data is extracted using scraping tools and APIs. The input is a URL or search query, and the output is the collected raw data.

[1337] Step 2: Information analysis

[1338] The server analyzes the information collected in step 1 using natural language processing technology. It uses Python's SpaCy and Transformers libraries to extract useful information from the text data and score its reliability. The input is raw data, and the output is analyzed information and its reliability score. Specifically, it performs text summarization, keyword extraction, sentiment analysis, etc.

[1339] Step 3: Enter user information

[1340] The user launches the application and enters information about their child's learning style and characteristics, including attention span, preferred learning methods, and strong and weak subjects. The input is the data provided in each information form, and the output is structured user information that the device sends to the server.

[1341] Step 4: Recommending materials

[1342] The server performs analysis based on the user information acquired in step 3 to recommend optimal learning materials and reference books. It analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of appropriate learning materials for the user. The input is user information, and the output is a list of recommended learning materials. Specifically, it selects the most appropriate learning materials based on past data and the AI ​​model.

[1343] Step 5: Create a study schedule

[1344] The user inputs their current learning progress and exam date. The device sends this information to the server. The server generates a customized learning schedule based on this data and periodically updates and adjusts it. The input is learning progress data and exam date, and the output is the learning schedule. Specifically, it generates a Gantt chart and allocates daily learning tasks.

[1345] Step 6: Question-answering

[1346] During learning, users enter questions they don't understand into a question form within the application. This question is sent from the device to the server. The server uses a generative AI model (such as GPT-3) to generate the optimal answer and explanation for the question. The input is the user's question, and the output is the generated answer and explanation. Specifically, it generates an appropriate answer for the following prompt:

[1347] Input: Why the sum of the interior angles of a triangle is 180 degrees

[1348] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[1349] Step 7: Online classes

[1350] Users use the application to book online individual lessons or training camp classes. The server manages the user's reservation information and sends a reminder notification and a participation link to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link, allowing the user to join the class. The input is the class reservation date and time and user information, and the output is a reminder notification and a participation link. Specifically, the calendar API is used to set up notifications and generate a link for the video conferencing system.

[1351] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1352] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[1353] System Configuration

[1354] 1. Information collection module

[1355] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[1356] 2. Information Analysis Module

[1357] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[1358] 3. User information input module

[1359] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[1360] 4. Teaching material recommendation module

[1361] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[1362] 5. Study Schedule Generation Module

[1363] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[1364] 6. Question Answering Module

[1365] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[1366] 7. Online Class Module

[1367] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[1368] 8. Emotion Recognition Engine

[1369] The server is equipped with an emotion recognition engine that recognizes the user's emotions and analyzes the user's emotional state during learning in real time.

[1370] Program processing explanation

[1371] Information collection and analysis

[1372] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[1373] Enter and submit user information

[1374] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[1375] Recommended teaching materials

[1376] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[1377] Generate a study schedule

[1378] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1379] Question and Answering

[1380] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[1381] Online classes

[1382] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[1383] How the emotion recognition engine works

[1384] The server analyzes the user's facial expressions and tone of voice captured through the camera and microphone, and evaluates the user's emotional state in real time. The emotion recognition engine determines the user's state, such as "concentrated," "tired," or "stressed."

[1385] 1. Collecting Emotional Data

[1386] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[1387] 2. Emotion analysis

[1388] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[1389] 3. Feedback and Adjustments

[1390] The server adjusts the learning content and schedule based on the user's emotional state. For example, if the server recognizes that the user is tired, it will suggest that the user take a break. If the user is concentrating, it will give feedback to continue learning.

[1391] 4. Sending encouraging messages

[1392] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device. For example, if the user has been concentrating for a long time, a positive message such as "Keep it up!" will be displayed.

[1393] Specific examples

[1394] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1395] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[1396] 2. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable teaching material, and sends this to the device.

[1397] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[1398] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[1399] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[1400] 6. While learning, the device captures the user's facial expressions and voice using a camera and microphone and sends them to the server. The server's emotion recognition engine determines that the user is "concentrating" and instructs the user to continue with the recommended learning material.

[1401] 7. If the user begins to feel tired while studying, the emotion recognition engine will determine that they are tired, and the server will display a message on the device saying, "Take a break."

[1402] In this way, by combining an emotion recognition engine, flexible support according to the user's condition becomes possible, maximizing learning efficiency and effectiveness.

[1403] The processing flow will be explained below.

[1404] Information collection and analysis

[1405] Step 1:

[1406] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[1407] Step 2:

[1408] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[1409] Step 3:

[1410] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[1411] Step 4:

[1412] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[1413] Step 5:

[1414] The server stores the analysis results and reliability scores in a database.

[1415] Enter and submit user information

[1416] Step 1:

[1417] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[1418] Step 2:

[1419] The terminal converts the input user information into a data package and transmits it to the server.

[1420] Step 3:

[1421] The server passes the received user information to the analysis module and starts the analysis.

[1422] Recommended teaching materials

[1423] Step 1:

[1424] The server uses an AI model to analyze the received user information.

[1425] Step 2:

[1426] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[1427] Step 3:

[1428] The server transmits the listed educational material information to the terminal.

[1429] Step 4:

[1430] The terminal displays the transmitted educational material information to the user.

[1431] Generate a study schedule

[1432] Step 1:

[1433] The user enters their current learning progress and target exam date into the application.

[1434] Step 2:

[1435] The terminal transmits the input data to the server.

[1436] Step 3:

[1437] The server operates a study schedule generation module based on the received study progress data and target exam date.

[1438] Step 4:

[1439] The server transmits the generated study schedule to the terminal.

[1440] Step 5:

[1441] The terminal displays the schedule to the user and periodically sends reminder notifications.

[1442] Question and Answering

[1443] Step 1:

[1444] Users enter questions they have about their studies into the application's question form.

[1445] Step 2:

[1446] The terminal converts the entered question into a data package and sends it to the server.

[1447] Step 3:

[1448] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[1449] Step 4:

[1450] The server sends the generated answers and explanations to the terminal.

[1451] Step 5:

[1452] The terminal displays the answer to the user.

[1453] Online classes

[1454] Step 1:

[1455] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[1456] Step 2:

[1457] The terminal transmits the reservation information to the server.

[1458] Step 3:

[1459] The server stores this reservation information in a database and sets a reminder notification before the class date.

[1460] Step 4:

[1461] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[1462] Step 5:

[1463] The terminal displays a notification to the user and provides a join link.

[1464] Step 6:

[1465] The user clicks on the class participation link in the reminder notification to participate in the online class.

[1466] How the emotion recognition engine works

[1467] Step 1:

[1468] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[1469] Step 2:

[1470] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[1471] Step 3:

[1472] The server adjusts learning content and schedules based on the user's emotional state. For example, if the server recognizes that the user is tired, it will display a suggestion such as "Take a break."

[1473] Step 4:

[1474] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device.

[1475] Step 5:

[1476] The device will then display the message to the user, for example, if they have been concentrating for a long time, it will display a positive message such as "Keep it up!"

[1477] Example 2

[1478] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1479] Current online learning support systems lack personalization based on learners' characteristics and progress, making it difficult to provide optimal learning content and schedules for individual learners. Furthermore, they are unable to assess learners' emotional states in real time and provide appropriate feedback, resulting in insufficient support when learners become tired or lose concentration. This can lead to reduced learning effectiveness.

[1480] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1481] In this invention, the server includes means for collecting information from word-of-mouth information and test-taking experiences across the country, means for analyzing the collected information and aggregating useful information, means for inputting and analyzing the characteristics of individual learners, means for recommending learning materials and reference books suitable for each learner based on the analysis results, means for generating test-taking preparation schedules according to the learner's progress, means for automatically answering questions from learners, means for offering individual lessons and training camps online and providing a place for learners to interact with each other, and means for evaluating the learner's emotional state in real time and providing feedback and support. This makes it possible to provide optimal learning content and schedules based on the learner's characteristics and emotional state, maximizing the learner's learning efficiency and effectiveness.

[1482] "Means for collecting information" refers to modules or software for obtaining data from online exam blogs and review sites.

[1483] "Means for analyzing information" refers to a module or software that evaluates acquired data using natural language processing technology and extracts useful information.

[1484] The "means for inputting and analyzing characteristics" refers to a module or software for inputting characteristic information such as a learner's learning style, strong subjects, and weak subjects into a server and analyzing this information.

[1485] "Means for recommending learning materials and reference books" refers to a module or software that selects and recommends the most suitable learning materials and reference books to learners based on the analysis results.

[1486] The "means for generating an exam preparation schedule" is a module or software for automatically generating an efficient study schedule based on the learner's progress information and exam date.

[1487] An "automatic answering means" is a module or software that uses an artificial intelligence model to generate and provide appropriate answers and explanations to questions posted by learners.

[1488] "Means for providing individual lessons and training camps" means modules or software for managing and providing individual lessons and training camps that can be attended by learners online.

[1489] The "means for assessing the learner's emotional state in real time" is a module or software for analyzing data acquired through a camera or microphone and determining the learner's emotional state in real time.

[1490] "Means for providing feedback and support" refers to a module or software that automatically provides appropriate encouraging messages and adjustments to learning content according to the learner's emotional state.

[1491] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[1492] System Configuration

[1493] This system mainly consists of the following modules and hardware components.

[1494] 1. Information collection module

[1495] The server periodically collects online reviews and test-taking experiences using Python and the Beautiful Soup library, and stores the collected data in a database in CSV or JSON format.

[1496] 2. Information Analysis Module

[1497] The server analyzes the collected data using natural language processing tools (e.g., NLTK or SpaCy), and generates a reliability score and categorizes the results.

[1498] 3. User information input module

[1499] The device sends the learner's characteristics (learning style, strong and weak subjects, etc.) entered by the user to the server using the HTTPS protocol.

[1500] 4. Teaching material recommendation module

[1501] The server analyzes user information using AI models such as TensorFlow and PyTorch, and based on the analysis results, it creates a list of learning materials and reference books suitable for each learner and sends them to the device.

[1502] 5. Study Schedule Generation Module

[1503] The server generates an efficient study schedule based on the learner's progress data and target date (exam date). The generated schedule is updated in real time and sent to the device.

[1504] 6. Question Answering Module

[1505] The device sends the user-entered question to the server, which uses BERT or GPT models to generate an answer and explanation for the question and sends it back to the device.

[1506] 7. Online Class Module

[1507] The server manages the online individual lessons and camp lessons booked by users, provides links to join the lessons, and also sends reminders before the lessons start.

[1508] 8. Emotion Recognition Engine

[1509] The server analyzes the user's facial expressions and tone of voice captured through a camera and microphone, and evaluates the user's emotional state in real time.

[1510] Specific examples

[1511] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1512] After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the application to the server. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable learning material, and sends this to the device. Alternatively, if the child enters "Why is the sum of the interior angles of a triangle 180 degrees?" into a question form, the content is sent from the device to the server. The server generates an answer using an AI model, explaining that "The sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[1513] Users can also reserve online individual lessons for the next month and receive reminder notifications from the server to ensure they don't miss them. They then click the class participation link at the specified time to attend the class. While studying, the device uses a camera and microphone to capture the user's facial expressions and voice and send them to the server. The server's emotion recognition engine determines that the user is "concentrating" and instructs the user to continue with the recommended learning materials. If the user begins to feel tired while studying, the emotion recognition engine determines that the user is "tired," and the server displays a message on the device saying, "Take a break."

[1514] Prompt Sentence Examples

[1515] As an example of a prompt, the following prompt is entered:

[1516] "Why does the sum of the interior angles of a triangle equal 180 degrees?"

[1517] "Please let me know the best study guide for exam preparation."

[1518] "Please tell me how to help my child concentrate on their studies."

[1519] The present invention allows users to be provided with optimal learning content and schedules based on the learner's characteristics and emotional state, thereby maximizing learning efficiency and effectiveness.

[1520] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1521] Step 1: Gather information

[1522] The server periodically collects data from online test-taking blogs and review sites using Python and the Beautiful Soup library. Specifically, the server creates a list of URLs and parses the HTML to extract text data. The input is the list of URLs to be collected, and the output is to save the extracted data in CSV or JSON format.

[1523] Step 2: Information analysis

[1524] The server analyzes the collected data using natural language processing tools (NLTK and SpaCy). Specifically, it filters unnecessary information from the text and calculates a reliability score. It also classifies the data into categories and extracts useful information. The input is the collected raw data, and the output is the reliability score and the data classified by category.

[1525] Step 3: Enter and submit user information

[1526] The user inputs learner characteristics (learning style, strong and weak subjects, etc.) through the application. The input information is sent to the server via the terminal using the HTTPS protocol. The input is the learner's characteristic data, and the output is the data sent to the server.

[1527] Step 4: Recommending materials

[1528] The server analyzes the received user information using an AI model such as TensorFlow or PyTorch. Specifically, the AI ​​model receives the learner's characteristics as input and lists the most suitable learning materials and reference books. The input is the learner's characteristic data, and the output is a list of recommended learning materials. The recommended list is presented to the user via their device.

[1529] Step 5: Generate a study schedule

[1530] The user inputs their current study progress and exam dates into the application. This information is sent to the server via the terminal. The server uses a Python scheduling library to generate an efficient study schedule. The input is study progress data and exam dates, and the output is the generated study schedule. The schedule is updated in real time and provided to the user via the terminal.

[1531] Step 6: Question-answering

[1532] Users enter questions they don't understand during their studies into a question form within the application. This question is sent to the server via the device. The server uses BERT or GPT models to generate an answer and explanation for the question. The input is the question, and the output is the answer and explanation. The answer is displayed to the user via the device.

[1533] Step 7: Book and attend online classes

[1534] A user reserves an online class using an application. The reservation information is sent to the server via the device. The server manages the reservation information and sends a reminder notification and a participation link to the device before the class starts. The input is the reservation information, and the output is the reminder notification and the participation link. When the class time arrives, the device sends a notification to the user, who clicks the participation link to join the class.

[1535] Step 8: Emotion Recognition

[1536] During learning, the device uses a camera and microphone to capture the user's facial expressions and voice. This data is sent to the server in real time. The server's emotion recognition engine analyzes this data and identifies the user's emotional state. The input is the captured facial and voice data, and the output is the user's emotional state. For example, states such as "concentrated" or "tired" are determined. Based on the emotional state, the server generates appropriate feedback or encouraging messages and sends them to the device.

[1537] (Application example 2)

[1538] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1539] Conventional online learning support systems have the problem of being unable to provide flexible support that responds to the learner's individual emotional state, preventing them from maximizing learning efficiency. The present invention aims to solve these problems and enable appropriate learning content and schedule adjustments based on the learner's emotional state. It also aims to increase the learner's persistence in learning by providing encouraging messages that maintain the learner's motivation and reduce fatigue and stress.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1541] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test-taking preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and providing a place for learners to interact with each other; means for recognizing the user's emotional state in real time and adjusting the learning content and schedule based on that state; and means for automatically generating and presenting encouraging messages based on the emotional state. This makes it possible to adjust the learning content and schedule appropriately according to the learner's emotional state, thereby improving learning efficiency and sustainability.

[1542] "Means of collecting information" refers to a function for collecting information from word-of-mouth information and test-taking experiences across the country.

[1543] "Means for analyzing information and aggregating useful information" refers to the function of analyzing collected information, selecting useful information, and aggregating it.

[1544] "Means for inputting and analyzing the characteristics of individual learners" is a function that inputs learner characteristics (learning style, strong and weak subjects, etc.) and analyzes them.

[1545] "Means for recommending teaching materials and reference books suitable for each learner" is a function that suggests the most suitable teaching materials and reference books for each learner based on the analysis results.

[1546] The "means for generating an exam preparation schedule" is a function that creates a study schedule necessary for exams based on the learner's progress.

[1547] "Means for automatically answering questions from learners" is a function that uses an AI model to automatically provide answers to questions submitted by learners.

[1548] "A means of offering individual lessons and training camps online, and providing a place for learners to interact with each other" refers to a function that holds individual lessons and group lessons online, and provides a place for learners to communicate with each other.

[1549] "Means for recognizing the user's emotional state in real time and adjusting the learning content and schedule based on that state" refers to a function that analyzes the user's emotional state in real time through a camera or microphone and flexibly changes the learning content and schedule based on the results.

[1550] The "means for automatically generating and presenting an encouraging message based on the emotional state" is a function for automatically creating and displaying an encouraging message in accordance with the emotional state of the user.

[1551] The present invention provides an online learning support system that recognizes the emotional state of a learner and adjusts the learning content and schedule based on that state. Specific embodiments of the present invention are described below.

[1552] System Overview

[1553] This system is composed of a server, terminals, and user interactions. The main modules include an information collection module, an information analysis module, a user information input module, a learning material recommendation module, a study schedule generation module, a question-answering module, an online class module, and an emotion recognition engine.

[1554] Hardware and Software

[1555] Hardware:

[1556] Smart glasses (with built-in camera and microphone)

[1557] server

[1558] User device (smartphone or tablet)

[1559] software:

[1560] Python programming language

[1561] OpenCV (image processing library)

[1562] DeepFace (emotion recognition library)

[1563] Playsound (audio playback library)

[1564] Program processing explanation

[1565] Information Collection Module

[1566] The server collects data in real time from major online blogs and review sites for entrance exams. This information is collected using web scraping technology. The collected data is then stored in a database for later analysis.

[1567] Information Analysis Module

[1568] The server analyzes the collected word-of-mouth information and test-taking experiences using natural language processing (NLP). Specifically, it uses text mining tools to extract useful information. It also evaluates the reliability of the information and extracts only valid data.

[1569] User information input module

[1570] Learners enter their characteristics, learning style, and current progress through their terminals. This information is sent to the server and used as the basis for analysis.

[1571] Material recommendation module

[1572] Based on the learner information sent, the server uses an AI model to recommend the most suitable learning materials and reference books. This recommendation list is sent to the user's device, allowing the user to proceed with their studies accordingly.

[1573] Study schedule generation module

[1574] The server generates an efficient study schedule taking into account the learner's progress and target date and time (e.g., exam date). The generated schedule is periodically updated and notified to the user's terminal.

[1575] Question and Answer Module

[1576] When users enter questions that arise during their studies into the application, the data is sent to the server, which uses an AI model to generate answers and detailed explanations and send them back to the user's device.

[1577] Online Class Module

[1578] Users can book individual or group lessons through the application. The server manages the reservation information and sends reminders and participation links before the lesson.

[1579] Emotion Recognition Engine

[1580] The server analyzes the user's facial expressions and voice in real time, captured through the smart glasses' camera and microphone, to assess the user's emotional state. The analysis uses the DeepFace library and performs the following steps:

[1581] Capture facial and voice data

[1582] Emotional state analysis

[1583] Adjusting learning content and schedules based on emotional state

[1584] Automatically generate and display encouraging messages

[1585] Specific examples

[1586] Consider a scenario where a user is preparing for a junior high school entrance exam. The user inputs their learning style information and receives recommended study materials, such as "math workbooks." While studying, if the user inputs a question about the sum of the interior angles of a triangle, the AI ​​model generates and notifies the user with an answer, such as "The sum of the interior angles of a triangle is 180 degrees." The smart glasses monitor the learner's level of concentration, prompting them to continue studying if they are focused, or encouraging them to take a break if they are tired.

[1587] Prompt Sentence Examples

[1588] "Generate positive messages to encourage stressed workers to take breaks."

[1589] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1590] Step 1:

[1591] Data collection by information collection module

[1592] The server collects data in real time from online exam blogs and review sites, specifically using web scraping technology to obtain exam-related information and store that data in a database.

[1593] Input: URL of an online exam blog or review site

[1594] Data processing: Extracting information through web scraping

[1595] Output: Collected exam information database

[1596] Step 2:

[1597] Data analysis using the information analysis module

[1598] The server analyzes the collected reviews and test-taking experiences using natural language processing (NLP) technology. Specifically, it uses text mining tools to extract useful information and evaluate its reliability.

[1599] Input: Collected exam information database

[1600] Data Computing: Text Analysis and Trust Assessment with NLP

[1601] Output: A database of useful analyzed information

[1602] Step 3:

[1603] Collecting characteristic information using the user information input module

[1604] Users use a smartphone or tablet to input information about their learner characteristics (such as their learning style, favorite subjects, and progress). The input information is then sent from the device to the server.

[1605] Input: Learner characteristics information

[1606] Data processing: Sending to the server

[1607] Output: Learner characteristics data stored on the server

[1608] Step 4:

[1609] Recommending materials using the material recommendation module

[1610] The server uses an AI model to recommend optimal learning materials and reference books based on the learner's characteristic data, and notifies the user of the generated recommendation list.

[1611] Input: Learner characteristics data, useful information database

[1612] Data calculation: Calculating optimal teaching materials using AI models

[1613] Output: Recommended learning materials list

[1614] Step 5:

[1615] Schedule creation using the study schedule generation module

[1616] The server generates an efficient study schedule based on the learner's progress and target date and time (e.g., exam date). The generated schedule is updated periodically and notified to the user's device.

[1617] Input: Learner progress, target date and time

[1618] Data Computation: Generating Efficient Schedules

[1619] Output: Study schedule

[1620] Step 6:

[1621] Answering questions using the question answering module

[1622] When a user has a question that arises during their study, they input it into their device and the data is sent to the server, which uses an AI model to generate an answer and detailed explanation, which is then sent back to the user's device.

[1623] Input: Question data from the user

[1624] Data Computation: Answer Generation with AI Models

[1625] Output: Answer and detailed explanation

[1626] Step 7:

[1627] Offering classes through online lesson modules

[1628] Users make reservations for individual or group lessons through the application. The server manages the reservation information and sends reminders and participation links before the lesson.

[1629] Input: Class reservation information

[1630] Data management: Reservation information management

[1631] Output: Reminder notification, participation link

[1632] Step 8:

[1633] Emotion analysis using an emotion recognition engine

[1634] The server analyzes the user's facial expressions and voice in real time, captured through the smart glasses' camera and microphone, and evaluates the user's emotional state. Based on the analysis results, the server adjusts the learning content and schedule and provides appropriate feedback.

[1635] Input: User's facial expression data, voice data

[1636] Data Computation: Emotion Analysis with DeepFace

[1637] Output: Adjustment of learning content and schedule based on emotional state, encouraging messages

[1638] Example processing steps

[1639] While the user is studying, the smart glasses analyze the user's facial expression and determine that the user is "highly stressed." In this case, the server automatically generates a message encouraging the user to "take a break" and notifies the user by voice and text. An example of a prompt sentence is "Please generate a positive message to suggest that highly stressed workers take a break."

[1640] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1641] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1642] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1643] [Fourth embodiment]

[1644] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1645] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1646] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1647] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1648] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1649] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1650] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1651] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1652] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1653] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1654] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1655] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1656] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1657] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system includes the following components and functions:

[1658] System Configuration

[1659] 1. Information collection module

[1660] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[1661] 2. Information Analysis Module

[1662] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[1663] 3. User information input module

[1664] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[1665] 4. Teaching material recommendation module

[1666] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[1667] 5. Study Schedule Generation Module

[1668] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[1669] 6. Question Answering Module

[1670] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[1671] 7. Online Class Module

[1672] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[1673] Program processing explanation

[1674] Information collection and analysis

[1675] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[1676] Enter and submit user information

[1677] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[1678] Recommended teaching materials

[1679] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[1680] Generate a study schedule

[1681] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1682] Question and Answering

[1683] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[1684] Online classes

[1685] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[1686] Specific examples

[1687] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1688] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[1689] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[1690] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[1691] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[1692] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[1693] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[1694] The processing flow will be explained below.

[1695] Information collection and analysis

[1696] Step 1:

[1697] The server loads a specified list of websites on the Internet and sets a data collection schedule.

[1698] Step 2:

[1699] The server scrapes and retrieves HTML data from the target URLs according to the set schedule.

[1700] Step 3:

[1701] The server extracts text data corresponding to test-taking experiences and word-of-mouth information from the acquired HTML data.

[1702] Step 4:

[1703] The server analyzes the extracted text data using natural language processing and calculates a reliability score.

[1704] Step 5:

[1705] The server stores the analysis results and reliability scores in a database.

[1706] Enter and submit user information

[1707] Step 1:

[1708] A user launches the application and enters information about the learner's characteristics (e.g., learning style, favorite subjects).

[1709] Step 2:

[1710] The terminal converts the input user information into a data package and transmits it to the server.

[1711] Step 3:

[1712] The server passes the received user information to the analysis module and starts the analysis.

[1713] Recommended teaching materials

[1714] Step 1:

[1715] The server uses an AI model to analyze the received user information.

[1716] Step 2:

[1717] The server uses the analysis results to create a list of the most suitable teaching materials and reference books.

[1718] Step 3:

[1719] The server transmits the listed educational material information to the terminal.

[1720] Step 4:

[1721] The terminal displays the transmitted educational material information to the user.

[1722] Generate a study schedule

[1723] Step 1:

[1724] The user enters their current learning progress and target exam date into the application.

[1725] Step 2:

[1726] The terminal transmits the input data to the server.

[1727] Step 3:

[1728] The server operates a study schedule generation module based on the received study progress data and target exam date.

[1729] Step 4:

[1730] The server transmits the generated study schedule to the terminal.

[1731] Step 5:

[1732] The terminal displays the schedule to the user and periodically sends reminder notifications.

[1733] Question and Answering

[1734] Step 1:

[1735] Users enter questions they have about their studies into the application's question form.

[1736] Step 2:

[1737] The terminal converts the entered question into a data package and sends it to the server.

[1738] Step 3:

[1739] The server passes the received question to the AI ​​question-answering module, which generates an answer.

[1740] Step 4:

[1741] The server sends the generated answers and explanations to the terminal.

[1742] Step 5:

[1743] The terminal displays the answer to the user.

[1744] Online classes

[1745] Step 1:

[1746] Users can use the application to book online individual lessons once a month or online training camp lessons once every three months.

[1747] Step 2:

[1748] The terminal transmits the reservation information to the server.

[1749] Step 3:

[1750] The server stores this reservation information in a database and sets a reminder notification before the class date.

[1751] Step 4:

[1752] The server will send a reminder notification and a link to join the class to the device 10 minutes before the class starts.

[1753] Step 5:

[1754] The terminal displays a notification to the user and provides a join link.

[1755] Step 6:

[1756] The user clicks on the class participation link in the reminder notification to participate in the online class.

[1757] Example 1

[1758] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1759] Conventional learning support systems have difficulty in recommending learning plans and materials that meet the individual needs of learners, and in managing progress, resulting in the inability to provide an effective learning environment. Furthermore, the reliability of exam information and prompt responses to learners' questions are also insufficient. This places a heavy burden on test takers and their families.

[1760] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1761] In this invention, the server includes: means for collecting information from nationwide assessment information and test experience stories; means for analyzing the collected information using natural language processing technology and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the generated analysis results; means for generating an effective study schedule based on the learner's learning progress data and target dates and times; means for automatically answering questions from learners using a large-scale language model; and means for providing online individual lessons and training camps and for providing a place where learners can interact with each other. This enables efficient and personalized study support and significantly reduces the burden on test takers and their families.

[1762] "National evaluation information" refers to information posted on the Internet based on test evaluations and test-taking experiences.

[1763] "Exam Experiences" are detailed reports and impressions about the exam written by test takers based on their own experiences.

[1764] "Natural language processing technology" is an artificial intelligence technology for understanding, analyzing, and generating human language.

[1765] A "large-scale language model" is an artificial intelligence model trained using massive amounts of text data, and is a technology that has the ability to understand and generate sentences like humans (e.g., GPT-4).

[1766] "Characteristics of individual learners" refers to information about characteristics related to individual learning, such as each learner's learning style, strong and weak subjects, and learning progress.

[1767] "Teaching materials and reference materials" refers to educational resources such as textbooks, workbooks, online courses, and video materials that learners use to advance their studies.

[1768] "Study progress data" is information that indicates how far a learner has progressed in their studies, and includes the content learned, the level of achievement, and the amount of time spent studying.

[1769] An "effective study schedule" is a study plan that is optimized based on the learner's goals and progress.

[1770] "Online private lessons and training camps" are private instruction and special group lessons provided via the Internet.

[1771] A "place where learners can interact" is an online platform where learners can communicate with each other, exchange information, and engage in collaborative learning.

[1772] The present invention provides an online learning support system to improve the learning environment for junior high school entrance exams and reduce the burden on students and their families. This system is implemented as follows.

[1773] System Configuration

[1774] The system consists of the following hardware and software elements:

[1775] Server: A central management system that collects, analyzes, stores, and analyzes data using AI models.

[1776] Device: The device used by the user (computer, tablet, smartphone, etc.).

[1777] Generative AI models (e.g., GPT-4, BERT, etc.): Used to analyze collected data and user input information and provide optimal learning resources.

[1778] Program processing explanation

[1779] Information collection and analysis

[1780] The server collects data from major exam blogs and review sites on the Internet. This process uses web scraping tools such as Python's Beautiful Soup and Scrapy. The collected data is analyzed using natural language processing techniques (e.g., Google's BERT model) and a reliability score is applied. The school name, study methods, and key points about exam preparation for each post are then stored in a database.

[1781] Enter and submit user information

[1782] Users launch the application, log in, and enter their child's learning style and characteristics (e.g., attention span, preferred learning methods). The device then sends this information to the server. Communication is via the HTTPS protocol.

[1783] Recommended teaching materials

[1784] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and creates a list of learning materials and reference books that are best suited to each learner. This list is sent to the device and presented to the user. For example, the server may recommend the "Math Masters" YouTube channel.

[1785] Generate a study schedule

[1786] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1787] Question and Answering

[1788] When a user has a question they don't understand while studying, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[1789] Online classes

[1790] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[1791] Examples of concrete examples and prompts

[1792] Specific examples

[1793] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1794] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[1795] 2. The server performs AI analysis based on the collected information, recommends the "Math Masters" YouTube channel as the most suitable teaching material, and sends this to the device.

[1796] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[1797] 4. The server generates an answer using an AI model, explains that "the sum of the interior angles of a triangle is 180 degrees because the angles that make up the triangle are the intersection of two straight lines," and sends the answer back to the device.

[1798] 5. The user reserves an online individual lesson for the next month, receives a reminder notification from the server to ensure they don't miss it, and then clicks the class participation link at the designated time to join the class.

[1799] Prompt Sentence Examples

[1800] "How can I effectively manage my child's learning style?"

[1801] "Please explain why the sum of the interior angles of the following triangle is 180 degrees."

[1802] In this way, the online learning support system of the present invention can effectively and economically support preparation for junior high school entrance exams, and can significantly reduce the burden on learners and their families.

[1803] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1804] Step 1: Start gathering information

[1805] The server accesses designated exam blogs and review sites at specific time intervals. A list of URLs of the sites to be collected is used as input. HTML data obtained from each site is obtained as output. Specifically, the data is collected using web scraping tools such as Python's Beautiful Soup and Scrapy.

[1806] Step 2: Data collection

[1807] The server analyzes the collected HTML data and extracts information related to the exam. The collected HTML data is used as input. Exam information (school name, study methods, exam preparation information, etc.) is obtained as output. Specifically, the server parses the HTML data to obtain the necessary text information and stores it in a database.

[1808] Step 3: Data analysis

[1809] The server analyzes the extracted exam information using natural language processing technology (e.g., Google's BERT model). The extracted exam information is used as input. The analyzed information and a reliability score are obtained as output. Specifically, the text information is input into the BERT model, which performs semantic analysis of the information and reliability evaluation.

[1810] Step 4: Reliability Scoring

[1811] The server scores the credibility of each post based on the analysis results and assigns a tag to each one. The analyzed information and the results of the credibility assessment are used as input. The output is the test information with a credibility score. The specific operation is to add the credibility score to each entry in the database.

[1812] Step 5: Enter user information

[1813] The user launches the application, logs in, and enters information about their child's learning style and characteristics. The learning style information entered by the user is used as input. This information is sent from the device to the server as output. The specific operation requires the user to enter information into a form and press the submit button.

[1814] Step 6: Send user information

[1815] The terminal sends the entered user information to the server. As input, the learning style information entered by the user is used. As output, this information is sent to the server. As a specific operation, data is sent securely using the HTTPS protocol.

[1816] Step 7: Recommending materials

[1817] The server analyzes the received user information using an AI model (e.g., OpenAI's GPT-4) and lists the most suitable learning materials and reference books. The user information is used as input. The output is a list of recommended learning materials and reference books. Specifically, the server inputs the user information into the AI ​​model and stores the generated recommendation list in a database.

[1818] Step 8: Submit your recommendation list

[1819] The server sends the generated teaching material recommendation list to the terminal. The generated recommendation list is used as input. This list is sent to the terminal as output. As a specific operation, the recommendation list is sent to the terminal via HTTPS protocol.

[1820] Step 9: Generate a study schedule

[1821] The server generates an efficient study schedule based on the study progress data and exam dates. The study progress data and exam dates are used as input. The generated study schedule is obtained as output. Specifically, the server calculates the optimal schedule using a scheduling algorithm (e.g., linear programming).

[1822] Step 10: Schedule Sending

[1823] The server sends the generated learning schedule to the terminal. The generated learning schedule is used as input. This schedule is sent to the terminal as output. As a specific operation, the learning schedule is sent to the terminal via the HTTPS protocol.

[1824] Step 11: Question Answering

[1825] When a user is studying, they enter a question they don't understand into a question form within the application. The question entered by the user is used as input. The question is sent from the device to the server as output. The specific operation requires the user to enter a question into the form and press the send button.

[1826] Step 12: Submit your question

[1827] The terminal sends the entered question to the server. The question entered by the user is used as input. The question is sent to the server as output. Specifically, the data is sent securely using the HTTPS protocol.

[1828] Step 13: Answer Generation

[1829] The server uses an AI model (e.g., GPT-4) to generate an answer and explanation for the question. The user's question is used as input. The generated answer and explanation are obtained as output. Specifically, the question is input into the AI ​​model, and the generated answer is stored in a database.

[1830] Step 14: Submit your answers

[1831] The server sends the generated answer to the terminal. The generated answer is used as input. This answer is sent to the terminal as output. As a specific operation, the answer is sent to the terminal via the HTTPS protocol.

[1832] Step 15: Book an online class

[1833] Users use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The lesson information reserved by the user is used as input. This reservation information is sent from the terminal to the server as output. The specific operation requires the user to select a class and press the reservation button.

[1834] Step 16: Reservation Information Management

[1835] The server saves and manages reservation information in a database. The user's reservation information is used as input. The saved reservation information is obtained as output. Specific operations include saving the reservation information in the database and displaying it on the management screen.

[1836] Step 17: Send reminder notifications

[1837] The server sends a reminder notification to the terminal before the start of the class. The reservation information and the start time of the class are used as input. The reminder notification is sent to the terminal as output. Specifically, the server generates a notification before the start time of the class and sends it to the terminal via HTTPS protocol.

[1838] Step 18: Send class participation link

[1839] The server sends a participation link to the terminal just before the class starts. The class reservation information and participation link are used as input. The participation link is sent to the terminal as output. Specifically, the participation link is generated and sent to the terminal via the HTTPS protocol.

[1840] (Application example 1)

[1841] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1842] The diverse learning environments required for junior high school entrance exams place a significant burden on students and their families. However, finding the optimal learning methods and materials for each individual student is not easy and requires time and effort. Students also need to be able to quickly and accurately respond to any questions they may have. Furthermore, because it is difficult to maximize the effectiveness of online classes and self-study tools, a system that can solve all of these issues at once is needed.

[1843] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1844] In this invention, the server includes: means for collecting information from word-of-mouth information and test-taking experiences across the country; means for analyzing the collected information and aggregating useful information; means for inputting and analyzing the characteristics of individual learners; means for recommending learning materials and reference books suitable for each learner based on the analysis results; means for generating test preparation schedules according to the learner's progress; means for automatically answering questions from learners; means for providing online individual lessons and training camps and a place for learners to interact with each other; means for generating optimal answers using a generative AI model based on the learner's learning data; and means for providing answer prompts generated by the AI ​​model. This makes it possible to efficiently and effectively manage learner progress and quickly resolve learner questions.

[1845] "Word of mouth" refers to the sharing of opinions and experiences published by individuals or groups on the Internet.

[1846] "Exam experience stories" are records of the experiences of test takers and their families in past exams, including the preparation process and results.

[1847] "Information gathering means" refers to devices or programs that have the function of automatically obtaining word-of-mouth information and test-taking experience stories from the Internet.

[1848] "Information analysis means" refers to technologies and programs for analyzing collected information and evaluating its usefulness and reliability.

[1849] "User information input means" refers to a device or interface for inputting learner characteristic information (for example, learning style, strong subjects, weak subjects, etc.).

[1850] "Materials recommendation methods" refer to technologies and algorithms that select and recommend the most appropriate materials and reference books for each learner based on collected and analyzed information.

[1851] "Study schedule generation means" refers to a device or program that creates an efficient study schedule based on the learner's progress data and target schedule.

[1852] A "question-answering tool" is a device or program that accepts questions from learners and automatically generates answers using technology such as AI.

[1853] "Online class delivery means" refers to a system or interface for providing and facilitating individual lessons and training camps via the Internet.

[1854] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to generate and provide optimal solutions to specific tasks.

[1855] A "prompt" is a sentence or phrase that serves as a question or instruction to be input into a generative AI model.

[1856] This invention is an online learning support system that reduces the burden on junior high school entrance exam students and their families. This system includes modules for information collection, information analysis, user information input, teaching material recommendation, study schedule generation, question answering, and online classes.

[1857] System Configuration

[1858] Information collection and analysis

[1859] The server periodically collects user reviews and test-taking experiences from major online test-taking blogs and review sites. This data is analyzed using natural language processing technology and a reliability score is assigned. The analysis results are stored in a database that includes the usefulness of each post and detailed information (e.g., school name, study method, test preparation). The system primarily uses the Python programming language, with SpaCy and Transformers (Hugging Face) as natural language processing libraries.

[1860] Enter and submit user information

[1861] After launching the application, users enter information about their child's learning style and characteristics, including attention span, preferred learning methods, and favorite and least favorite subjects, on their smartphone screen. The entered data is then sent from the device to a server.

[1862] Recommended teaching materials

[1863] The server selects the most suitable learning materials and reference books for each user based on the information received from the user. During this process, it analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of recommended learning materials. The generated list is sent to the device and provided to the user.

[1864] Generate a study schedule

[1865] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate a customized learning schedule for each user, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1866] Question and Answering

[1867] When a user is studying, they enter a question they don't understand into the question form within the application. The question is sent from the device to the server. The server uses a generative AI model to generate the optimal answer and explanation for the question and sends it back to the device. The user can check the answer in real time and continue studying. As a specific example, if a user asks "Why is the sum of the interior angles of a triangle 180 degrees?" the server sends the following prompt to the AI ​​model to generate an answer.

[1868] Input: Why the sum of the interior angles of a triangle is 180 degrees

[1869] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[1870] Online classes

[1871] Users can use the application to reserve online individual lessons or online training camps. The server manages reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link so the user can join the class.

[1872] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1873] Step 1: Gather information

[1874] The server automatically collects test-taking experience information and reviews from major test-taking blogs and review sites on the Internet. This process involves periodically accessing specific websites and obtaining data in HTML or JSON format. Specifically, data is extracted using scraping tools and APIs. The input is a URL or search query, and the output is the collected raw data.

[1875] Step 2: Information analysis

[1876] The server analyzes the information collected in step 1 using natural language processing technology. It uses Python's SpaCy and Transformers libraries to extract useful information from the text data and score its reliability. The input is raw data, and the output is analyzed information and its reliability score. Specifically, it performs text summarization, keyword extraction, sentiment analysis, etc.

[1877] Step 3: Enter user information

[1878] The user launches the application and enters information about their child's learning style and characteristics, including attention span, preferred learning methods, and strong and weak subjects. The input is the data provided in each information form, and the output is structured user information that the device sends to the server.

[1879] Step 4: Recommending materials

[1880] The server performs analysis based on the user information acquired in step 3 to recommend optimal learning materials and reference books. It analyzes the data using an AI model (e.g., machine learning algorithms or artificial intelligence technology) and generates a list of appropriate learning materials for the user. The input is user information, and the output is a list of recommended learning materials. Specifically, it selects the most appropriate learning materials based on past data and the AI ​​model.

[1881] Step 5: Create a study schedule

[1882] The user inputs their current learning progress and exam date. The device sends this information to the server. The server generates a customized learning schedule based on this data and periodically updates and adjusts it. The input is learning progress data and exam date, and the output is the learning schedule. Specifically, it generates a Gantt chart and allocates daily learning tasks.

[1883] Step 6: Question-answering

[1884] During learning, users enter questions they don't understand into a question form within the application. This question is sent from the device to the server. The server uses a generative AI model (such as GPT-3) to generate the optimal answer and explanation for the question. The input is the user's question, and the output is the generated answer and explanation. Specifically, it generates an appropriate answer for the following prompt:

[1885] Input: Why the sum of the interior angles of a triangle is 180 degrees

[1886] Expected output: The reason the angles of a triangle sum to 180 degrees is because the sum of all the angles in a triangle is a straight line. In fact, if you take all three angles that make up a triangle and put them together, they form a straight line that equals 180 degrees.

[1887] Step 7: Online classes

[1888] Users use the application to book online individual lessons or training camp classes. The server manages the user's reservation information and sends a reminder notification and a participation link to the device before the class starts. When the class time arrives, the device sends a notification to the user and displays a participation link, allowing the user to join the class. The input is the class reservation date and time and user information, and the output is a reminder notification and a participation link. Specifically, the calendar API is used to set up notifications and generate a link for the video conferencing system.

[1889] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1890] The present invention is an online learning support system that supports junior high school entrance exams, and provides multifaceted support by recognizing the user's emotions and adjusting the learning content and schedule based on those emotions, sending encouraging messages, etc. Specific embodiments of the present invention and details of the program processing are described below.

[1891] System Configuration

[1892] 1. Information collection module

[1893] The server periodically collects online word-of-mouth information and test-taking experiences and stores them in a database.

[1894] 2. Information Analysis Module

[1895] The server analyzes the collected information using natural language processing technology, and evaluates and classifies it for usefulness and reliability.

[1896] 3. User information input module

[1897] The terminal transmits the learner's characteristics (learning style, strong and weak subjects, etc.) input by the user to the server.

[1898] 4. Teaching material recommendation module

[1899] Based on user information, the server uses an AI model to recommend learning materials and reference books suitable for the learner.

[1900] 5. Study Schedule Generation Module

[1901] The server generates an efficient study schedule based on the learner's progress data and the target date and time (exam date).

[1902] 6. Question Answering Module

[1903] The device sends the questions entered by the user to the server, which then generates and provides answers and explanations using AI.

[1904] 7. Online Class Module

[1905] The server manages online individual lessons and training camp lessons booked by users and provides links to join the lessons.

[1906] 8. Emotion Recognition Engine

[1907] The server is equipped with an emotion recognition engine that recognizes the user's emotions and analyzes the user's emotional state during learning in real time.

[1908] Program processing explanation

[1909] Information collection and analysis

[1910] The server periodically collects data from major online blogs and review sites for entrance exam preparation. The collected data is analyzed using natural language processing technology and a reliability score is applied. The school name, study methods, and key points regarding exam preparation for each post are then stored in a database.

[1911] Enter and submit user information

[1912] The user launches the application and enters information about their child's learning style and characteristics (e.g., attention span, preferred learning methods), which the device then transmits to the server.

[1913] Recommended teaching materials

[1914] The server uses an AI model to analyze the received user information and create a list of learning materials and reference books that are best suited to each learner. This information is then sent to the device and presented to the user.

[1915] Generate a study schedule

[1916] The user inputs their current learning progress and exam date. The device sends this information to the server. The server uses this data to generate an efficient learning schedule, which is updated and adjusted periodically. The generated schedule is sent to the device and provided to the user.

[1917] Question and Answering

[1918] When users encounter a problem during their studies, they enter it into a question form within the application. The device then sends the question to the server. The server uses an AI model to generate an answer and explanation for the question and sends it back to the device. The answer is displayed on the device, allowing the user to continue studying in real time.

[1919] Online classes

[1920] Users can use the application to reserve online individual lessons once a month or online training camp lessons once every three months. The server manages this reservation information and sends reminder notifications and participation links to the device before the class starts. When the class time arrives, the device sends a notification to the user and provides a participation link.

[1921] How the emotion recognition engine works

[1922] The server analyzes the user's facial expressions and tone of voice captured through the camera and microphone, and evaluates the user's emotional state in real time. The emotion recognition engine determines the user's state, such as "concentrated," "tired," or "stressed."

[1923] 1. Collecting Emotional Data

[1924] The device uses a camera and microphone to capture the user's facial expressions and voice in real time and transmits the data to a server.

[1925] 2. Emotion analysis

[1926] The server uses an emotion recognition engine to analyze the received data and determine the user's current emotional state.

[1927] 3. Feedback and Adjustments

[1928] The server adjusts the learning content and schedule based on the user's emotional state. For example, if the server recognizes that the user is tired, it will suggest that the user take a break. If the user is concentrating, it will give feedback to continue learning.

[1929] 4. Sending encouraging messages

[1930] The server automatically generates encouraging and follow-up messages based on the user's emotional state and sends them to the device. For example, if the user has been concentrating for a long time, a positive message such as "Keep it up!" will be displayed.

[1931] Specific examples

[1932] As an example, consider a scenario in which a user uses this system to prepare for a junior high school entrance exam.

[1933] 1. After logging in for the first time, the user enters information about their child's learning style, and the data is sent from the device to the server.

[1934] 2. The server performs AI analysis based on the collected information, recommends an "arithmetic workbook" as the most suitable teaching material, and sends this to the device.

[1935] 3. When the user's child enters the question "Why is the sum of the interior angles of a triangle 180 degrees?" into the question form, the content is sent from the device to the server.

[1936] 4. The server generates an answer usi...

Claims

1. A means of collecting information from word-of-mouth information and test-taking experiences across the country, A means of analyzing the collected information and aggregating useful information; A means for inputting and analyzing the characteristics of individual learners; Based on the analysis results, a method is provided to recommend teaching materials and reference books suitable for each learner. a means for generating an exam preparation schedule according to the learner's progress; a means for automatically answering questions from learners; We provide individual lessons and training camps online, and provide a place for learners to interact with each other. A system including:

2. The system of claim 1 further comprising a means for evaluating the reliability of the word-of-mouth information and test-taking experiences.

3. The system according to claim 1 , further comprising a means for recommending the most suitable lesson video to the learner based on the compatibility diagnosis.

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