System

Abstract

A system is provided.SOLUTION: An input means for inputting basic information related to learning by a user, a generation means for receiving the input information and generating an individual learning plan by using an AI algorithm, a transmission means for transmitting the generated learning plan to a terminal of the user, a question provision means for generating and transmitting daily questions based on a progress status of the user, an answer determination means for determining an answer received from the user and transmitting a result thereof, and a learning data analysis means for analyzing past learning data; A system comprising: analyzing means for identifying weaknesses in a user; remedial measures providing means for generating and transmitting remedial measures plans to overcome the identified weaknesses; monitoring means for continuously monitoring the user's learning progress and generating and transmitting visual reports; and question answering means for receiving user questions and generating and transmitting answers using AI.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] Many students preparing for entrance exams require learning support tailored to their individual needs and challenges, but efficient, personalized support is currently not being provided. Gen Z students in particular require interactive learning support that utilizes the communication methods they use on a daily basis. Therefore, it is necessary to improve learning efficiency through customized plans tailored to each student's learning situation and real-time question and answer sessions. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: An input means is provided for the user to input basic information about their studies, and a generation means is used that receives the information and generates an individualized study plan using an AI algorithm. Furthermore, a problem providing means is provided that includes a transmission means that transmits the generated study plan to the user's device and generates and transmits daily problems based on the user's progress.

[0006] The system also includes an answer determination means for determining answers received from the user and transmitting the results, an analysis means for analyzing past learning data and identifying the user's weaknesses, a supplementary lesson provision means for generating and transmitting a supplementary lesson plan to overcome the identified weaknesses, a monitoring means for continuously monitoring the user's learning progress and generating and transmitting a visual report, and a question answering means for receiving the user's questions and generating and transmitting answers using AI. In this way, the system provides efficient learning support tailored to individual needs and supports students in passing the entrance exams for their desired schools.

[0007] The "input means" is an interface for the user to input basic information about the study.

[0008] "Generation means" refers to a means for creating an individual learning plan using an AI algorithm based on received user information.

[0009] The "transmission means" is a means for transmitting the generated study plan and progress report to the user's terminal.

[0010] The "problem providing means" is a means for generating daily problems based on the user's study plan and progress, and providing the selected problems to the user.

[0011] The "answer determination means" is a means for analyzing the answers received from the user and determining whether they are correct or incorrect.

[0012] The "analysis means" is a means for analyzing past learning data and identifying the user's weaknesses.

[0013] The "tuition provision means" is a means for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[0014] The "monitoring means" is a means for continuously tracking a user's learning progress and creating visual reports based on that data.

[0015] A "question answering means" is a means for receiving a question from a user, generating an appropriate answer using AI, and sending it to the user. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that provides customized learning support to each student. This system is implemented via a communication application that users use on a daily basis. Below, we will show an embodiment of each of the main functions.

[0038] Customized learning plan design

[0039] 1. Enter your study information

[0040] Users input their basic information (grade, desired school, strong and weak subjects, target score, etc.) via a communication application.

[0041] The terminal transmits the input information to the server.

[0042] 2. Plan Generation

[0043] The server uses AI algorithms to generate a learning plan tailored to each student based on the information it receives.

[0044] A generating means determines the schedule, material selection, etc., and formats the individual plan for a communication application.

[0045] 3. Send plan

[0046] The transmission means transmits the generated study plan to the user's terminal.

[0047] Examples:

[0048] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[0049] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[0050] Questions provided regularly every morning

[0051] 1. Problem generation

[0052] The server generates a problem set based on the study plan.

[0053] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[0054] 2. Submit your question

[0055] The server sends the problem to the user's communication application at a set time each morning.

[0056] The user checks the question on the communication application and submits the answer.

[0057] 3. Receiving answers and judging

[0058] The terminal transmits the user's answer to the server.

[0059] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[0060] Examples:

[0061] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application.

[0062] The user answers the questions and sends the answers via a communication application.

[0063] The server automatically checks the answers and immediately sends the results.

[0064] AI support to overcome weaknesses

[0065] 1. Analysis of learning results

[0066] The server analyzes the user's past answer data and identifies weak points.

[0067] 2. Supplementary lesson plan generation

[0068] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[0069] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[0070] Examples:

[0071] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics.

[0072] The server creates a supplementary lesson plan on electromagnetics and provides related questions and explanatory videos via a communication application.

[0073] Progress monitoring and feedback transfer

[0074] 1. Collecting training data

[0075] The server periodically collects the user's learning data (daily answer results, progress, etc.).

[0076] 2. Data analysis and feedback generation

[0077] The server analyzes the collected data and evaluates the user's progress.

[0078] A monitoring means generates progress reports.

[0079] 3. Send Feedback

[0080] The transmission means transmits the generated progress report to the user via a communication application.

[0081] Examples:

[0082] The server analyzes the learning data for one week and graphs the level of understanding and progress.

[0083] The server generates a weekly report every Monday and sends it to the user via a communication application.

[0084] Real-time question answering

[0085] 1. Receiving questions

[0086] Users use a communication application to submit questions they are studying.

[0087] The terminal sends a query to the server.

[0088] 2. Response Generation

[0089] The server uses AI to analyze the received questions and generate appropriate answers.

[0090] 3. Submit your response

[0091] The sending means returns the generated answer to the communication application in real time.

[0092] Examples:

[0093] A user sends a question via a communication application saying, "I don't know how to solve a quadratic equation."

[0094] The server receives the question and uses AI to generate a detailed answer.

[0095] The server sends the answer in real time via a communication application and replies, "Please try solving it by following the steps below."

[0096] The above is a detailed description of the embodiments of the invention based on the claims.

[0097] The processing flow will be explained below.

[0098] Customized learning plan design

[0099] Step 1:

[0100] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[0101] Step 2:

[0102] The terminal transmits the input user information to the server.

[0103] Step 3:

[0104] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[0105] The server selects the schedule and study materials and creates a customized learning plan.

[0106] Step 4:

[0107] The server formats the generated lesson plan and sends it to the communication application.

[0108] Questions provided regularly every morning

[0109] Step 1:

[0110] The server generates a problem set based on the user's study plan and progress.

[0111] Step 2:

[0112] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[0113] Step 3:

[0114] The server sends selected questions to the user's communication application at a set time every morning.

[0115] Step 4:

[0116] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[0117] Step 5:

[0118] The terminal sends the user's answer to the server.

[0119] Step 6:

[0120] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[0121] AI support to overcome weaknesses

[0122] Step 1:

[0123] The server collects the user's past answer data and uses analytical algorithms to identify weaknesses.

[0124] Step 2:

[0125] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[0126] Step 3:

[0127] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[0128] Step 4:

[0129] The user advances his / her studies using the supplementary learning materials sent to him / her.

[0130] Progress monitoring and feedback transfer

[0131] Step 1:

[0132] The server collects the user's daily learning data (answers to questions, time, progress, etc.).

[0133] Step 2:

[0134] The server analyzes the data collected and evaluates learning progress and level of understanding.

[0135] Step 3:

[0136] The server generates weekly and monthly progress reports.

[0137] Step 4:

[0138] The server formats the generated report and sends it to the communication application.

[0139] Step 5:

[0140] Users can check the reports and understand their own learning status.

[0141] Real-time question answering

[0142] Step 1:

[0143] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[0144] Step 2:

[0145] The terminal sends the user's question to the server.

[0146] Step 3:

[0147] The server receives the question and uses AI to generate an appropriate answer.

[0148] Step 4:

[0149] The server sends the generated answer in real time to the communication application.

[0150] Step 5:

[0151] The user checks the answer in the communication application and resolves the question.

[0152] The above are the specific steps of the program processing of this system.

[0153] Example 1

[0154] 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."

[0155] Conventional learning support systems have difficulty fully addressing the learning progress and weaknesses of individual students, and are limited to providing uniform learning plans and feedback. Furthermore, it is difficult to provide real-time question and answer sessions and progress reports, which prevents users from maintaining their motivation to learn and from providing effective learning support.

[0156] 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.

[0157] In this invention, the server includes an input means for a user to input basic information about their studies, a generation means for receiving the input information and generating an individualized study plan using a generative AI model, a transmission means for transmitting the generated study plan to the user's device, a question providing means for generating and transmitting daily questions based on the user's progress, an answer determining means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report, a question answering means for receiving the user's questions and generating and transmitting answers using the generative AI model, a collection means for periodically collecting user data, and a formatting means for formatting the user's study plan for a communication application, thereby enabling customized study support for individual students, real-time feedback, and question answering.

[0158] "Input means" refers to a device or interface for a user to input basic information related to learning.

[0159] A "generator" is a device or algorithm that uses a generative AI model to generate an individualized learning plan based on learning information received from a user.

[0160] "Transmission means" refers to a device or function for transmitting the generated study plan, answer determination results, and visual report to the user's terminal.

[0161] A "question provider" is a device or algorithm that generates and sends daily study questions based on the user's progress.

[0162] An "answer determination means" is a device or algorithm for immediately determining an answer received from a user and transmitting the result.

[0163] "Analysis means" refers to a device or algorithm that analyzes past learning data and identifies the user's weaknesses.

[0164] A "remedial action provider" is a device or algorithm for generating and delivering a remedial action plan to address identified weaknesses.

[0165] A "monitoring means" is a device or algorithm for continuously monitoring a user's learning progress and generating and transmitting visual reports.

[0166] A "question answering means" is a device or algorithm for receiving a user's question and generating and transmitting an answer using a generative AI model.

[0167] The "collection means" is a device or algorithm for periodically collecting user learning data.

[0168] A "formatting means" is a device or algorithm for formatting the generated lesson plan into a format suitable for communication applications.

[0169] The present invention provides a learning support system that is customized for each student. This system is realized through a communication application that users use on a daily basis.

[0170] Customized learning plan design

[0171] Entering study information

[0172] 1. Users enter basic information such as their grade, preferred school, strong and weak subjects, and target score via a communication application. This can be done using a device such as a smartphone, tablet, or PC.

[0173] 2. The terminal sends the entered information to the server.

[0174] Plan Generation

[0175] 1. The server uses a generative AI model (e.g., GPT-4) based on the received information to generate a learning plan appropriate for each student.

[0176] 2. A generating means determines the schedule, material selection, etc., and formats the individual plan for the communication application.

[0177] Send Plan

[0178] 1. The transmission means transmits the generated study plan to the user's terminal.

[0179] Examples:

[0180] The user inputs their grade ("third year high school student"), their preferred school ("top university"), and their weak subject ("math"), and the server uses an AI algorithm to create a "math focus plan" and sends it to the user via a communication application.

[0181] Questions provided regularly every morning

[0182] Problem generation

[0183] 1. The server generates a problem set based on the study plan.

[0184] 2. The problem provision method selects problems of a level of difficulty that corresponds to the user's progress and level of understanding.

[0185] Submit a problem

[0186] 1. The server sends a question to the user's communication application at a set time every morning.

[0187] 2. The user checks the question on the communication application and submits the answer.

[0188] Receiving answers and judging

[0189] 1. The terminal sends the user's answer to the server.

[0190] 2. The server receives the answer and uses the judging means to determine whether it is correct or incorrect.

[0191] Examples:

[0192] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application. The user answers the problems and sends the answers via the communication application. The server automatically checks the answers and immediately sends the results.

[0193] AI support to overcome weaknesses

[0194] Analysis of learning results

[0195] 1. The server analyzes the user's past answer data and identifies weak points.

[0196] Supplementary lesson plan generation

[0197] 1. The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[0198] 2. A tutoring provider selects relevant questions and materials and sends them to the user's communication application.

[0199] Examples:

[0200] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics. The server then creates a supplementary study plan for electromagnetics and provides related questions and explanatory videos via a communication application.

[0201] Progress monitoring and feedback transfer

[0202] Collection of training data

[0203] 1. The server periodically collects the user's daily learning data.

[0204] Data analysis and feedback generation

[0205] 1. The server analyzes the collected data and evaluates the user's progress.

[0206] 2. Monitoring measures generate progress reports.

[0207] Send Feedback

[0208] 1. The transmission means transmits the generated progress report to the user via a communication application.

[0209] Examples:

[0210] The server analyzes the learning data for one week and graphs the level of understanding and progress. The server generates a weekly report every Monday and sends it to the user via a communication application.

[0211] Real-time question answering

[0212] Question received

[0213] 1. The user submits a question they are studying using a communication application.

[0214] 2. The device sends a query to the server.

[0215] Response Generation

[0216] 1. The server analyzes the received question using a generative AI model and generates an appropriate answer.

[0217] Send response

[0218] 1. The generated answer is returned to the communication application in real time by the transmitting means.

[0219] Examples:

[0220] A user sends a question via a communication application, such as "I don't know how to solve a quadratic equation." The server receives the question and uses AI to generate a detailed answer. The server then sends the answer in real time via the communication application and replies, "Try solving it by following the steps below."

[0221] Prompt Sentence Examples

[0222] 1. Lesson plan generation prompt:

[0223] "Generate a study plan for a high school senior who wants to attend a top university and whose weakest subject is math."

[0224] 2. AI Q&A prompt:

[0225] "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[0226] Through this, the system is able to provide personalized and effective learning support in real time.

[0227] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0228] Step 1:

[0229] The user starts the communication application and inputs basic information such as grade level, desired school, strong and weak subjects, target score, etc. This input information includes the user's learning background and goals.

[0230] Step 2:

[0231] The device sends the entered basic information to the server. This data is sent to the server in JSON format. Here, the entered basic information is used as the transmission data as is.

[0232] Step 3:

[0233] The server uses a generative AI model to generate an individualized learning plan based on the received basic information. Specifically, the server inputs a prompt statement (e.g., "Please generate a learning plan for a high school senior who is weak in math") into the generative AI model, which then generates an appropriate learning plan. The learning plan generated as the output includes a learning schedule and recommended learning materials.

[0234] Step 4:

[0235] The server sends the generated study plan to the user's terminal. The formatting means formats the study plan into a user-friendly format and transfers it to the user's terminal. The terminal then outputs the received study plan on the application's display screen.

[0236] Step 5:

[0237] The server generates a daily problem set based on the user's progress. Specifically, it uses the user's individual learning plan and past correct / incorrect data as input data and creates a problem set using an AI model. The problem set generated as output contains problems of a difficulty level appropriate to the user's level of understanding.

[0238] Step 6:

[0239] The server sends the generated problem set to the user's terminal at a fixed time every morning. A schedule management system is used so that the user can receive the problem. The terminal displays the received problem and notifies the user.

[0240] Step 7:

[0241] The user answers the received questions using a communication application, and the user's answers are entered using the application's input form.

[0242] Step 8:

[0243] The terminal transmits the user's answers to the server, which stores the transmitted answer data in its database.

[0244] Step 9:

[0245] The server immediately judges the received answers using a judgment method, automatically classifying the answers using a generative AI model and generating a correct / incorrect result, which includes feedback on correct and incorrect answers.

[0246] Step 10:

[0247] The server sends the result of the judgment to the user's device, which then displays the received feedback on the application and notifies the user.

[0248] Step 11:

[0249] The server analyzes the user's past answer data to identify weaknesses. Here, analytical means are used to find patterns in the past data to identify the user's weaknesses. The output includes information about the identified weaknesses.

[0250] Step 12:

[0251] The server generates a remedial plan based on the identified weaknesses. Using a generative AI model, the server generates a remedial plan based on a prompt such as, "Please generate a remedial plan for students who are weak in the electromagnetic field of physics." The remedial plan includes relevant questions and explanatory materials.

[0252] Step 13:

[0253] The server sends the supplementary lesson plan to the user's terminal, which receives the supplementary lesson plan and displays it in a manner that notifies the user.

[0254] Step 14:

[0255] The server periodically collects the user's daily learning data and continuously monitors their progress. It analyzes the collected data and generates a progress report for the user. This output includes graphs and charts that visualize the user's understanding and progress.

[0256] Step 15:

[0257] The server sends the generated progress report to the user's terminal, which displays the received report and notifies the user.

[0258] Step 16:

[0259] The user sends a question during the study using a communication application, and the terminal sends the question to the server.

[0260] Step 17:

[0261] The server analyzes the received question using a generative AI model and generates an appropriate answer. Specifically, it receives a prompt such as, "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[0262] Step 18:

[0263] The server sends the generated answers back to the user in real time via a communication application, where the user can check the answers and use them for learning.

[0264] The above is the specific processing flow in this system.

[0265] (Application example 1)

[0266] 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."

[0267] Conventional learning support systems have difficulty providing customized learning plans based on each student's learning progress and level of understanding. They also face challenges in providing individual questions, feedback, and answering questions in real time. This can prevent students from learning efficiently and can result in insufficient support for overcoming weaknesses in specific subjects or areas.

[0268] 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.

[0269] In this invention, the server includes: an input means for a user to input basic information related to their studies; a generation means for receiving the input information and generating an individualized study plan using an AI algorithm; a transmission means for transmitting the generated study plan to the user's terminal; a question providing means for generating and transmitting daily questions based on the user's progress; an answer determination means for determining answers received from the user and transmitting the results; an analysis means for analyzing past study data and identifying the user's weaknesses; a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses; a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report; a question answering means for receiving the user's questions and generating and transmitting answers using AI; a content providing means for delivering customized study content to the user based on a generative AI model; a notification means for generating a question set at a fixed time every morning and notifying the user; and an answer reception determination means for receiving the user's answers in real time and automatically determining them. This makes it possible to deliver learning plans and content optimized for each student, and by providing questions and feedback in real time according to individual progress, it is possible to improve learning efficiency and comprehension.

[0270] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[0271] A "generation method" is a process or system that uses AI algorithms to generate an individualized learning plan based on input information.

[0272] "Transmission means" refers to the communication technology or method for transmitting the generated learning plan and data to the user's terminal.

[0273] "Question providing means" refers to a function or system that generates and sends daily questions based on the user's progress.

[0274] An "answer determination means" is a process or algorithm for determining the answers received from the user and transmitting the results.

[0275] "Analysis means" refers to a method or system for analyzing past learning data and identifying the user's weaknesses.

[0276] A "remedial action delivery vehicle" is a process or system that generates and delivers a remedial action plan to address identified weaknesses.

[0277] "Monitoring means" refers to functions and methods for continuously monitoring a user's learning progress and generating and sending visual reports.

[0278] A "question answering tool" is a system or process that receives a user's question and uses AI to generate and send an answer.

[0279] "Content provision means" refers to the function or method for delivering customized learning content to users based on a generative AI model.

[0280] The "notification means" is a system or method for generating a problem set at a fixed time every morning and notifying the user.

[0281] The "answer reception and determination means" is a process or algorithm for receiving the user's answers in real time and automatically determining them.

[0282] This invention is a system that provides customized learning support to each student. This system has multiple functions and can be used by users using everyday devices such as smartphones. The main functions and their implementation modes are described below.

[0283] Entering study information

[0284] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[0285] Customized learning plan design

[0286] The server uses an AI algorithm to generate an individualized learning plan based on the received user information. The plan is then sent to the user's device, where the user can review it and work on their daily studies.

[0287] Questions provided regularly every morning

[0288] The server generates a set of questions based on the user's study plan and progress at a fixed time every morning and notifies the user's device. The user can then check the questions on the application and submit their answers.

[0289] Receiving answers and judging

[0290] The server receives the answers sent by the user in real time and automatically judges them. The results are immediately sent to the user's device, allowing the user to check their level of understanding as they continue their studies.

[0291] AI support to overcome weaknesses

[0292] The server analyzes past learning data to identify the user's weaknesses, then generates a supplementary study plan to overcome the identified weaknesses and provides the user with relevant learning materials and questions.

[0293] Progress monitoring and feedback transfer

[0294] The server continuously monitors the user's learning progress and generates visual reports, such as weekly or monthly progress reports, which are sent to the user's device, allowing the user to understand their own learning progress.

[0295] Real-time question answering

[0296] Users can send questions that arise during their studies to the server via a communication application. The server uses an AI algorithm to analyze the questions, generate appropriate answers in real time, and return them to the user.

[0297] Details of content delivery methods

[0298] The server delivers customized learning content to users based on generative AI models, making it possible to provide a learning experience optimized for each individual student, rather than the traditional one-size-fits-all approach.

[0299] Examples of prompt statements

[0300] "If a user inputs their preferred school as a 'top university' and their weakest subject as 'math', generate a 'math focus plan' based on the identified information and deliver it to them via a communication application. Also, deliver a set of math problems to the user every morning at 8:00, check the answers in real time, and provide feedback."

[0301] This system configuration enables learning support customized for each student, enabling efficient learning progress and improved understanding.

[0302] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0303] Step 1: Enter your study information

[0304] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[0305] Input: Grade, desired school, strong subjects, weak subjects, target score

[0306] Output: User basic information sent to the server

[0307] Step 2: Generate a customized study plan

[0308] The server uses an AI algorithm to generate an individual learning plan based on the received user information. Specifically, the generative AI model analyzes the user's profile and plans the optimal learning materials, schedule, and learning progress method.

[0309] Input: User basic information

[0310] Output: A customized study plan

[0311] Step 3: Submit your study plan

[0312] The server sends the generated study plan to the user's device, and the user can open the application from the notification and view the provided study plan.

[0313] Input: Customized Study Plan

[0314] Output: The learning plan displayed on the user's device

[0315] Step 4: Provide regular questions every morning

[0316] The server generates a set of questions every morning at a fixed time based on the user's study plan and progress, and notifies the user's device. The difficulty of the questions is adjusted according to the user's level of understanding.

[0317] Input: Study plan, progress data

[0318] Output: A set of questions to be sent to you every morning

[0319] Step 5: Submit your answers and have them graded

[0320] The user answers questions provided on the application. The answers are sent from the device to the server, which judges the answers in real time and provides immediate visual feedback on whether the answer is correct or incorrect.

[0321] Input: User's answer

[0322] Output: Feedback of correct / incorrect results

[0323] Step 6: AI support to overcome weaknesses

[0324] The server analyzes past learning data to identify the user's weaknesses, generates a remedial study plan based on the identified weaknesses, and provides the user with relevant learning materials and questions.

[0325] Input: Past training data

[0326] Output: Supplementary lesson plan, related questions and materials

[0327] Step 7: Progress monitoring and feedback

[0328] The server continuously monitors the user's learning progress and generates visual reports, for example, weekly or monthly progress reports, which are sent to the user's device.

[0329] Input: Learning progress data

[0330] Output: Visual report

[0331] Step 8: Real-time question answering

[0332] Users can send questions that arise during their studies to the server via a communication application, and the server will analyze the questions using an AI algorithm, generate appropriate answers in real time, and send them back to the user.

[0333] Input: User question

[0334] Output: AI-generated answer

[0335] Step 9: Content Delivery Methods

[0336] The server delivers customized learning content based on generative AI models, providing users with individually optimized learning materials and tests.

[0337] Input: Database of learning content, user characteristics

[0338] Output: Customized learning content

[0339] Step 10: Set a notification method every morning

[0340] The server generates a set of questions every morning at a fixed time and notifies the user's device. When the user receives the notification, they can open the application and check the questions.

[0341] Input: Study schedule, question database

[0342] Output: Problem notification every morning

[0343] Step 11: Answer reception determination means

[0344] When a user submits their answer, the server receives it in real time and automatically judges it, with the results immediately sent to the user's device.

[0345] Input: User's answer

[0346] Output: Feedback of the judgement result

[0347] 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.

[0348] This invention is a system that provides flexible and efficient learning support tailored to the user's emotional state by combining an emotion engine with an individual learning support system. This system is implemented via a communication application that the user uses on a daily basis. Below, we will show an embodiment of each of the main functions.

[0349] Customized learning plan design

[0350] 1. Enter your study information

[0351] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[0352] The terminal transmits the input information to the server.

[0353] 2. Plan Generation

[0354] The server uses AI algorithms based on the received information to generate a personalized learning plan for the user.

[0355] The generation means selects schedules and learning materials to create customized learning plans.

[0356] 3. Send plan

[0357] The transmission means transmits the generated study plan to the user's terminal.

[0358] Examples:

[0359] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[0360] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[0361] Questions provided regularly every morning

[0362] 1. Problem generation

[0363] The server generates a problem set based on the study plan.

[0364] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[0365] 2. Submit your question

[0366] The server sends the problem to the user's communication application at a set time each morning.

[0367] The user checks the questions on the communication application and inputs the answers.

[0368] 3. Receiving answers and judging

[0369] The terminal transmits the user's answer to the server.

[0370] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[0371] AI support to overcome weaknesses

[0372] 1. Analysis of learning results

[0373] The server collects the user's past answer data and uses analytical algorithms to identify weak spots.

[0374] 2. Supplementary lesson plan generation

[0375] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[0376] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[0377] Progress monitoring and feedback transfer

[0378] 1. Collecting training data

[0379] The server periodically collects the user's daily learning data (answers to questions, time, progress, etc.).

[0380] 2. Data analysis and feedback generation

[0381] The server analyzes the collected data and evaluates learning progress and level of understanding.

[0382] A monitoring means generates progress reports.

[0383] 3. Send Feedback

[0384] The transmission means transmits the generated progress report to the user via a communication application.

[0385] Real-time question answering

[0386] 1. Receiving questions

[0387] Users use a communication application to input and send questions or concerns they have about their studies.

[0388] 2. Response Generation

[0389] The server uses AI to analyze the received questions and generate appropriate answers.

[0390] 3. Submit your response

[0391] The sending means returns the generated answer to the communication application in real time.

[0392] Introducing the Emotion Engine

[0393] 1. Acquiring Emotion Data

[0394] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[0395] 2. Sentiment Analysis

[0396] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[0397] 3. Plan adjustment

[0398] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[0399] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[0400] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[0401] Examples:

[0402] If it is determined that the user is feeling stressed by the questions, the server provides easier questions based on that data to maintain the user's motivation.

[0403] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[0404] The above is a detailed description of an embodiment of the present invention. This system provides personalized learning support that takes into account the user's emotions, thereby realizing a more effective learning environment.

[0405] The processing flow will be explained below.

[0406] Customized learning plan design

[0407] Step 1:

[0408] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[0409] Step 2:

[0410] The terminal transmits the input user information to the server.

[0411] Step 3:

[0412] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[0413] The server selects the schedule and study materials and creates a customized learning plan.

[0414] Step 4:

[0415] The server formats the generated lesson plan and sends it to the communication application.

[0416] Questions provided regularly every morning

[0417] Step 1:

[0418] The server generates a problem set based on the user's study plan and progress.

[0419] Step 2:

[0420] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[0421] Step 3:

[0422] The server sends selected questions to the user's communication application at a set time every morning.

[0423] Step 4:

[0424] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[0425] Step 5:

[0426] The terminal sends the user's answer to the server.

[0427] Step 6:

[0428] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[0429] AI support to overcome weaknesses

[0430] Step 1:

[0431] The server collects the user's past answer data and learning outcomes and uses analytical algorithms to identify weak points.

[0432] Step 2:

[0433] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[0434] Step 3:

[0435] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[0436] Step 4:

[0437] The user advances his / her studies using the supplementary learning materials sent to him / her.

[0438] Progress monitoring and feedback transfer

[0439] Step 1:

[0440] The server periodically collects the user's daily learning data (answer results, time spent studying, progress, etc.).

[0441] Step 2:

[0442] The server analyzes the data collected and evaluates the progress of learning and level of understanding.

[0443] Step 3:

[0444] The server generates weekly and monthly progress reports.

[0445] Step 4:

[0446] The server formats the generated report and sends it to the communication application.

[0447] Step 5:

[0448] Users can check the reports and understand their own learning status.

[0449] Real-time question answering

[0450] Step 1:

[0451] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[0452] Step 2:

[0453] The terminal transmits the user's question data to the server.

[0454] Step 3:

[0455] The server receives the question data and uses AI to generate appropriate answers.

[0456] Step 4:

[0457] The server returns the generated answer to the communication application in real time.

[0458] Step 5:

[0459] The user checks the answer in the communication application and resolves the question.

[0460] Introducing the Emotion Engine

[0461] Step 1:

[0462] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[0463] Step 2:

[0464] The server uses emotion recognition means to analyze the user's emotions and identify their current emotional state.

[0465] Step 3:

[0466] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[0467] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[0468] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[0469] Examples:

[0470] If it is determined that the user is feeling stressed by the questions, the server will provide easier questions based on that data, maintaining the user's motivation to study.

[0471] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[0472] Example 2

[0473] 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."

[0474] Conventional learning support systems lack the ability to provide appropriate feedback based on individual users' progress and weaknesses, and are unable to flexibly adjust learning plans that take into account the user's emotional state. Furthermore, they lack the ability to provide real-time answer assessment and question response, making it difficult to maximize the user's motivation and efficiency in learning. These issues need to be addressed.

[0475] 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.

[0476] In this invention, the server includes a generation means for receiving input information and generating an individualized study plan using an AI algorithm, a transmission means for transmitting the generated study plan to the user's computer, a problem provision means for generating and transmitting daily problems based on the user's progress, an answer determination means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study provision means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, and an emotion engine means for acquiring and analyzing the user's emotional data and adjusting the study plan based on the user's current emotional state. This enables flexible adjustment of the study plan according to the individual's learning situation, real-time answer determination, and question response, thereby improving the user's learning efficiency and motivation.

[0477] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[0478] "Generation means" refers to a device or program that has the function of generating an individualized learning plan using an AI algorithm based on the information received.

[0479] "Transmission means" refers to a device or function for transmitting the generated lesson plan and other data to the user's computer.

[0480] A "problem provider" is a device or system that has the function of generating and transmitting daily problems based on the user's progress.

[0481] The "answer determination means" refers to a device or algorithm that has the function of analyzing the answer received from the user, determining whether it is correct or incorrect, and transmitting the result.

[0482] "Analysis means" refers to devices or algorithms that have the ability to analyze past learning data and identify the user's weaknesses.

[0483] The "tuition provision means" refers to a device or system for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[0484] "Monitoring means" refers to a device or system that has the ability to continuously monitor a user's learning progress and generate visual reports.

[0485] A "question answering means" is a device or system that has the function of receiving a user's question, using AI to generate an appropriate answer, and sending it.

[0486] The "emotion engine means" refers to a device or system that has the function of acquiring and analyzing the user's emotional data and adjusting the learning plan and content based on the user's emotional state.

[0487] This invention is a system that provides flexible and efficient learning support according to the user's emotional state by combining an emotion engine with an individual learning support system. Below, an embodiment of each main function is shown.

[0488] Customized learning plan design

[0489] 1. Enter your study information

[0490] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[0491] The terminal transmits the input information to the server.

[0492] 2. Create a learning plan

[0493] The server uses AI algorithms (e.g., TensorFlow, PyTorch) based on the received information to generate a personalized learning plan for the user.

[0494] The generation means selects schedules and learning materials to create customized learning plans.

[0495] 3. Submit your study plan

[0496] The server transmits the generated study plan to the user's terminal.

[0497] The user views the customized learning plan on the device.

[0498] Questions provided regularly every morning

[0499] 1. Problem Generation

[0500] The server generates daily problem sets based on the user's study plan.

[0501] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[0502] 2. Submit your issue

[0503] The server sends the problem to the user's communication application at a fixed time each morning.

[0504] The user checks the questions on the communication application and inputs the answers.

[0505] 3. Receiving and judging answers

[0506] The terminal transmits the user's answer to the server.

[0507] The server receives the answers and uses an AI algorithm to determine whether they are correct.

[0508] AI support to overcome weaknesses

[0509] 1. Analysis of learning results

[0510] The server collects the user's past answer data and uses analytical means to identify weak points.

[0511] 2. Generate a supplementary lesson plan

[0512] The server uses AI to generate a remedial plan to address identified weaknesses.

[0513] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[0514] Progress monitoring and feedback transfer

[0515] 1. Collecting training data

[0516] The server periodically collects the user's daily learning data (answers to questions, learning time, progress, etc.).

[0517] 2. Data analysis and feedback generation

[0518] The server analyzes the collected data and generates progress reports.

[0519] A monitoring means generates progress reports.

[0520] 3. Submitting Feedback

[0521] The server sends the generated progress report to the user via a communication application.

[0522] Real-time question answering

[0523] 1. Receiving Questions

[0524] Users use a communication application to input and send questions or concerns they have about their studies.

[0525] 2. Generating a Response

[0526] The server uses AI to analyze the received questions and generate appropriate answers.

[0527] 3. Submit your response

[0528] The server returns the generated answer to the communication application in real time.

[0529] Introducing the Emotion Engine

[0530] 1. Acquiring Emotion Data

[0531] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[0532] 2. Sentiment Analysis

[0533] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[0534] 3. Adjust your study plan

[0535] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[0536] If the user is feeling stressed, easy questions are provided, and if the user is feeling positive, more difficult questions are provided.

[0537] Specific examples

[0538] If a user inputs their grade as "third year high school student," their desired school as "top university," and their weak subject as "math," the server will generate a "math emphasis plan" and send it to the user via a communication application.

[0539] Every morning, the server sends the user a customized math problem, and the user enters and submits the answer.

[0540] The server judges the answer and provides real-time feedback on whether it is correct or incorrect.

[0541] Based on past data, the server identifies that "quadratic functions" is a weak point and sends a remedial plan specific to that area.

[0542] The server monitors learning progress and periodically sends progress reports to the user.

[0543] The server obtains the user's emotional data and adjusts the learning plan.

[0544] Example of input prompt for generative AI model

[0545] "I'm a third-year high school student and I'm hoping to get into a top university. I'm not good at math, but my strongest subject is English. My goal is to get 900 points on the National Center Test. Please provide me with a customized study plan."

[0546] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0547] Step 1:

[0548] Entering study information

[0549] The user uses a communication application to input basic information such as grade, desired school, favorite subjects, weak subjects, and target score.

[0550] The terminal collects the input data and sends it to a backend endpoint.

[0551] Input data: grade, desired school, strong subjects, weak subjects, target score

[0552] Output data: Data set of input information

[0553] Specific operation: The user enters information into the input form of the communication application and presses the "Send" button. The device then sends the data to the server's API as a POST request.

[0554] Step 2:

[0555] Generate a learning plan

[0556] The server uses AI algorithms to generate an individualized learning plan based on the information received.

[0557] AI algorithms (e.g., TensorFlow, PyTorch) analyze input data and select appropriate schedules and teaching materials.

[0558] Input data: Input information dataset

[0559] Output data: personalized learning plan

[0560] Specific operation: The server inputs the received data into an AI algorithm, analyzes it, and generates an optimal learning plan for the user. The generated plan is stored in a database.

[0561] Step 3:

[0562] Submit your study plan

[0563] The server transmits the generated study plan to the user's terminal.

[0564] The device will display the received learning plan so that it can be reviewed.

[0565] Input data: Individual learning plan

[0566] Output data: The learning plan displayed on the user's device

[0567] Specific operation: The server sends the generated study plan to the user's device, and the user checks the study plan using a communication application.

[0568] Step 4:

[0569] Daily problem provision

[0570] The server generates daily questions based on the user's progress.

[0571] Questions of appropriate difficulty are selected taking into account the user's past learning data and current learning plan.

[0572] Input data: User's learning plan, progress data

[0573] Output data: Generated daily problems

[0574] Specific operation: The server generates questions using an AI algorithm based on daily data and sends the questions to the user's device at the specified time.

[0575] Step 5:

[0576] Submitting questions and entering answers

[0577] The server sends questions to the user's device at a set time every morning.

[0578] The user answers the questions and enters the answers via a communication application.

[0579] Input data: Generated problems

[0580] Output data: User's answer data

[0581] Specific operation: The server sends the created questions to the user at 8:00 every morning, and the user enters the answers in the app.

[0582] Step 6:

[0583] Receiving answers and determining whether they are correct or incorrect

[0584] The terminal transmits the user's answer data to the server.

[0585] The server uses an AI algorithm to determine whether the answer is correct.

[0586] Input data: User's answer data

[0587] Output data: Correct / incorrect result

[0588] Specific operation: The server receives the answer data sent from the device, determines whether it is correct using an AI algorithm, and sends the result to the user.

[0589] Step 7:

[0590] Generate a remedial lesson plan to address weaknesses

[0591] The server analyzes past answer data and identifies the user's weaknesses.

[0592] AI generates a supplementary lesson plan based on identified weaknesses.

[0593] Input data: Answer data, analysis data

[0594] Output data: Supplementary lesson plan

[0595] Specific operation: The server analyzes past answer data, detects weaknesses, generates a supplementary study plan specific to the detected weaknesses, and sends it to the user's device.

[0596] Step 8:

[0597] Progress monitoring and feedback

[0598] The server periodically collects and analyzes the user's daily learning data and generates a progress report.

[0599] Send progress reports to the user's device.

[0600] Input data: Learning data (answer results, learning time, progress)

[0601] Output data: Progress report

[0602] Specific operation: The server collects learning data, analyzes it using AI algorithms, generates reports, and periodically sends them to the user's device.

[0603] Step 9:

[0604] Real-time question answering

[0605] The user inputs questions about the study through a communication application and sends them to the server.

[0606] The server uses AI to analyze the question and generate an appropriate answer.

[0607] Input data: Question content

[0608] Output data: The generated answer

[0609] How it works: Users submit questions via the app, the server uses AI to generate answers, and sends the answers to users in real time.

[0610] Step 10:

[0611] Introducing the Emotion Engine

[0612] The device uses a camera and microphone to capture the user's biometric signals and transmit them to the server.

[0613] The server uses emotion recognition means to analyze the emotion data and identify the current emotional state.

[0614] Adjust your study plan or problem difficulty based on your emotional state.

[0615] Input data: Emotion data (facial expressions, voice, etc.)

[0616] Output data: adjusted lesson plans and questions

[0617] How it works: The device collects the user's emotional data and sends it to the server, which analyzes the data to identify the user's emotional state and dynamically adjusts the learning plan and difficulty of the questions.

[0618] (Application example 2)

[0619] 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."

[0620] To improve employee productivity and motivation, it is important to properly understand employees' emotional states and provide appropriate work support. However, conventional systems lack emotional recognition capabilities and rely on rigid work procedures and task provision, making it difficult to reduce employee burden and stress. Furthermore, they lack real-time feedback and support, making it difficult to provide effective support in workplaces where immediate response is required.

[0621] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input basic information about learning, generation means for receiving the input information and generating an individualized learning plan using an AI algorithm, transmission means for transmitting the generated learning plan to the user's terminal, question provision means for generating and transmitting daily questions based on the user's progress, answer determination means for determining answers received from the user and transmitting the results, analysis means for analyzing past learning data and identifying the user's weaknesses, supplementary learning provision means for generating and transmitting supplementary learning plans to overcome the identified weaknesses, monitoring means for continuously monitoring the user's learning progress and generating and transmitting visual reports, question answering means for receiving the user's questions and generating and transmitting answers using AI, emotion recognition means for acquiring the user's biosignals via a communication application and analyzing their emotions, and plan adjustment means for adjusting the difficulty of the learning plan and the provided questions based on the emotional state analyzed by the emotion recognition means. This enables flexible work support according to the user's emotional state, improving employee productivity and motivation.

[0622] "Input means" refers to a device or mechanism that allows a user to input basic information related to learning.

[0623] "Generation means" refers to the function or process that uses an AI algorithm to generate an individual learning plan based on information received from the input means.

[0624] "Transmission means" refers to a function or device for transmitting the generated study plan or other information to the user's terminal.

[0625] "Question provision means" refers to the functions and processes for generating and providing daily questions based on the user's progress.

[0626] The "answer determination means" is a function or process for determining the answer received from the user and transmitting the result.

[0627] "Analysis means" refers to functions and processes for analyzing past learning data and identifying the user's weaknesses.

[0628] The "tuition provision means" is a function or process for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[0629] "Monitoring means" refers to the functions and processes that continuously monitor a user's learning progress and generate and send visual reports.

[0630] A "question answering means" is a function or process for receiving a user's question, generating an answer using AI, and sending it to the user.

[0631] The "emotion recognition means" refers to a function or process for acquiring a user's biosignal via a communication application and analyzing the user's emotions.

[0632] The "plan adjustment means" is a function or process for adjusting the difficulty of the study plan and the questions provided based on the emotional state analyzed by the emotion recognition means.

[0633] overview

[0634] This invention is a learning support system that takes the user's emotions into consideration, creating individual learning plans, providing regular quizzes, answering questions in real time, and adjusting plans based on emotions. This system can also be applied to work support in factories, providing flexible work support that responds to the emotions of employees.

[0635] composition

[0636] The system of the present invention is realized using the following hardware and software.

[0637] Hardware: Cameras, PCs, smartphones, tablets

[0638] Software: AI algorithms, emotion recognition models, communication applications

[0639] Program processing details

[0640] Enter basic information about your studies

[0641] The terminal accepts basic information about the user's studies (grade, desired school, favorite subjects, weak subjects, target score, etc.) entered by the user. This information is sent to the server via a communication application.

[0642] Generate a personalized learning plan

[0643] The server uses an AI algorithm to automatically generate a study plan based on the received user information, including a schedule and the selection of study materials.

[0644] Submit a plan

[0645] The generated learning plan is sent from the server to the terminal, where the user can view it via a communication application.

[0646] Daily problem provision

[0647] The server generates daily study tasks based on the user's progress and sends them to the user's device at a set time. The difficulty of the tasks is adjusted according to the user's progress and level of understanding.

[0648] Receiving and judging answers

[0649] The user answers the questions and sends the answers to the server via a communication application, which immediately judges the answers and returns the results to the user in real time.

[0650] Weakness analysis and supplementary study plan

[0651] The server analyzes past learning data to identify the user's weaknesses, and then generates and provides a supplementary study plan to the user to overcome those weaknesses.

[0652] Progress monitoring and feedback

[0653] The server continuously monitors the user's learning progress, generates visual reports, and periodically sends these to the user's device.

[0654] Real-time question answering

[0655] If a user has any questions during their studies, they can ask them in real time via a communication application, and the server will use AI to generate appropriate answers and send them back to the user.

[0656] Emotion recognition and plan adjustment

[0657] The device acquires biometric information such as the user's facial expressions and voice via a communication application. The server uses an emotion recognition model to analyze the user's emotions and adjusts the learning plan and difficulty of the questions provided based on the user's state.

[0658] Specific examples

[0659] For example, if a user feels stressed while studying for a regular exam, the device's camera captures their facial expression and sends it to the server, which then detects the stress and flexibly adjusts the study plan, providing easier questions to keep the user motivated.

[0660] Prompt Sentence Examples

[0661] For example, if a system designer is considering how to use a generative AI model to tailor a learning plan, they could use a prompt like this:

[0662] "In a factory work support system, I want to assign work tasks based on the results of employee emotion analysis. For example, if it is determined that an employee is feeling stressed, how can I provide them with easier work tasks?"

[0663] This enables flexible work support according to the user's emotional state, improving employee productivity and motivation.

[0664] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0665] Step 1:

[0666] The terminal accepts basic information about learning (grade, desired school, favorite subjects, weak subjects, target score, etc.) input by the user. This information is sent to the server via a communication application. The input data includes the user's basic information. The output is sent to the server.

[0667] Step 2:

[0668] The server processes the received user information and automatically generates a study plan using an AI algorithm. At this time, the server analyzes the user's basic information, which is input data, and selects the appropriate schedule and learning materials for the study plan. The output is a customized study plan.

[0669] Step 3:

[0670] The generated study plan is sent from the server to the terminal, where the user can check it via the communication application. The input data is the generated study plan, and the output is the plan sent to the user terminal.

[0671] Step 4:

[0672] The server generates daily study tasks based on the user's progress and sends them to the user's device at a set time. The difficulty of the questions is adjusted according to the user's progress and level of understanding. The input data includes progress and past answer data, and the daily study tasks are generated as output.

[0673] Step 5:

[0674] The user answers the questions and sends the answers to the server via a communication application. The input data is the user's answer, and the output is the answer sent to the server.

[0675] Step 6:

[0676] The server immediately judges the received answer and returns the result to the user in real time. The input data is the user's answer, and the output is the judgment result. Specific operations include analyzing whether the answer is correct or incorrect.

[0677] Step 7:

[0678] The server analyzes past training data and identifies the user's weaknesses. The input data is the past training data, and the output is the identified weaknesses. The server analyzes the data using an analysis algorithm.

[0679] Step 8:

[0680] The server generates a remedial plan to overcome the weaknesses and provides it to the user. The input data is the identified weaknesses, and the output is the remedial plan. The generated remedial plan is sent to the user's terminal.

[0681] Step 9:

[0682] The server continuously monitors the user's learning progress and generates and sends visual reports. The input data is daily learning data, and the output is visual reports. Progress is recorded and analyzed using monitoring methods.

[0683] Step 10:

[0684] If a user has a question during learning, they can ask it in real time via a communication application. The server uses AI to generate an appropriate answer and sends it back to the user. The input data is the user's question, and the output is the server's answer.

[0685] Step 11:

[0686] The device acquires biometric information such as the user's facial expression and voice via a communication application. The input data is the user's facial expression and voice, and the output is the biometric information.

[0687] Step 12:

[0688] The server uses an emotion recognition model to analyze the user's emotions and adjust the difficulty of the study plan and questions provided based on that state. The input data is the acquired biometric information, and the output is the adjusted study plan and the difficulty of the questions provided. Specifically, the AI ​​model analyzes emotions and adjusts the plan based on the emotional state.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] [Second embodiment]

[0693] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0704] 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."

[0705] This invention is a system that provides customized learning support to each student. This system is implemented via a communication application that users use on a daily basis. Below, we will show an embodiment of each of the main functions.

[0706] Customized learning plan design

[0707] 1. Enter your study information

[0708] Users input their basic information (grade, desired school, strong and weak subjects, target score, etc.) via a communication application.

[0709] The terminal transmits the input information to the server.

[0710] 2. Plan Generation

[0711] The server uses AI algorithms to generate a learning plan tailored to each student based on the information it receives.

[0712] A generating means determines the schedule, material selection, etc., and formats the individual plan for a communication application.

[0713] 3. Send plan

[0714] The transmission means transmits the generated study plan to the user's terminal.

[0715] Examples:

[0716] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[0717] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[0718] Questions provided regularly every morning

[0719] 1. Problem generation

[0720] The server generates a problem set based on the study plan.

[0721] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[0722] 2. Submit your question

[0723] The server sends the problem to the user's communication application at a set time each morning.

[0724] The user checks the question on the communication application and submits the answer.

[0725] 3. Receiving answers and judging

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

[0727] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[0728] Examples:

[0729] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application.

[0730] The user answers the questions and sends the answers via a communication application.

[0731] The server automatically checks the answers and immediately sends the results.

[0732] AI support to overcome weaknesses

[0733] 1. Analysis of learning results

[0734] The server analyzes the user's past answer data and identifies weak points.

[0735] 2. Supplementary lesson plan generation

[0736] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[0737] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[0738] Examples:

[0739] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics.

[0740] The server creates a supplementary lesson plan on electromagnetics and provides related questions and explanatory videos via a communication application.

[0741] Progress monitoring and feedback transfer

[0742] 1. Collecting training data

[0743] The server periodically collects the user's learning data (daily answer results, progress, etc.).

[0744] 2. Data analysis and feedback generation

[0745] The server analyzes the collected data and evaluates the user's progress.

[0746] A monitoring means generates progress reports.

[0747] 3. Send Feedback

[0748] The transmission means transmits the generated progress report to the user via a communication application.

[0749] Examples:

[0750] The server analyzes the learning data for one week and graphs the level of understanding and progress.

[0751] The server generates a weekly report every Monday and sends it to the user via a communication application.

[0752] Real-time question answering

[0753] 1. Receiving questions

[0754] Users use a communication application to submit questions they are studying.

[0755] The terminal sends a query to the server.

[0756] 2. Response Generation

[0757] The server uses AI to analyze the received questions and generate appropriate answers.

[0758] 3. Submit your response

[0759] The sending means returns the generated answer to the communication application in real time.

[0760] Examples:

[0761] A user sends a question via a communication application saying, "I don't know how to solve a quadratic equation."

[0762] The server receives the question and uses AI to generate a detailed answer.

[0763] The server sends the answer in real time via a communication application and replies, "Please try solving it by following the steps below."

[0764] The above is a detailed description of the embodiments of the invention based on the claims.

[0765] The processing flow will be explained below.

[0766] Customized learning plan design

[0767] Step 1:

[0768] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[0769] Step 2:

[0770] The terminal transmits the input user information to the server.

[0771] Step 3:

[0772] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[0773] The server selects the schedule and study materials and creates a customized learning plan.

[0774] Step 4:

[0775] The server formats the generated lesson plan and sends it to the communication application.

[0776] Questions provided regularly every morning

[0777] Step 1:

[0778] The server generates a problem set based on the user's study plan and progress.

[0779] Step 2:

[0780] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[0781] Step 3:

[0782] The server sends selected questions to the user's communication application at a set time every morning.

[0783] Step 4:

[0784] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[0785] Step 5:

[0786] The terminal sends the user's answer to the server.

[0787] Step 6:

[0788] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[0789] AI support to overcome weaknesses

[0790] Step 1:

[0791] The server collects the user's past answer data and uses analytical algorithms to identify weaknesses.

[0792] Step 2:

[0793] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[0794] Step 3:

[0795] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[0796] Step 4:

[0797] The user advances his / her studies using the supplementary learning materials sent to him / her.

[0798] Progress monitoring and feedback transfer

[0799] Step 1:

[0800] The server collects the user's daily learning data (answers to questions, time, progress, etc.).

[0801] Step 2:

[0802] The server analyzes the data collected and evaluates learning progress and level of understanding.

[0803] Step 3:

[0804] The server generates weekly and monthly progress reports.

[0805] Step 4:

[0806] The server formats the generated report and sends it to the communication application.

[0807] Step 5:

[0808] Users can check the reports and understand their own learning status.

[0809] Real-time question answering

[0810] Step 1:

[0811] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[0812] Step 2:

[0813] The terminal sends the user's question to the server.

[0814] Step 3:

[0815] The server receives the question and uses AI to generate an appropriate answer.

[0816] Step 4:

[0817] The server sends the generated answer in real time to the communication application.

[0818] Step 5:

[0819] The user checks the answer in the communication application and resolves the question.

[0820] The above are the specific steps of the program processing of this system.

[0821] Example 1

[0822] 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."

[0823] Conventional learning support systems have difficulty fully addressing the learning progress and weaknesses of individual students, and are limited to providing uniform learning plans and feedback. Furthermore, it is difficult to provide real-time question and answer sessions and progress reports, which prevents users from maintaining their motivation to learn and from providing effective learning support.

[0824] 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.

[0825] In this invention, the server includes an input means for a user to input basic information about their studies, a generation means for receiving the input information and generating an individualized study plan using a generative AI model, a transmission means for transmitting the generated study plan to the user's device, a question providing means for generating and transmitting daily questions based on the user's progress, an answer determining means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report, a question answering means for receiving the user's questions and generating and transmitting answers using the generative AI model, a collection means for periodically collecting user data, and a formatting means for formatting the user's study plan for a communication application, thereby enabling customized study support for individual students, real-time feedback, and question answering.

[0826] "Input means" refers to a device or interface for a user to input basic information related to learning.

[0827] A "generator" is a device or algorithm that uses a generative AI model to generate an individualized learning plan based on learning information received from a user.

[0828] "Transmission means" refers to a device or function for transmitting the generated study plan, answer determination results, and visual report to the user's terminal.

[0829] A "question provider" is a device or algorithm that generates and sends daily study questions based on the user's progress.

[0830] An "answer determination means" is a device or algorithm for immediately determining an answer received from a user and transmitting the result.

[0831] "Analysis means" refers to a device or algorithm that analyzes past learning data and identifies the user's weaknesses.

[0832] A "remedial action provider" is a device or algorithm for generating and delivering a remedial action plan to address identified weaknesses.

[0833] A "monitoring means" is a device or algorithm for continuously monitoring a user's learning progress and generating and transmitting visual reports.

[0834] A "question answering means" is a device or algorithm for receiving a user's question and generating and transmitting an answer using a generative AI model.

[0835] The "collection means" is a device or algorithm for periodically collecting user learning data.

[0836] A "formatting means" is a device or algorithm for formatting the generated lesson plan into a format suitable for communication applications.

[0837] The present invention provides a learning support system that is customized for each student. This system is realized through a communication application that users use on a daily basis.

[0838] Customized learning plan design

[0839] Entering study information

[0840] 1. Users enter basic information such as their grade, preferred school, strong and weak subjects, and target score via a communication application. This can be done using a device such as a smartphone, tablet, or PC.

[0841] 2. The terminal sends the entered information to the server.

[0842] Plan Generation

[0843] 1. The server uses a generative AI model (e.g., GPT-4) based on the received information to generate a learning plan appropriate for each student.

[0844] 2. A generating means determines the schedule, material selection, etc., and formats the individual plan for the communication application.

[0845] Send Plan

[0846] 1. The transmission means transmits the generated study plan to the user's terminal.

[0847] Examples:

[0848] The user inputs their grade ("third year high school student"), their preferred school ("top university"), and their weak subject ("math"), and the server uses an AI algorithm to create a "math focus plan" and sends it to the user via a communication application.

[0849] Questions provided regularly every morning

[0850] Problem generation

[0851] 1. The server generates a problem set based on the study plan.

[0852] 2. The problem provision method selects problems of a level of difficulty that corresponds to the user's progress and level of understanding.

[0853] Submit a problem

[0854] 1. The server sends a question to the user's communication application at a set time every morning.

[0855] 2. The user checks the question on the communication application and submits the answer.

[0856] Receiving answers and judging

[0857] 1. The terminal sends the user's answer to the server.

[0858] 2. The server receives the answer and uses the judging means to determine whether it is correct or incorrect.

[0859] Examples:

[0860] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application. The user answers the problems and sends the answers via the communication application. The server automatically checks the answers and immediately sends the results.

[0861] AI support to overcome weaknesses

[0862] Analysis of learning results

[0863] 1. The server analyzes the user's past answer data and identifies weak points.

[0864] Supplementary lesson plan generation

[0865] 1. The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[0866] 2. A tutoring provider selects relevant questions and materials and sends them to the user's communication application.

[0867] Examples:

[0868] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics. The server then creates a supplementary study plan for electromagnetics and provides related questions and explanatory videos via a communication application.

[0869] Progress monitoring and feedback transfer

[0870] Collection of training data

[0871] 1. The server periodically collects the user's daily learning data.

[0872] Data analysis and feedback generation

[0873] 1. The server analyzes the collected data and evaluates the user's progress.

[0874] 2. Monitoring measures generate progress reports.

[0875] Send Feedback

[0876] 1. The transmission means transmits the generated progress report to the user via a communication application.

[0877] Examples:

[0878] The server analyzes the learning data for one week and graphs the level of understanding and progress. The server generates a weekly report every Monday and sends it to the user via a communication application.

[0879] Real-time question answering

[0880] Question received

[0881] 1. The user submits a question they are studying using a communication application.

[0882] 2. The device sends a query to the server.

[0883] Response Generation

[0884] 1. The server analyzes the received question using a generative AI model and generates an appropriate answer.

[0885] Send response

[0886] 1. The generated answer is returned to the communication application in real time by the transmitting means.

[0887] Examples:

[0888] A user sends a question via a communication application, such as "I don't know how to solve a quadratic equation." The server receives the question and uses AI to generate a detailed answer. The server then sends the answer in real time via the communication application and replies, "Try solving it by following the steps below."

[0889] Prompt Sentence Examples

[0890] 1. Lesson plan generation prompt:

[0891] "Generate a study plan for a high school senior who wants to attend a top university and whose weakest subject is math."

[0892] 2. AI Q&A prompt:

[0893] "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[0894] Through this, the system is able to provide personalized and effective learning support in real time.

[0895] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0896] Step 1:

[0897] The user starts the communication application and inputs basic information such as grade level, desired school, strong and weak subjects, target score, etc. This input information includes the user's learning background and goals.

[0898] Step 2:

[0899] The device sends the entered basic information to the server. This data is sent to the server in JSON format. Here, the entered basic information is used as the transmission data as is.

[0900] Step 3:

[0901] The server uses a generative AI model to generate an individualized learning plan based on the received basic information. Specifically, the server inputs a prompt statement (e.g., "Please generate a learning plan for a high school senior who is weak in math") into the generative AI model, which then generates an appropriate learning plan. The learning plan generated as the output includes a learning schedule and recommended learning materials.

[0902] Step 4:

[0903] The server sends the generated study plan to the user's terminal. The formatting means formats the study plan into a user-friendly format and transfers it to the user's terminal. The terminal then outputs the received study plan on the application's display screen.

[0904] Step 5:

[0905] The server generates a daily problem set based on the user's progress. Specifically, it uses the user's individual learning plan and past correct / incorrect data as input data and creates a problem set using an AI model. The problem set generated as output contains problems of a difficulty level appropriate to the user's level of understanding.

[0906] Step 6:

[0907] The server sends the generated problem set to the user's terminal at a fixed time every morning. A schedule management system is used so that the user can receive the problem. The terminal displays the received problem and notifies the user.

[0908] Step 7:

[0909] The user answers the received questions using a communication application, and the user's answers are entered using the application's input form.

[0910] Step 8:

[0911] The terminal transmits the user's answers to the server, which stores the transmitted answer data in its database.

[0912] Step 9:

[0913] The server immediately judges the received answers using a judgment method, automatically classifying the answers using a generative AI model and generating a correct / incorrect result, which includes feedback on correct and incorrect answers.

[0914] Step 10:

[0915] The server sends the result of the judgment to the user's device, which then displays the received feedback on the application and notifies the user.

[0916] Step 11:

[0917] The server analyzes the user's past answer data to identify weaknesses. Here, analytical means are used to find patterns in the past data to identify the user's weaknesses. The output includes information about the identified weaknesses.

[0918] Step 12:

[0919] The server generates a remedial plan based on the identified weaknesses. Using a generative AI model, the server generates a remedial plan based on a prompt such as, "Please generate a remedial plan for students who are weak in the electromagnetic field of physics." The remedial plan includes relevant questions and explanatory materials.

[0920] Step 13:

[0921] The server sends the supplementary lesson plan to the user's terminal, which receives the supplementary lesson plan and displays it in a manner that notifies the user.

[0922] Step 14:

[0923] The server periodically collects the user's daily learning data and continuously monitors their progress. It analyzes the collected data and generates a progress report for the user. This output includes graphs and charts that visualize the user's understanding and progress.

[0924] Step 15:

[0925] The server sends the generated progress report to the user's terminal, which displays the received report and notifies the user.

[0926] Step 16:

[0927] The user sends a question during the study using a communication application, and the terminal sends the question to the server.

[0928] Step 17:

[0929] The server analyzes the received question using a generative AI model and generates an appropriate answer. Specifically, it receives a prompt such as, "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[0930] Step 18:

[0931] The server sends the generated answers back to the user in real time via a communication application, where the user can check the answers and use them for learning.

[0932] The above is the specific processing flow in this system.

[0933] (Application example 1)

[0934] 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."

[0935] Conventional learning support systems have difficulty providing customized learning plans based on each student's learning progress and level of understanding. They also face challenges in providing individual questions, feedback, and answering questions in real time. This can prevent students from learning efficiently and can result in insufficient support for overcoming weaknesses in specific subjects or areas.

[0936] 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.

[0937] In this invention, the server includes: an input means for a user to input basic information related to their studies; a generation means for receiving the input information and generating an individualized study plan using an AI algorithm; a transmission means for transmitting the generated study plan to the user's terminal; a question providing means for generating and transmitting daily questions based on the user's progress; an answer determination means for determining answers received from the user and transmitting the results; an analysis means for analyzing past study data and identifying the user's weaknesses; a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses; a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report; a question answering means for receiving the user's questions and generating and transmitting answers using AI; a content providing means for delivering customized study content to the user based on a generative AI model; a notification means for generating a question set at a fixed time every morning and notifying the user; and an answer reception determination means for receiving the user's answers in real time and automatically determining them. This makes it possible to deliver learning plans and content optimized for each student, and by providing questions and feedback in real time according to individual progress, it is possible to improve learning efficiency and comprehension.

[0938] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[0939] A "generation method" is a process or system that uses AI algorithms to generate an individualized learning plan based on input information.

[0940] "Transmission means" refers to the communication technology or method for transmitting the generated learning plan and data to the user's terminal.

[0941] "Question providing means" refers to a function or system that generates and sends daily questions based on the user's progress.

[0942] An "answer determination means" is a process or algorithm for determining the answers received from the user and transmitting the results.

[0943] "Analysis means" refers to a method or system for analyzing past learning data and identifying the user's weaknesses.

[0944] A "remedial action delivery vehicle" is a process or system that generates and delivers a remedial action plan to address identified weaknesses.

[0945] "Monitoring means" refers to functions and methods for continuously monitoring a user's learning progress and generating and sending visual reports.

[0946] A "question answering tool" is a system or process that receives a user's question and uses AI to generate and send an answer.

[0947] "Content provision means" refers to the function or method for delivering customized learning content to users based on a generative AI model.

[0948] The "notification means" is a system or method for generating a problem set at a fixed time every morning and notifying the user.

[0949] The "answer reception and determination means" is a process or algorithm for receiving the user's answers in real time and automatically determining them.

[0950] This invention is a system that provides customized learning support to each student. This system has multiple functions and can be used by users using everyday devices such as smartphones. The main functions and their implementation modes are described below.

[0951] Entering study information

[0952] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[0953] Customized learning plan design

[0954] The server uses an AI algorithm to generate an individualized learning plan based on the received user information. The plan is then sent to the user's device, where the user can review it and work on their daily studies.

[0955] Questions provided regularly every morning

[0956] The server generates a set of questions based on the user's study plan and progress at a fixed time every morning and notifies the user's device. The user can then check the questions on the application and submit their answers.

[0957] Receiving answers and judging

[0958] The server receives the answers sent by the user in real time and automatically judges them. The results are immediately sent to the user's device, allowing the user to check their level of understanding as they continue their studies.

[0959] AI support to overcome weaknesses

[0960] The server analyzes past learning data to identify the user's weaknesses, then generates a supplementary study plan to overcome the identified weaknesses and provides the user with relevant learning materials and questions.

[0961] Progress monitoring and feedback transfer

[0962] The server continuously monitors the user's learning progress and generates visual reports, such as weekly or monthly progress reports, which are sent to the user's device, allowing the user to understand their own learning progress.

[0963] Real-time question answering

[0964] Users can send questions that arise during their studies to the server via a communication application. The server uses an AI algorithm to analyze the questions, generate appropriate answers in real time, and return them to the user.

[0965] Details of content delivery methods

[0966] The server delivers customized learning content to users based on generative AI models, making it possible to provide a learning experience optimized for each individual student, rather than the traditional one-size-fits-all approach.

[0967] Examples of prompt statements

[0968] "If a user inputs their preferred school as a 'top university' and their weakest subject as 'math', generate a 'math focus plan' based on the identified information and deliver it to them via a communication application. Also, deliver a set of math problems to the user every morning at 8:00, check the answers in real time, and provide feedback."

[0969] This system configuration enables learning support customized for each student, enabling efficient learning progress and improved understanding.

[0970] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0971] Step 1: Enter your study information

[0972] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[0973] Input: Grade, desired school, strong subjects, weak subjects, target score

[0974] Output: User basic information sent to the server

[0975] Step 2: Generate a customized study plan

[0976] The server uses an AI algorithm to generate an individual learning plan based on the received user information. Specifically, the generative AI model analyzes the user's profile and plans the optimal learning materials, schedule, and learning progress method.

[0977] Input: User basic information

[0978] Output: A customized study plan

[0979] Step 3: Submit your study plan

[0980] The server sends the generated study plan to the user's device, and the user can open the application from the notification and view the provided study plan.

[0981] Input: Customized Study Plan

[0982] Output: The learning plan displayed on the user's device

[0983] Step 4: Provide regular questions every morning

[0984] The server generates a set of questions every morning at a fixed time based on the user's study plan and progress, and notifies the user's device. The difficulty of the questions is adjusted according to the user's level of understanding.

[0985] Input: Study plan, progress data

[0986] Output: A set of questions to be sent to you every morning

[0987] Step 5: Submit your answers and have them graded

[0988] The user answers questions provided on the application. The answers are sent from the device to the server, which judges the answers in real time and provides immediate visual feedback on whether the answer is correct or incorrect.

[0989] Input: User's answer

[0990] Output: Feedback of correct / incorrect results

[0991] Step 6: AI support to overcome weaknesses

[0992] The server analyzes past learning data to identify the user's weaknesses, generates a remedial study plan based on the identified weaknesses, and provides the user with relevant learning materials and questions.

[0993] Input: Past training data

[0994] Output: Supplementary lesson plan, related questions and materials

[0995] Step 7: Progress monitoring and feedback

[0996] The server continuously monitors the user's learning progress and generates visual reports, for example, weekly or monthly progress reports, which are sent to the user's device.

[0997] Input: Learning progress data

[0998] Output: Visual report

[0999] Step 8: Real-time question answering

[1000] Users can send questions that arise during their studies to the server via a communication application, and the server will analyze the questions using an AI algorithm, generate appropriate answers in real time, and send them back to the user.

[1001] Input: User question

[1002] Output: AI-generated answer

[1003] Step 9: Content Delivery Methods

[1004] The server delivers customized learning content based on generative AI models, providing users with individually optimized learning materials and tests.

[1005] Input: Database of learning content, user characteristics

[1006] Output: Customized learning content

[1007] Step 10: Set a notification method every morning

[1008] The server generates a set of questions every morning at a fixed time and notifies the user's device. When the user receives the notification, they can open the application and check the questions.

[1009] Input: Study schedule, question database

[1010] Output: Problem notification every morning

[1011] Step 11: Answer reception determination means

[1012] When a user submits their answer, the server receives it in real time and automatically judges it, with the results immediately sent to the user's device.

[1013] Input: User's answer

[1014] Output: Feedback of the judgement result

[1015] 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.

[1016] This invention is a system that provides flexible and efficient learning support tailored to the user's emotional state by combining an emotion engine with an individual learning support system. This system is implemented via a communication application that the user uses on a daily basis. Below, we will show an embodiment of each of the main functions.

[1017] Customized learning plan design

[1018] 1. Enter your study information

[1019] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[1020] The terminal transmits the input information to the server.

[1021] 2. Plan Generation

[1022] The server uses AI algorithms based on the received information to generate a personalized learning plan for the user.

[1023] The generation means selects schedules and learning materials to create customized learning plans.

[1024] 3. Send plan

[1025] The transmission means transmits the generated study plan to the user's terminal.

[1026] Examples:

[1027] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[1028] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[1029] Questions provided regularly every morning

[1030] 1. Problem generation

[1031] The server generates a problem set based on the study plan.

[1032] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[1033] 2. Submit your question

[1034] The server sends the problem to the user's communication application at a set time each morning.

[1035] The user checks the questions on the communication application and inputs the answers.

[1036] 3. Receiving answers and judging

[1037] The terminal transmits the user's answer to the server.

[1038] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[1039] AI support to overcome weaknesses

[1040] 1. Analysis of learning results

[1041] The server collects the user's past answer data and uses analytical algorithms to identify weak spots.

[1042] 2. Supplementary lesson plan generation

[1043] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[1044] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[1045] Progress monitoring and feedback transfer

[1046] 1. Collecting training data

[1047] The server periodically collects the user's daily learning data (answers to questions, time, progress, etc.).

[1048] 2. Data analysis and feedback generation

[1049] The server analyzes the collected data and evaluates learning progress and level of understanding.

[1050] A monitoring means generates progress reports.

[1051] 3. Send Feedback

[1052] The transmission means transmits the generated progress report to the user via a communication application.

[1053] Real-time question answering

[1054] 1. Receiving questions

[1055] Users use a communication application to input and send questions or concerns they have about their studies.

[1056] 2. Response Generation

[1057] The server uses AI to analyze the received questions and generate appropriate answers.

[1058] 3. Submit your response

[1059] The sending means returns the generated answer to the communication application in real time.

[1060] Introducing the Emotion Engine

[1061] 1. Acquiring Emotion Data

[1062] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[1063] 2. Sentiment Analysis

[1064] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[1065] 3. Plan adjustment

[1066] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[1067] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[1068] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[1069] Examples:

[1070] If it is determined that the user is feeling stressed by the questions, the server provides easier questions based on that data to maintain the user's motivation.

[1071] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[1072] The above is a detailed description of an embodiment of the present invention. This system provides personalized learning support that takes into account the user's emotions, thereby realizing a more effective learning environment.

[1073] The processing flow will be explained below.

[1074] Customized learning plan design

[1075] Step 1:

[1076] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[1077] Step 2:

[1078] The terminal transmits the input user information to the server.

[1079] Step 3:

[1080] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[1081] The server selects the schedule and study materials and creates a customized learning plan.

[1082] Step 4:

[1083] The server formats the generated lesson plan and sends it to the communication application.

[1084] Questions provided regularly every morning

[1085] Step 1:

[1086] The server generates a problem set based on the user's study plan and progress.

[1087] Step 2:

[1088] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[1089] Step 3:

[1090] The server sends selected questions to the user's communication application at a set time every morning.

[1091] Step 4:

[1092] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[1093] Step 5:

[1094] The terminal sends the user's answer to the server.

[1095] Step 6:

[1096] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[1097] AI support to overcome weaknesses

[1098] Step 1:

[1099] The server collects the user's past answer data and learning outcomes and uses analytical algorithms to identify weak points.

[1100] Step 2:

[1101] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[1102] Step 3:

[1103] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[1104] Step 4:

[1105] The user advances his / her studies using the supplementary learning materials sent to him / her.

[1106] Progress monitoring and feedback transfer

[1107] Step 1:

[1108] The server periodically collects the user's daily learning data (answer results, time spent studying, progress, etc.).

[1109] Step 2:

[1110] The server analyzes the data collected and evaluates the progress of learning and level of understanding.

[1111] Step 3:

[1112] The server generates weekly and monthly progress reports.

[1113] Step 4:

[1114] The server formats the generated report and sends it to the communication application.

[1115] Step 5:

[1116] Users can check the reports and understand their own learning status.

[1117] Real-time question answering

[1118] Step 1:

[1119] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[1120] Step 2:

[1121] The terminal transmits the user's question data to the server.

[1122] Step 3:

[1123] The server receives the question data and uses AI to generate appropriate answers.

[1124] Step 4:

[1125] The server returns the generated answer to the communication application in real time.

[1126] Step 5:

[1127] The user checks the answer in the communication application and resolves the question.

[1128] Introducing the Emotion Engine

[1129] Step 1:

[1130] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[1131] Step 2:

[1132] The server uses emotion recognition means to analyze the user's emotions and identify their current emotional state.

[1133] Step 3:

[1134] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[1135] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[1136] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[1137] Examples:

[1138] If it is determined that the user is feeling stressed by the questions, the server will provide easier questions based on that data, maintaining the user's motivation to study.

[1139] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[1140] Example 2

[1141] 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."

[1142] Conventional learning support systems lack the ability to provide appropriate feedback based on individual users' progress and weaknesses, and are unable to flexibly adjust learning plans that take into account the user's emotional state. Furthermore, they lack the ability to provide real-time answer assessment and question response, making it difficult to maximize the user's motivation and efficiency in learning. These issues need to be addressed.

[1143] 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.

[1144] In this invention, the server includes a generation means for receiving input information and generating an individualized study plan using an AI algorithm, a transmission means for transmitting the generated study plan to the user's computer, a problem provision means for generating and transmitting daily problems based on the user's progress, an answer determination means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study provision means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, and an emotion engine means for acquiring and analyzing the user's emotional data and adjusting the study plan based on the user's current emotional state. This enables flexible adjustment of the study plan according to the individual's learning situation, real-time answer determination, and question response, thereby improving the user's learning efficiency and motivation.

[1145] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[1146] "Generation means" refers to a device or program that has the function of generating an individualized learning plan using an AI algorithm based on the information received.

[1147] "Transmission means" refers to a device or function for transmitting the generated lesson plan and other data to the user's computer.

[1148] A "problem provider" is a device or system that has the function of generating and transmitting daily problems based on the user's progress.

[1149] The "answer determination means" refers to a device or algorithm that has the function of analyzing the answer received from the user, determining whether it is correct or incorrect, and transmitting the result.

[1150] "Analysis means" refers to devices or algorithms that have the ability to analyze past learning data and identify the user's weaknesses.

[1151] The "tuition provision means" refers to a device or system for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[1152] "Monitoring means" refers to a device or system that has the ability to continuously monitor a user's learning progress and generate visual reports.

[1153] A "question answering means" is a device or system that has the function of receiving a user's question, using AI to generate an appropriate answer, and sending it.

[1154] The "emotion engine means" refers to a device or system that has the function of acquiring and analyzing the user's emotional data and adjusting the learning plan and content based on the user's emotional state.

[1155] This invention is a system that provides flexible and efficient learning support according to the user's emotional state by combining an emotion engine with an individual learning support system. Below, an embodiment of each main function is shown.

[1156] Customized learning plan design

[1157] 1. Enter your study information

[1158] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[1159] The terminal transmits the input information to the server.

[1160] 2. Create a learning plan

[1161] The server uses AI algorithms (e.g., TensorFlow, PyTorch) based on the received information to generate a personalized learning plan for the user.

[1162] The generation means selects schedules and learning materials to create customized learning plans.

[1163] 3. Submit your study plan

[1164] The server transmits the generated study plan to the user's terminal.

[1165] The user views the customized learning plan on the device.

[1166] Questions provided regularly every morning

[1167] 1. Problem Generation

[1168] The server generates daily problem sets based on the user's study plan.

[1169] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[1170] 2. Submit your issue

[1171] The server sends the problem to the user's communication application at a fixed time each morning.

[1172] The user checks the questions on the communication application and inputs the answers.

[1173] 3. Receiving and judging answers

[1174] The terminal transmits the user's answer to the server.

[1175] The server receives the answers and uses an AI algorithm to determine whether they are correct.

[1176] AI support to overcome weaknesses

[1177] 1. Analysis of learning results

[1178] The server collects the user's past answer data and uses analytical means to identify weak points.

[1179] 2. Generate a supplementary lesson plan

[1180] The server uses AI to generate a remedial plan to address identified weaknesses.

[1181] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[1182] Progress monitoring and feedback transfer

[1183] 1. Collecting training data

[1184] The server periodically collects the user's daily learning data (answers to questions, learning time, progress, etc.).

[1185] 2. Data analysis and feedback generation

[1186] The server analyzes the collected data and generates progress reports.

[1187] A monitoring means generates progress reports.

[1188] 3. Submitting Feedback

[1189] The server sends the generated progress report to the user via a communication application.

[1190] Real-time question answering

[1191] 1. Receiving Questions

[1192] Users use a communication application to input and send questions or concerns they have about their studies.

[1193] 2. Generating a Response

[1194] The server uses AI to analyze the received questions and generate appropriate answers.

[1195] 3. Submit your response

[1196] The server returns the generated answer to the communication application in real time.

[1197] Introducing the Emotion Engine

[1198] 1. Acquiring Emotion Data

[1199] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[1200] 2. Sentiment Analysis

[1201] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[1202] 3. Adjust your study plan

[1203] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[1204] If the user is feeling stressed, easy questions are provided, and if the user is feeling positive, more difficult questions are provided.

[1205] Specific examples

[1206] If a user inputs their grade as "third year high school student," their desired school as "top university," and their weak subject as "math," the server will generate a "math emphasis plan" and send it to the user via a communication application.

[1207] Every morning, the server sends the user a customized math problem, and the user enters and submits the answer.

[1208] The server judges the answer and provides real-time feedback on whether it is correct or incorrect.

[1209] Based on past data, the server identifies that "quadratic functions" is a weak point and sends a remedial plan specific to that area.

[1210] The server monitors learning progress and periodically sends progress reports to the user.

[1211] The server obtains the user's emotional data and adjusts the learning plan.

[1212] Example of input prompt for generative AI model

[1213] "I'm a third-year high school student and I'm hoping to get into a top university. I'm not good at math, but my strongest subject is English. My goal is to get 900 points on the National Center Test. Please provide me with a customized study plan."

[1214] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1215] Step 1:

[1216] Entering study information

[1217] The user uses a communication application to input basic information such as grade, desired school, favorite subjects, weak subjects, and target score.

[1218] The terminal collects the input data and sends it to a backend endpoint.

[1219] Input data: grade, desired school, strong subjects, weak subjects, target score

[1220] Output data: Data set of input information

[1221] Specific operation: The user enters information into the input form of the communication application and presses the "Send" button. The device then sends the data to the server's API as a POST request.

[1222] Step 2:

[1223] Generate a learning plan

[1224] The server uses AI algorithms to generate an individualized learning plan based on the information received.

[1225] AI algorithms (e.g., TensorFlow, PyTorch) analyze input data and select appropriate schedules and teaching materials.

[1226] Input data: Input information dataset

[1227] Output data: personalized learning plan

[1228] Specific operation: The server inputs the received data into an AI algorithm, analyzes it, and generates an optimal learning plan for the user. The generated plan is stored in a database.

[1229] Step 3:

[1230] Submit your study plan

[1231] The server transmits the generated study plan to the user's terminal.

[1232] The device will display the received learning plan so that it can be reviewed.

[1233] Input data: Individual learning plan

[1234] Output data: The learning plan displayed on the user's device

[1235] Specific operation: The server sends the generated study plan to the user's device, and the user checks the study plan using a communication application.

[1236] Step 4:

[1237] Daily problem provision

[1238] The server generates daily questions based on the user's progress.

[1239] Questions of appropriate difficulty are selected taking into account the user's past learning data and current learning plan.

[1240] Input data: User's learning plan, progress data

[1241] Output data: Generated daily problems

[1242] Specific operation: The server generates questions using an AI algorithm based on daily data and sends the questions to the user's device at the specified time.

[1243] Step 5:

[1244] Submitting questions and entering answers

[1245] The server sends questions to the user's device at a set time every morning.

[1246] The user answers the questions and enters the answers via a communication application.

[1247] Input data: Generated problems

[1248] Output data: User's answer data

[1249] Specific operation: The server sends the created questions to the user at 8:00 every morning, and the user enters the answers in the app.

[1250] Step 6:

[1251] Receiving answers and determining whether they are correct or incorrect

[1252] The terminal transmits the user's answer data to the server.

[1253] The server uses an AI algorithm to determine whether the answer is correct.

[1254] Input data: User's answer data

[1255] Output data: Correct / incorrect result

[1256] Specific operation: The server receives the answer data sent from the device, determines whether it is correct using an AI algorithm, and sends the result to the user.

[1257] Step 7:

[1258] Generate a remedial lesson plan to address weaknesses

[1259] The server analyzes past answer data and identifies the user's weaknesses.

[1260] AI generates a supplementary lesson plan based on identified weaknesses.

[1261] Input data: Answer data, analysis data

[1262] Output data: Supplementary lesson plan

[1263] Specific operation: The server analyzes past answer data, detects weaknesses, generates a supplementary study plan specific to the detected weaknesses, and sends it to the user's device.

[1264] Step 8:

[1265] Progress monitoring and feedback

[1266] The server periodically collects and analyzes the user's daily learning data and generates a progress report.

[1267] Send progress reports to the user's device.

[1268] Input data: Learning data (answer results, learning time, progress)

[1269] Output data: Progress report

[1270] Specific operation: The server collects learning data, analyzes it using AI algorithms, generates reports, and periodically sends them to the user's device.

[1271] Step 9:

[1272] Real-time question answering

[1273] The user inputs questions about the study through a communication application and sends them to the server.

[1274] The server uses AI to analyze the question and generate an appropriate answer.

[1275] Input data: Question content

[1276] Output data: The generated answer

[1277] How it works: Users submit questions via the app, the server uses AI to generate answers, and sends the answers to users in real time.

[1278] Step 10:

[1279] Introducing the Emotion Engine

[1280] The device uses a camera and microphone to capture the user's biometric signals and transmit them to the server.

[1281] The server uses emotion recognition means to analyze the emotion data and identify the current emotional state.

[1282] Adjust your study plan or problem difficulty based on your emotional state.

[1283] Input data: Emotion data (facial expressions, voice, etc.)

[1284] Output data: adjusted lesson plans and questions

[1285] How it works: The device collects the user's emotional data and sends it to the server, which analyzes the data to identify the user's emotional state and dynamically adjusts the learning plan and difficulty of the questions.

[1286] (Application example 2)

[1287] 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."

[1288] To improve employee productivity and motivation, it is important to properly understand employees' emotional states and provide appropriate work support. However, conventional systems lack emotional recognition capabilities and rely on rigid work procedures and task provision, making it difficult to reduce employee burden and stress. Furthermore, they lack real-time feedback and support, making it difficult to provide effective support in workplaces where immediate response is required.

[1289] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input basic information about learning, generation means for receiving the input information and generating an individualized learning plan using an AI algorithm, transmission means for transmitting the generated learning plan to the user's terminal, question provision means for generating and transmitting daily questions based on the user's progress, answer determination means for determining answers received from the user and transmitting the results, analysis means for analyzing past learning data and identifying the user's weaknesses, supplementary learning provision means for generating and transmitting supplementary learning plans to overcome the identified weaknesses, monitoring means for continuously monitoring the user's learning progress and generating and transmitting visual reports, question answering means for receiving the user's questions and generating and transmitting answers using AI, emotion recognition means for acquiring the user's biosignals via a communication application and analyzing their emotions, and plan adjustment means for adjusting the difficulty of the learning plan and the provided questions based on the emotional state analyzed by the emotion recognition means. This enables flexible work support according to the user's emotional state, improving employee productivity and motivation.

[1290] "Input means" refers to a device or mechanism that allows a user to input basic information related to learning.

[1291] "Generation means" refers to the function or process that uses an AI algorithm to generate an individual learning plan based on information received from the input means.

[1292] "Transmission means" refers to a function or device for transmitting the generated study plan or other information to the user's terminal.

[1293] "Question provision means" refers to the functions and processes for generating and providing daily questions based on the user's progress.

[1294] The "answer determination means" is a function or process for determining the answer received from the user and transmitting the result.

[1295] "Analysis means" refers to functions and processes for analyzing past learning data and identifying the user's weaknesses.

[1296] The "tuition provision means" is a function or process for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[1297] "Monitoring means" refers to the functions and processes that continuously monitor a user's learning progress and generate and send visual reports.

[1298] A "question answering means" is a function or process for receiving a user's question, generating an answer using AI, and sending it to the user.

[1299] The "emotion recognition means" refers to a function or process for acquiring a user's biosignal via a communication application and analyzing the user's emotions.

[1300] The "plan adjustment means" is a function or process for adjusting the difficulty of the study plan and the questions provided based on the emotional state analyzed by the emotion recognition means.

[1301] overview

[1302] This invention is a learning support system that takes the user's emotions into consideration, creating individual learning plans, providing regular quizzes, answering questions in real time, and adjusting plans based on emotions. This system can also be applied to work support in factories, providing flexible work support that responds to the emotions of employees.

[1303] composition

[1304] The system of the present invention is realized using the following hardware and software.

[1305] Hardware: Cameras, PCs, smartphones, tablets

[1306] Software: AI algorithms, emotion recognition models, communication applications

[1307] Program processing details

[1308] Enter basic information about your studies

[1309] The terminal accepts basic information about the user's studies (grade, desired school, favorite subjects, weak subjects, target score, etc.) entered by the user. This information is sent to the server via a communication application.

[1310] Generate a personalized learning plan

[1311] The server uses an AI algorithm to automatically generate a study plan based on the received user information, including a schedule and the selection of study materials.

[1312] Submit a plan

[1313] The generated learning plan is sent from the server to the terminal, where the user can view it via a communication application.

[1314] Daily problem provision

[1315] The server generates daily study tasks based on the user's progress and sends them to the user's device at a set time. The difficulty of the tasks is adjusted according to the user's progress and level of understanding.

[1316] Receiving and judging answers

[1317] The user answers the questions and sends the answers to the server via a communication application, which immediately judges the answers and returns the results to the user in real time.

[1318] Weakness analysis and supplementary study plan

[1319] The server analyzes past learning data to identify the user's weaknesses, and then generates and provides a supplementary study plan to the user to overcome those weaknesses.

[1320] Progress monitoring and feedback

[1321] The server continuously monitors the user's learning progress, generates visual reports, and periodically sends these to the user's device.

[1322] Real-time question answering

[1323] If a user has any questions during their studies, they can ask them in real time via a communication application, and the server will use AI to generate appropriate answers and send them back to the user.

[1324] Emotion recognition and plan adjustment

[1325] The device acquires biometric information such as the user's facial expressions and voice via a communication application. The server uses an emotion recognition model to analyze the user's emotions and adjusts the learning plan and difficulty of the questions provided based on the user's state.

[1326] Specific examples

[1327] For example, if a user feels stressed while studying for a regular exam, the device's camera captures their facial expression and sends it to the server, which then detects the stress and flexibly adjusts the study plan, providing easier questions to keep the user motivated.

[1328] Prompt Sentence Examples

[1329] For example, if a system designer is considering how to use a generative AI model to tailor a learning plan, they could use a prompt like this:

[1330] "In a factory work support system, I want to assign work tasks based on the results of employee emotion analysis. For example, if it is determined that an employee is feeling stressed, how can I provide them with easier work tasks?"

[1331] This enables flexible work support according to the user's emotional state, improving employee productivity and motivation.

[1332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1333] Step 1:

[1334] The terminal accepts basic information about learning (grade, desired school, favorite subjects, weak subjects, target score, etc.) input by the user. This information is sent to the server via a communication application. The input data includes the user's basic information. The output is sent to the server.

[1335] Step 2:

[1336] The server processes the received user information and automatically generates a study plan using an AI algorithm. At this time, the server analyzes the user's basic information, which is input data, and selects the appropriate schedule and learning materials for the study plan. The output is a customized study plan.

[1337] Step 3:

[1338] The generated study plan is sent from the server to the terminal, where the user can check it via the communication application. The input data is the generated study plan, and the output is the plan sent to the user terminal.

[1339] Step 4:

[1340] The server generates daily study tasks based on the user's progress and sends them to the user's device at a set time. The difficulty of the questions is adjusted according to the user's progress and level of understanding. The input data includes progress and past answer data, and the daily study tasks are generated as output.

[1341] Step 5:

[1342] The user answers the questions and sends the answers to the server via a communication application. The input data is the user's answer, and the output is the answer sent to the server.

[1343] Step 6:

[1344] The server immediately judges the received answer and returns the result to the user in real time. The input data is the user's answer, and the output is the judgment result. Specific operations include analyzing whether the answer is correct or incorrect.

[1345] Step 7:

[1346] The server analyzes past training data and identifies the user's weaknesses. The input data is the past training data, and the output is the identified weaknesses. The server analyzes the data using an analysis algorithm.

[1347] Step 8:

[1348] The server generates a remedial plan to overcome the weaknesses and provides it to the user. The input data is the identified weaknesses, and the output is the remedial plan. The generated remedial plan is sent to the user's terminal.

[1349] Step 9:

[1350] The server continuously monitors the user's learning progress and generates and sends visual reports. The input data is daily learning data, and the output is visual reports. Progress is recorded and analyzed using monitoring methods.

[1351] Step 10:

[1352] If a user has a question during learning, they can ask it in real time via a communication application. The server uses AI to generate an appropriate answer and sends it back to the user. The input data is the user's question, and the output is the server's answer.

[1353] Step 11:

[1354] The device acquires biometric information such as the user's facial expression and voice via a communication application. The input data is the user's facial expression and voice, and the output is the biometric information.

[1355] Step 12:

[1356] The server uses an emotion recognition model to analyze the user's emotions and adjust the difficulty of the study plan and questions provided based on that state. The input data is the acquired biometric information, and the output is the adjusted study plan and the difficulty of the questions provided. Specifically, the AI ​​model analyzes emotions and adjusts the plan based on the emotional state.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] [Third embodiment]

[1361] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1362] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1363] 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).

[1364] 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.

[1365] 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.

[1366] 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).

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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."

[1373] This invention is a system that provides customized learning support to each student. This system is implemented via a communication application that users use on a daily basis. Below, we will show an embodiment of each of the main functions.

[1374] Customized learning plan design

[1375] 1. Enter your study information

[1376] Users input their basic information (grade, desired school, strong and weak subjects, target score, etc.) via a communication application.

[1377] The terminal transmits the input information to the server.

[1378] 2. Plan Generation

[1379] The server uses AI algorithms to generate a learning plan tailored to each student based on the information it receives.

[1380] A generating means determines the schedule, material selection, etc., and formats the individual plan for a communication application.

[1381] 3. Send plan

[1382] The transmission means transmits the generated study plan to the user's terminal.

[1383] Examples:

[1384] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[1385] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[1386] Questions provided regularly every morning

[1387] 1. Problem generation

[1388] The server generates a problem set based on the study plan.

[1389] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[1390] 2. Submit your question

[1391] The server sends the problem to the user's communication application at a set time each morning.

[1392] The user checks the question on the communication application and submits the answer.

[1393] 3. Receiving answers and judging

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

[1395] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[1396] Examples:

[1397] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application.

[1398] The user answers the questions and sends the answers via a communication application.

[1399] The server automatically checks the answers and immediately sends the results.

[1400] AI support to overcome weaknesses

[1401] 1. Analysis of learning results

[1402] The server analyzes the user's past answer data and identifies weak points.

[1403] 2. Supplementary lesson plan generation

[1404] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[1405] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[1406] Examples:

[1407] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics.

[1408] The server creates a supplementary lesson plan on electromagnetics and provides related questions and explanatory videos via a communication application.

[1409] Progress monitoring and feedback transfer

[1410] 1. Collecting training data

[1411] The server periodically collects the user's learning data (daily answer results, progress, etc.).

[1412] 2. Data analysis and feedback generation

[1413] The server analyzes the collected data and evaluates the user's progress.

[1414] A monitoring means generates progress reports.

[1415] 3. Send Feedback

[1416] The transmission means transmits the generated progress report to the user via a communication application.

[1417] Examples:

[1418] The server analyzes the learning data for one week and graphs the level of understanding and progress.

[1419] The server generates a weekly report every Monday and sends it to the user via a communication application.

[1420] Real-time question answering

[1421] 1. Receiving questions

[1422] Users use a communication application to submit questions they are studying.

[1423] The terminal sends a query to the server.

[1424] 2. Response Generation

[1425] The server uses AI to analyze the received questions and generate appropriate answers.

[1426] 3. Submit your response

[1427] The sending means returns the generated answer to the communication application in real time.

[1428] Examples:

[1429] A user sends a question via a communication application saying, "I don't know how to solve a quadratic equation."

[1430] The server receives the question and uses AI to generate a detailed answer.

[1431] The server sends the answer in real time via a communication application and replies, "Please try solving it by following the steps below."

[1432] The above is a detailed description of the embodiments of the invention based on the claims.

[1433] The processing flow will be explained below.

[1434] Customized learning plan design

[1435] Step 1:

[1436] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[1437] Step 2:

[1438] The terminal transmits the input user information to the server.

[1439] Step 3:

[1440] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[1441] The server selects the schedule and study materials and creates a customized learning plan.

[1442] Step 4:

[1443] The server formats the generated lesson plan and sends it to the communication application.

[1444] Questions provided regularly every morning

[1445] Step 1:

[1446] The server generates a problem set based on the user's study plan and progress.

[1447] Step 2:

[1448] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[1449] Step 3:

[1450] The server sends selected questions to the user's communication application at a set time every morning.

[1451] Step 4:

[1452] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[1453] Step 5:

[1454] The terminal sends the user's answer to the server.

[1455] Step 6:

[1456] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[1457] AI support to overcome weaknesses

[1458] Step 1:

[1459] The server collects the user's past answer data and uses analytical algorithms to identify weaknesses.

[1460] Step 2:

[1461] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[1462] Step 3:

[1463] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[1464] Step 4:

[1465] The user advances his / her studies using the supplementary learning materials sent to him / her.

[1466] Progress monitoring and feedback transfer

[1467] Step 1:

[1468] The server collects the user's daily learning data (answers to questions, time, progress, etc.).

[1469] Step 2:

[1470] The server analyzes the data collected and evaluates learning progress and level of understanding.

[1471] Step 3:

[1472] The server generates weekly and monthly progress reports.

[1473] Step 4:

[1474] The server formats the generated report and sends it to the communication application.

[1475] Step 5:

[1476] Users can check the reports and understand their own learning status.

[1477] Real-time question answering

[1478] Step 1:

[1479] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[1480] Step 2:

[1481] The terminal sends the user's question to the server.

[1482] Step 3:

[1483] The server receives the question and uses AI to generate an appropriate answer.

[1484] Step 4:

[1485] The server sends the generated answer in real time to the communication application.

[1486] Step 5:

[1487] The user checks the answer in the communication application and resolves the question.

[1488] The above are the specific steps of the program processing of this system.

[1489] Example 1

[1490] 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."

[1491] Conventional learning support systems have difficulty fully addressing the learning progress and weaknesses of individual students, and are limited to providing uniform learning plans and feedback. Furthermore, it is difficult to provide real-time question and answer sessions and progress reports, which prevents users from maintaining their motivation to learn and from providing effective learning support.

[1492] 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.

[1493] In this invention, the server includes an input means for a user to input basic information about their studies, a generation means for receiving the input information and generating an individualized study plan using a generative AI model, a transmission means for transmitting the generated study plan to the user's device, a question providing means for generating and transmitting daily questions based on the user's progress, an answer determining means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report, a question answering means for receiving the user's questions and generating and transmitting answers using the generative AI model, a collection means for periodically collecting user data, and a formatting means for formatting the user's study plan for a communication application, thereby enabling customized study support for individual students, real-time feedback, and question answering.

[1494] "Input means" refers to a device or interface for a user to input basic information related to learning.

[1495] A "generator" is a device or algorithm that uses a generative AI model to generate an individualized learning plan based on learning information received from a user.

[1496] "Transmission means" refers to a device or function for transmitting the generated study plan, answer determination results, and visual report to the user's terminal.

[1497] A "question provider" is a device or algorithm that generates and sends daily study questions based on the user's progress.

[1498] An "answer determination means" is a device or algorithm for immediately determining an answer received from a user and transmitting the result.

[1499] "Analysis means" refers to a device or algorithm that analyzes past learning data and identifies the user's weaknesses.

[1500] A "remedial action provider" is a device or algorithm for generating and delivering a remedial action plan to address identified weaknesses.

[1501] A "monitoring means" is a device or algorithm for continuously monitoring a user's learning progress and generating and transmitting visual reports.

[1502] A "question answering means" is a device or algorithm for receiving a user's question and generating and transmitting an answer using a generative AI model.

[1503] The "collection means" is a device or algorithm for periodically collecting user learning data.

[1504] A "formatting means" is a device or algorithm for formatting the generated lesson plan into a format suitable for communication applications.

[1505] The present invention provides a learning support system that is customized for each student. This system is realized through a communication application that users use on a daily basis.

[1506] Customized learning plan design

[1507] Entering study information

[1508] 1. Users enter basic information such as their grade, preferred school, strong and weak subjects, and target score via a communication application. This can be done using a device such as a smartphone, tablet, or PC.

[1509] 2. The terminal sends the entered information to the server.

[1510] Plan Generation

[1511] 1. The server uses a generative AI model (e.g., GPT-4) based on the received information to generate a learning plan appropriate for each student.

[1512] 2. A generating means determines the schedule, material selection, etc., and formats the individual plan for the communication application.

[1513] Send Plan

[1514] 1. The transmission means transmits the generated study plan to the user's terminal.

[1515] Examples:

[1516] The user inputs their grade ("third year high school student"), their preferred school ("top university"), and their weak subject ("math"), and the server uses an AI algorithm to create a "math focus plan" and sends it to the user via a communication application.

[1517] Questions provided regularly every morning

[1518] Problem generation

[1519] 1. The server generates a problem set based on the study plan.

[1520] 2. The problem provision method selects problems of a level of difficulty that corresponds to the user's progress and level of understanding.

[1521] Submit a problem

[1522] 1. The server sends a question to the user's communication application at a set time every morning.

[1523] 2. The user checks the question on the communication application and submits the answer.

[1524] Receiving answers and judging

[1525] 1. The terminal sends the user's answer to the server.

[1526] 2. The server receives the answer and uses the judging means to determine whether it is correct or incorrect.

[1527] Examples:

[1528] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application. The user answers the problems and sends the answers via the communication application. The server automatically checks the answers and immediately sends the results.

[1529] AI support to overcome weaknesses

[1530] Analysis of learning results

[1531] 1. The server analyzes the user's past answer data and identifies weak points.

[1532] Supplementary lesson plan generation

[1533] 1. The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[1534] 2. A tutoring provider selects relevant questions and materials and sends them to the user's communication application.

[1535] Examples:

[1536] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics. The server then creates a supplementary study plan for electromagnetics and provides related questions and explanatory videos via a communication application.

[1537] Progress monitoring and feedback transfer

[1538] Collection of training data

[1539] 1. The server periodically collects the user's daily learning data.

[1540] Data analysis and feedback generation

[1541] 1. The server analyzes the collected data and evaluates the user's progress.

[1542] 2. Monitoring measures generate progress reports.

[1543] Send Feedback

[1544] 1. The transmission means transmits the generated progress report to the user via a communication application.

[1545] Examples:

[1546] The server analyzes the learning data for one week and graphs the level of understanding and progress. The server generates a weekly report every Monday and sends it to the user via a communication application.

[1547] Real-time question answering

[1548] Question received

[1549] 1. The user submits a question they are studying using a communication application.

[1550] 2. The device sends a query to the server.

[1551] Response Generation

[1552] 1. The server analyzes the received question using a generative AI model and generates an appropriate answer.

[1553] Send response

[1554] 1. The generated answer is returned to the communication application in real time by the transmitting means.

[1555] Examples:

[1556] A user sends a question via a communication application, such as "I don't know how to solve a quadratic equation." The server receives the question and uses AI to generate a detailed answer. The server then sends the answer in real time via the communication application and replies, "Try solving it by following the steps below."

[1557] Prompt Sentence Examples

[1558] 1. Lesson plan generation prompt:

[1559] "Generate a study plan for a high school senior who wants to attend a top university and whose weakest subject is math."

[1560] 2. AI Q&A prompt:

[1561] "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[1562] Through this, the system is able to provide personalized and effective learning support in real time.

[1563] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1564] Step 1:

[1565] The user starts the communication application and inputs basic information such as grade level, desired school, strong and weak subjects, target score, etc. This input information includes the user's learning background and goals.

[1566] Step 2:

[1567] The device sends the entered basic information to the server. This data is sent to the server in JSON format. Here, the entered basic information is used as the transmission data as is.

[1568] Step 3:

[1569] The server uses a generative AI model to generate an individualized learning plan based on the received basic information. Specifically, the server inputs a prompt statement (e.g., "Please generate a learning plan for a high school senior who is weak in math") into the generative AI model, which then generates an appropriate learning plan. The learning plan generated as the output includes a learning schedule and recommended learning materials.

[1570] Step 4:

[1571] The server sends the generated study plan to the user's terminal. The formatting means formats the study plan into a user-friendly format and transfers it to the user's terminal. The terminal then outputs the received study plan on the application's display screen.

[1572] Step 5:

[1573] The server generates a daily problem set based on the user's progress. Specifically, it uses the user's individual learning plan and past correct / incorrect data as input data and creates a problem set using an AI model. The problem set generated as output contains problems of a difficulty level appropriate to the user's level of understanding.

[1574] Step 6:

[1575] The server sends the generated problem set to the user's terminal at a fixed time every morning. A schedule management system is used so that the user can receive the problem. The terminal displays the received problem and notifies the user.

[1576] Step 7:

[1577] The user answers the received questions using a communication application, and the user's answers are entered using the application's input form.

[1578] Step 8:

[1579] The terminal transmits the user's answers to the server, which stores the transmitted answer data in its database.

[1580] Step 9:

[1581] The server immediately judges the received answers using a judgment method, automatically classifying the answers using a generative AI model and generating a correct / incorrect result, which includes feedback on correct and incorrect answers.

[1582] Step 10:

[1583] The server sends the result of the judgment to the user's device, which then displays the received feedback on the application and notifies the user.

[1584] Step 11:

[1585] The server analyzes the user's past answer data to identify weaknesses. Here, analytical means are used to find patterns in the past data to identify the user's weaknesses. The output includes information about the identified weaknesses.

[1586] Step 12:

[1587] The server generates a remedial plan based on the identified weaknesses. Using a generative AI model, the server generates a remedial plan based on a prompt such as, "Please generate a remedial plan for students who are weak in the electromagnetic field of physics." The remedial plan includes relevant questions and explanatory materials.

[1588] Step 13:

[1589] The server sends the supplementary lesson plan to the user's terminal, which receives the supplementary lesson plan and displays it in a manner that notifies the user.

[1590] Step 14:

[1591] The server periodically collects the user's daily learning data and continuously monitors their progress. It analyzes the collected data and generates a progress report for the user. This output includes graphs and charts that visualize the user's understanding and progress.

[1592] Step 15:

[1593] The server sends the generated progress report to the user's terminal, which displays the received report and notifies the user.

[1594] Step 16:

[1595] The user sends a question during the study using a communication application, and the terminal sends the question to the server.

[1596] Step 17:

[1597] The server analyzes the received question using a generative AI model and generates an appropriate answer. Specifically, it receives a prompt such as, "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[1598] Step 18:

[1599] The server sends the generated answers back to the user in real time via a communication application, where the user can check the answers and use them for learning.

[1600] The above is the specific processing flow in this system.

[1601] (Application example 1)

[1602] 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."

[1603] Conventional learning support systems have difficulty providing customized learning plans based on each student's learning progress and level of understanding. They also face challenges in providing individual questions, feedback, and answering questions in real time. This can prevent students from learning efficiently and can result in insufficient support for overcoming weaknesses in specific subjects or areas.

[1604] 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.

[1605] In this invention, the server includes: an input means for a user to input basic information related to their studies; a generation means for receiving the input information and generating an individualized study plan using an AI algorithm; a transmission means for transmitting the generated study plan to the user's terminal; a question providing means for generating and transmitting daily questions based on the user's progress; an answer determination means for determining answers received from the user and transmitting the results; an analysis means for analyzing past study data and identifying the user's weaknesses; a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses; a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report; a question answering means for receiving the user's questions and generating and transmitting answers using AI; a content providing means for delivering customized study content to the user based on a generative AI model; a notification means for generating a question set at a fixed time every morning and notifying the user; and an answer reception determination means for receiving the user's answers in real time and automatically determining them. This makes it possible to deliver learning plans and content optimized for each student, and by providing questions and feedback in real time according to individual progress, it is possible to improve learning efficiency and comprehension.

[1606] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[1607] A "generation method" is a process or system that uses AI algorithms to generate an individualized learning plan based on input information.

[1608] "Transmission means" refers to the communication technology or method for transmitting the generated learning plan and data to the user's terminal.

[1609] "Question providing means" refers to a function or system that generates and sends daily questions based on the user's progress.

[1610] An "answer determination means" is a process or algorithm for determining the answers received from the user and transmitting the results.

[1611] "Analysis means" refers to a method or system for analyzing past learning data and identifying the user's weaknesses.

[1612] A "remedial action delivery vehicle" is a process or system that generates and delivers a remedial action plan to address identified weaknesses.

[1613] "Monitoring means" refers to functions and methods for continuously monitoring a user's learning progress and generating and sending visual reports.

[1614] A "question answering tool" is a system or process that receives a user's question and uses AI to generate and send an answer.

[1615] "Content provision means" refers to the function or method for delivering customized learning content to users based on a generative AI model.

[1616] The "notification means" is a system or method for generating a problem set at a fixed time every morning and notifying the user.

[1617] The "answer reception and determination means" is a process or algorithm for receiving the user's answers in real time and automatically determining them.

[1618] This invention is a system that provides customized learning support to each student. This system has multiple functions and can be used by users using everyday devices such as smartphones. The main functions and their implementation modes are described below.

[1619] Entering study information

[1620] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[1621] Customized learning plan design

[1622] The server uses an AI algorithm to generate an individualized learning plan based on the received user information. The plan is then sent to the user's device, where the user can review it and work on their daily studies.

[1623] Questions provided regularly every morning

[1624] The server generates a set of questions based on the user's study plan and progress at a fixed time every morning and notifies the user's device. The user can then check the questions on the application and submit their answers.

[1625] Receiving answers and judging

[1626] The server receives the answers sent by the user in real time and automatically judges them. The results are immediately sent to the user's device, allowing the user to check their level of understanding as they continue their studies.

[1627] AI support to overcome weaknesses

[1628] The server analyzes past learning data to identify the user's weaknesses, then generates a supplementary study plan to overcome the identified weaknesses and provides the user with relevant learning materials and questions.

[1629] Progress monitoring and feedback transfer

[1630] The server continuously monitors the user's learning progress and generates visual reports, such as weekly or monthly progress reports, which are sent to the user's device, allowing the user to understand their own learning progress.

[1631] Real-time question answering

[1632] Users can send questions that arise during their studies to the server via a communication application. The server uses an AI algorithm to analyze the questions, generate appropriate answers in real time, and return them to the user.

[1633] Details of content delivery methods

[1634] The server delivers customized learning content to users based on generative AI models, making it possible to provide a learning experience optimized for each individual student, rather than the traditional one-size-fits-all approach.

[1635] Examples of prompt statements

[1636] "If a user inputs their preferred school as a 'top university' and their weakest subject as 'math', generate a 'math focus plan' based on the identified information and deliver it to them via a communication application. Also, deliver a set of math problems to the user every morning at 8:00, check the answers in real time, and provide feedback."

[1637] This system configuration enables learning support customized for each student, enabling efficient learning progress and improved understanding.

[1638] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1639] Step 1: Enter your study information

[1640] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[1641] Input: Grade, desired school, strong subjects, weak subjects, target score

[1642] Output: User basic information sent to the server

[1643] Step 2: Generate a customized study plan

[1644] The server uses an AI algorithm to generate an individual learning plan based on the received user information. Specifically, the generative AI model analyzes the user's profile and plans the optimal learning materials, schedule, and learning progress method.

[1645] Input: User basic information

[1646] Output: A customized study plan

[1647] Step 3: Submit your study plan

[1648] The server sends the generated study plan to the user's device, and the user can open the application from the notification and view the provided study plan.

[1649] Input: Customized Study Plan

[1650] Output: The learning plan displayed on the user's device

[1651] Step 4: Provide regular questions every morning

[1652] The server generates a set of questions every morning at a fixed time based on the user's study plan and progress, and notifies the user's device. The difficulty of the questions is adjusted according to the user's level of understanding.

[1653] Input: Study plan, progress data

[1654] Output: A set of questions to be sent to you every morning

[1655] Step 5: Submit your answers and have them graded

[1656] The user answers questions provided on the application. The answers are sent from the device to the server, which judges the answers in real time and provides immediate visual feedback on whether the answer is correct or incorrect.

[1657] Input: User's answer

[1658] Output: Feedback of correct / incorrect results

[1659] Step 6: AI support to overcome weaknesses

[1660] The server analyzes past learning data to identify the user's weaknesses, generates a remedial study plan based on the identified weaknesses, and provides the user with relevant learning materials and questions.

[1661] Input: Past training data

[1662] Output: Supplementary lesson plan, related questions and materials

[1663] Step 7: Progress monitoring and feedback

[1664] The server continuously monitors the user's learning progress and generates visual reports, for example, weekly or monthly progress reports, which are sent to the user's device.

[1665] Input: Learning progress data

[1666] Output: Visual report

[1667] Step 8: Real-time question answering

[1668] Users can send questions that arise during their studies to the server via a communication application, and the server will analyze the questions using an AI algorithm, generate appropriate answers in real time, and send them back to the user.

[1669] Input: User question

[1670] Output: AI-generated answer

[1671] Step 9: Content Delivery Methods

[1672] The server delivers customized learning content based on generative AI models, providing users with individually optimized learning materials and tests.

[1673] Input: Database of learning content, user characteristics

[1674] Output: Customized learning content

[1675] Step 10: Set a notification method every morning

[1676] The server generates a set of questions every morning at a fixed time and notifies the user's device. When the user receives the notification, they can open the application and check the questions.

[1677] Input: Study schedule, question database

[1678] Output: Problem notification every morning

[1679] Step 11: Answer reception determination means

[1680] When a user submits their answer, the server receives it in real time and automatically judges it, with the results immediately sent to the user's device.

[1681] Input: User's answer

[1682] Output: Feedback of the judgement result

[1683] 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.

[1684] This invention is a system that provides flexible and efficient learning support tailored to the user's emotional state by combining an emotion engine with an individual learning support system. This system is implemented via a communication application that the user uses on a daily basis. Below, we will show an embodiment of each of the main functions.

[1685] Customized learning plan design

[1686] 1. Enter your study information

[1687] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[1688] The terminal transmits the input information to the server.

[1689] 2. Plan Generation

[1690] The server uses AI algorithms based on the received information to generate a personalized learning plan for the user.

[1691] The generation means selects schedules and learning materials to create customized learning plans.

[1692] 3. Send plan

[1693] The transmission means transmits the generated study plan to the user's terminal.

[1694] Examples:

[1695] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[1696] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[1697] Questions provided regularly every morning

[1698] 1. Problem generation

[1699] The server generates a problem set based on the study plan.

[1700] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[1701] 2. Submit your question

[1702] The server sends the problem to the user's communication application at a set time each morning.

[1703] The user checks the questions on the communication application and inputs the answers.

[1704] 3. Receiving answers and judging

[1705] The terminal transmits the user's answer to the server.

[1706] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[1707] AI support to overcome weaknesses

[1708] 1. Analysis of learning results

[1709] The server collects the user's past answer data and uses analytical algorithms to identify weak spots.

[1710] 2. Supplementary lesson plan generation

[1711] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[1712] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[1713] Progress monitoring and feedback transfer

[1714] 1. Collecting training data

[1715] The server periodically collects the user's daily learning data (answers to questions, time, progress, etc.).

[1716] 2. Data analysis and feedback generation

[1717] The server analyzes the collected data and evaluates learning progress and level of understanding.

[1718] A monitoring means generates progress reports.

[1719] 3. Send Feedback

[1720] The transmission means transmits the generated progress report to the user via a communication application.

[1721] Real-time question answering

[1722] 1. Receiving questions

[1723] Users use a communication application to input and send questions or concerns they have about their studies.

[1724] 2. Response Generation

[1725] The server uses AI to analyze the received questions and generate appropriate answers.

[1726] 3. Submit your response

[1727] The sending means returns the generated answer to the communication application in real time.

[1728] Introducing the Emotion Engine

[1729] 1. Acquiring Emotion Data

[1730] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[1731] 2. Sentiment Analysis

[1732] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[1733] 3. Plan adjustment

[1734] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[1735] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[1736] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[1737] Examples:

[1738] If it is determined that the user is feeling stressed by the questions, the server provides easier questions based on that data to maintain the user's motivation.

[1739] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[1740] The above is a detailed description of an embodiment of the present invention. This system provides personalized learning support that takes into account the user's emotions, thereby realizing a more effective learning environment.

[1741] The processing flow will be explained below.

[1742] Customized learning plan design

[1743] Step 1:

[1744] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[1745] Step 2:

[1746] The terminal transmits the input user information to the server.

[1747] Step 3:

[1748] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[1749] The server selects the schedule and study materials and creates a customized learning plan.

[1750] Step 4:

[1751] The server formats the generated lesson plan and sends it to the communication application.

[1752] Questions provided regularly every morning

[1753] Step 1:

[1754] The server generates a problem set based on the user's study plan and progress.

[1755] Step 2:

[1756] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[1757] Step 3:

[1758] The server sends selected questions to the user's communication application at a set time every morning.

[1759] Step 4:

[1760] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[1761] Step 5:

[1762] The terminal sends the user's answer to the server.

[1763] Step 6:

[1764] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[1765] AI support to overcome weaknesses

[1766] Step 1:

[1767] The server collects the user's past answer data and learning outcomes and uses analytical algorithms to identify weak points.

[1768] Step 2:

[1769] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[1770] Step 3:

[1771] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[1772] Step 4:

[1773] The user advances his / her studies using the supplementary learning materials sent to him / her.

[1774] Progress monitoring and feedback transfer

[1775] Step 1:

[1776] The server periodically collects the user's daily learning data (answer results, time spent studying, progress, etc.).

[1777] Step 2:

[1778] The server analyzes the data collected and evaluates the progress of learning and level of understanding.

[1779] Step 3:

[1780] The server generates weekly and monthly progress reports.

[1781] Step 4:

[1782] The server formats the generated report and sends it to the communication application.

[1783] Step 5:

[1784] Users can check the reports and understand their own learning status.

[1785] Real-time question answering

[1786] Step 1:

[1787] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[1788] Step 2:

[1789] The terminal transmits the user's question data to the server.

[1790] Step 3:

[1791] The server receives the question data and uses AI to generate appropriate answers.

[1792] Step 4:

[1793] The server returns the generated answer to the communication application in real time.

[1794] Step 5:

[1795] The user checks the answer in the communication application and resolves the question.

[1796] Introducing the Emotion Engine

[1797] Step 1:

[1798] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[1799] Step 2:

[1800] The server uses emotion recognition means to analyze the user's emotions and identify their current emotional state.

[1801] Step 3:

[1802] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[1803] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[1804] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[1805] Examples:

[1806] If it is determined that the user is feeling stressed by the questions, the server will provide easier questions based on that data, maintaining the user's motivation to study.

[1807] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[1808] Example 2

[1809] 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."

[1810] Conventional learning support systems lack the ability to provide appropriate feedback based on individual users' progress and weaknesses, and are unable to flexibly adjust learning plans that take into account the user's emotional state. Furthermore, they lack the ability to provide real-time answer assessment and question response, making it difficult to maximize the user's motivation and efficiency in learning. These issues need to be addressed.

[1811] 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.

[1812] In this invention, the server includes a generation means for receiving input information and generating an individualized study plan using an AI algorithm, a transmission means for transmitting the generated study plan to the user's computer, a problem provision means for generating and transmitting daily problems based on the user's progress, an answer determination means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study provision means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, and an emotion engine means for acquiring and analyzing the user's emotional data and adjusting the study plan based on the user's current emotional state. This enables flexible adjustment of the study plan according to the individual's learning situation, real-time answer determination, and question response, thereby improving the user's learning efficiency and motivation.

[1813] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[1814] "Generation means" refers to a device or program that has the function of generating an individualized learning plan using an AI algorithm based on the information received.

[1815] "Transmission means" refers to a device or function for transmitting the generated lesson plan and other data to the user's computer.

[1816] A "problem provider" is a device or system that has the function of generating and transmitting daily problems based on the user's progress.

[1817] The "answer determination means" refers to a device or algorithm that has the function of analyzing the answer received from the user, determining whether it is correct or incorrect, and transmitting the result.

[1818] "Analysis means" refers to devices or algorithms that have the ability to analyze past learning data and identify the user's weaknesses.

[1819] The "tuition provision means" refers to a device or system for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[1820] "Monitoring means" refers to a device or system that has the ability to continuously monitor a user's learning progress and generate visual reports.

[1821] A "question answering means" is a device or system that has the function of receiving a user's question, using AI to generate an appropriate answer, and sending it.

[1822] The "emotion engine means" refers to a device or system that has the function of acquiring and analyzing the user's emotional data and adjusting the learning plan and content based on the user's emotional state.

[1823] This invention is a system that provides flexible and efficient learning support according to the user's emotional state by combining an emotion engine with an individual learning support system. Below, an embodiment of each main function is shown.

[1824] Customized learning plan design

[1825] 1. Enter your study information

[1826] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[1827] The terminal transmits the input information to the server.

[1828] 2. Create a learning plan

[1829] The server uses AI algorithms (e.g., TensorFlow, PyTorch) based on the received information to generate a personalized learning plan for the user.

[1830] The generation means selects schedules and learning materials to create customized learning plans.

[1831] 3. Submit your study plan

[1832] The server transmits the generated study plan to the user's terminal.

[1833] The user views the customized learning plan on the device.

[1834] Questions provided regularly every morning

[1835] 1. Problem Generation

[1836] The server generates daily problem sets based on the user's study plan.

[1837] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[1838] 2. Submit your issue

[1839] The server sends the problem to the user's communication application at a fixed time each morning.

[1840] The user checks the questions on the communication application and inputs the answers.

[1841] 3. Receiving and judging answers

[1842] The terminal transmits the user's answer to the server.

[1843] The server receives the answers and uses an AI algorithm to determine whether they are correct.

[1844] AI support to overcome weaknesses

[1845] 1. Analysis of learning results

[1846] The server collects the user's past answer data and uses analytical means to identify weak points.

[1847] 2. Generate a supplementary lesson plan

[1848] The server uses AI to generate a remedial plan to address identified weaknesses.

[1849] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[1850] Progress monitoring and feedback transfer

[1851] 1. Collecting training data

[1852] The server periodically collects the user's daily learning data (answers to questions, learning time, progress, etc.).

[1853] 2. Data analysis and feedback generation

[1854] The server analyzes the collected data and generates progress reports.

[1855] A monitoring means generates progress reports.

[1856] 3. Submitting Feedback

[1857] The server sends the generated progress report to the user via a communication application.

[1858] Real-time question answering

[1859] 1. Receiving Questions

[1860] Users use a communication application to input and send questions or concerns they have about their studies.

[1861] 2. Generating a Response

[1862] The server uses AI to analyze the received questions and generate appropriate answers.

[1863] 3. Submit your response

[1864] The server returns the generated answer to the communication application in real time.

[1865] Introducing the Emotion Engine

[1866] 1. Acquiring Emotion Data

[1867] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[1868] 2. Sentiment Analysis

[1869] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[1870] 3. Adjust your study plan

[1871] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[1872] If the user is feeling stressed, easy questions are provided, and if the user is feeling positive, more difficult questions are provided.

[1873] Specific examples

[1874] If a user inputs their grade as "third year high school student," their desired school as "top university," and their weak subject as "math," the server will generate a "math emphasis plan" and send it to the user via a communication application.

[1875] Every morning, the server sends the user a customized math problem, and the user enters and submits the answer.

[1876] The server judges the answer and provides real-time feedback on whether it is correct or incorrect.

[1877] Based on past data, the server identifies that "quadratic functions" is a weak point and sends a remedial plan specific to that area.

[1878] The server monitors learning progress and periodically sends progress reports to the user.

[1879] The server obtains the user's emotional data and adjusts the learning plan.

[1880] Example of input prompt for generative AI model

[1881] "I'm a third-year high school student and I'm hoping to get into a top university. I'm not good at math, but my strongest subject is English. My goal is to get 900 points on the National Center Test. Please provide me with a customized study plan."

[1882] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1883] Step 1:

[1884] Entering study information

[1885] The user uses a communication application to input basic information such as grade, desired school, favorite subjects, weak subjects, and target score.

[1886] The terminal collects the input data and sends it to a backend endpoint.

[1887] Input data: grade, desired school, strong subjects, weak subjects, target score

[1888] Output data: Data set of input information

[1889] Specific operation: The user enters information into the input form of the communication application and presses the "Send" button. The device then sends the data to the server's API as a POST request.

[1890] Step 2:

[1891] Generate a learning plan

[1892] The server uses AI algorithms to generate an individualized learning plan based on the information received.

[1893] AI algorithms (e.g., TensorFlow, PyTorch) analyze input data and select appropriate schedules and teaching materials.

[1894] Input data: Input information dataset

[1895] Output data: personalized learning plan

[1896] Specific operation: The server inputs the received data into an AI algorithm, analyzes it, and generates an optimal learning plan for the user. The generated plan is stored in a database.

[1897] Step 3:

[1898] Submit your study plan

[1899] The server transmits the generated study plan to the user's terminal.

[1900] The device will display the received learning plan so that it can be reviewed.

[1901] Input data: Individual learning plan

[1902] Output data: The learning plan displayed on the user's device

[1903] Specific operation: The server sends the generated study plan to the user's device, and the user checks the study plan using a communication application.

[1904] Step 4:

[1905] Daily problem provision

[1906] The server generates daily questions based on the user's progress.

[1907] Questions of appropriate difficulty are selected taking into account the user's past learning data and current learning plan.

[1908] Input data: User's learning plan, progress data

[1909] Output data: Generated daily problems

[1910] Specific operation: The server generates questions using an AI algorithm based on daily data and sends the questions to the user's device at the specified time.

[1911] Step 5:

[1912] Submitting questions and entering answers

[1913] The server sends questions to the user's device at a set time every morning.

[1914] The user answers the questions and enters the answers via a communication application.

[1915] Input data: Generated problems

[1916] Output data: User's answer data

[1917] Specific operation: The server sends the created questions to the user at 8:00 every morning, and the user enters the answers in the app.

[1918] Step 6:

[1919] Receiving answers and determining whether they are correct or incorrect

[1920] The terminal transmits the user's answer data to the server.

[1921] The server uses an AI algorithm to determine whether the answer is correct.

[1922] Input data: User's answer data

[1923] Output data: Correct / incorrect result

[1924] Specific operation: The server receives the answer data sent from the device, determines whether it is correct using an AI algorithm, and sends the result to the user.

[1925] Step 7:

[1926] Generate a remedial lesson plan to address weaknesses

[1927] The server analyzes past answer data and identifies the user's weaknesses.

[1928] AI generates a supplementary lesson plan based on identified weaknesses.

[1929] Input data: Answer data, analysis data

[1930] Output data: Supplementary lesson plan

[1931] Specific operation: The server analyzes past answer data, detects weaknesses, generates a supplementary study plan specific to the detected weaknesses, and sends it to the user's device.

[1932] Step 8:

[1933] Progress monitoring and feedback

[1934] The server periodically collects and analyzes the user's daily learning data and generates a progress report.

[1935] Send progress reports to the user's device.

[1936] Input data: Learning data (answer results, learning time, progress)

[1937] Output data: Progress report

[1938] Specific operation: The server collects learning data, analyzes it using AI algorithms, generates reports, and periodically sends them to the user's device.

[1939] Step 9:

[1940] Real-time question answering

[1941] The user inputs questions about the study through a communication application and sends them to the server.

[1942] The server uses AI to analyze the question and generate an appropriate answer.

[1943] Input data: Question content

[1944] Output data: The generated answer

[1945] How it works: Users submit questions via the app, the server uses AI to generate answers, and sends the answers to users in real time.

[1946] Step 10:

[1947] Introducing the Emotion Engine

[1948] The device uses a camera and microphone to capture the user's biometric signals and transmit them to the server.

[1949] The server uses emotion recognition means to analyze the emotion data and identify the current emotional state.

[1950] Adjust your study plan or problem difficulty based on your emotional state.

[1951] Input data: Emotion data (facial expressions, voice, etc.)

[1952] Output data: adjusted lesson plans and questions

[1953] How it works: The device collects the user's emotional data and sends it to the server, which analyzes the data to identify the user's emotional state and dynamically adjusts the learning plan and difficulty of the questions.

[1954] (Application example 2)

[1955] 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."

[1956] To improve employee productivity and motivation, it is important to properly understand employees' emotional states and provide appropriate work support. However, conventional systems lack emotional recognition capabilities and rely on rigid work procedures and task provision, making it difficult to reduce employee burden and stress. Furthermore, they lack real-time feedback and support, making it difficult to provide effective support in workplaces where immediate response is required.

[1957] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input basic information about learning, generation means for receiving the input information and generating an individualized learning plan using an AI algorithm, transmission means for transmitting the generated learning plan to the user's terminal, question provision means for generating and transmitting daily questions based on the user's progress, answer determination means for determining answers received from the user and transmitting the results, analysis means for analyzing past learning data and identifying the user's weaknesses, supplementary learning provision means for generating and transmitting supplementary learning plans to overcome the identified weaknesses, monitoring means for continuously monitoring the user's learning progress and generating and transmitting visual reports, question answering means for receiving the user's questions and generating and transmitting answers using AI, emotion recognition means for acquiring the user's biosignals via a communication application and analyzing their emotions, and plan adjustment means for adjusting the difficulty of the learning plan and the provided questions based on the emotional state analyzed by the emotion recognition means. This enables flexible work support according to the user's emotional state, improving employee productivity and motivation.

[1958] "Input means" refers to a device or mechanism that allows a user to input basic information related to learning.

[1959] "Generation means" refers to the function or process that uses an AI algorithm to generate an individual learning plan based on information received from the input means.

[1960] "Transmission means" refers to a function or device for transmitting the generated study plan or other information to the user's terminal.

[1961] "Question provision means" refers to the functions and processes for generating and providing daily questions based on the user's progress.

[1962] The "answer determination means" is a function or process for determining the answer received from the user and transmitting the result.

[1963] "Analysis means" refers to functions and processes for analyzing past learning data and identifying the user's weaknesses.

[1964] The "tuition provision means" is a function or process for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[1965] "Monitoring means" refers to the functions and processes that continuously monitor a user's learning progress and generate and send visual reports.

[1966] A "question answering means" is a function or process for receiving a user's question, generating an answer using AI, and sending it to the user.

[1967] The "emotion recognition means" refers to a function or process for acquiring a user's biosignal via a communication application and analyzing the user's emotions.

[1968] The "plan adjustment means" is a function or process for adjusting the difficulty of the study plan and the questions provided based on the emotional state analyzed by the emotion recognition means.

[1969] overview

[1970] This invention is a learning support system that takes the user's emotions into consideration, creating individual learning plans, providing regular quizzes, answering questions in real time, and adjusting plans based on emotions. This system can also be applied to work support in factories, providing flexible work support that responds to the emotions of employees.

[1971] composition

[1972] The system of the present invention is realized using the following hardware and software.

[1973] Hardware: Cameras, PCs, smartphones, tablets

[1974] Software: AI algorithms, emotion recognition models, communication applications

[1975] Program processing details

[1976] Enter basic information about your studies

[1977] The terminal accepts basic information about the user's studies (grade, desired school, favorite subjects, weak subjects, target score, etc.) entered by the user. This information is sent to the server via a communication application.

[1978] Generate a personalized learning plan

[1979] The server uses an AI algorithm to automatically generate a study plan based on the received user information, including a schedule and the selection of study materials.

[1980] Submit a plan

[1981] The generated learning plan is sent from the server to the terminal, where the user can view it via a communication application.

[1982] Daily problem provision

[1983] The server generates daily study tasks based on the user's progress and sends them to the user's device at a set time. The difficulty of the tasks is adjusted according to the user's progress and level of understanding.

[1984] Receiving and judging answers

[1985] The user answers the questions and sends the answers to the server via a communication application, which immediately judges the answers and returns the results to the user in real time.

[1986] Weakness analysis and supplementary study plan

[1987] The server analyzes past learning data to identify the user's weaknesses, and then generates and provides a supplementary study plan to the user to overcome those weaknesses.

[1988] Progress monitoring and feedback

[1989] The server continuously monitors the user's learning progress, generates visual reports, and periodically sends these to the user's device.

[1990] Real-time question answering

[1991] If a user has any questions during their studies, they can ask them in real time via a communication application, and the server will use AI to generate appropriate answers and send them back to the user.

[1992] Emotion recognition and plan adjustment

[1993] The device acquires biometric information such as the user's facial expressions and voice via a communication application. The server uses an emotion recognition model to analyze the user's emotions and adjusts the learning plan and difficulty of the questions provided based on the user's state.

[1994] Specific examples

[1995] For example, if a user feels stressed while studying for a regular exam, the device's camera captures their facial expression and sends it to the server, which then detects the stress and flexibly adjusts the study plan, providing easier questions to keep the user motivated.

[1996] Prompt Sentence Examples

[1997] For example, if a system designer is considering how to use a generative AI model to tailor a learning plan, they could use a prompt like this:

[1998] "In a factory work support system, I want to assign work tasks based on the results of employee emotion analysis. For example, if it is determined that an employee is feeling stressed, how can I provide them with easier work tasks?"

[1999] This enables flexible work support according to the user's emotional state, improving employee productivity and motivation.

[2000] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2001] Step 1:

[2002] The terminal accepts basic information about learning (grade, desired school, favorite subjects, weak subjects, target score, etc.) input by the user. This information is sent to the server via a communication application. The input data includes the user's basic information. The output is sent to the server.

[2003] Step 2:

[2004] The server processes the received user information and automatically generates a study plan using an AI algorithm. At this time, the server analyzes the user's basic information, which is input data, and selects the appropriate schedule and learning materials for the study plan. The output is a customized study plan.

[2005] Step 3:

[2006] The generated study plan is sent from the server to the terminal, where the user can check it via the communication application. The input data is the generated study plan, and the output is the plan sent to the user terminal.

[2007] Step 4:

[2008] The server generates daily study tasks based on the user's progress and sends them to the user's device at a set time. The difficulty of the questions is adjusted according to the user's progress and level of understanding. The input data includes progress and past answer data, and the daily study tasks are generated as output.

[2009] Step 5:

[2010] The user answers the questions and sends the answers to the server via a communication application. The input data is the user's answer, and the output is the answer sent to the server.

[2011] Step 6:

[2012] The server immediately judges the received answer and returns the result to the user in real time. The input data is the user's answer, and the output is the judgment result. Specific operations include analyzing whether the answer is correct or incorrect.

[2013] Step 7:

[2014] The server analyzes past training data and identifies the user's weaknesses. The input data is the past training data, and the output is the identified weaknesses. The server analyzes the data using an analysis algorithm.

[2015] Step 8:

[2016] The server generates a remedial plan to overcome the weaknesses and provides it to the user. The input data is the identified weaknesses, and the output is the remedial plan. The generated remedial plan is sent to the user's terminal.

[2017] Step 9:

[2018] The server continuously monitors the user's learning progress and generates and sends visual reports. The input data is daily learning data, and the output is visual reports. Progress is recorded and analyzed using monitoring methods.

[2019] Step 10:

[2020] If a user has a question during learning, they can ask it in real time via a communication application. The server uses AI to generate an appropriate answer and sends it back to the user. The input data is the user's question, and the output is the server's answer.

[2021] Step 11:

[2022] The device acquires biometric information such as the user's facial expression and voice via a communication application. The input data is the user's facial expression and voice, and the output is the biometric information.

[2023] Step 12:

[2024] The server uses an emotion recognition model to analyze the user's emotions and adjust the difficulty of the study plan and questions provided based on that state. The input data is the acquired biometric information, and the output is the adjusted study plan and the difficulty of the questions provided. Specifically, the AI ​​model analyzes emotions and adjusts the plan based on the emotional state.

[2025] 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.

[2026] 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.

[2027] 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.

[2028] [Fourth embodiment]

[2029] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2030] 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.

[2031] 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).

[2032] 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.

[2033] 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.

[2034] 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).

[2035] 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.

[2036] 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.

[2037] 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.

[2038] 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.

[2039] 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.

[2040] 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.

[2041] 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."

[2042] This invention is a system that provides customized learning support to each student. This system is implemented via a communication application that users use on a daily basis. Below, we will show an embodiment of each of the main functions.

[2043] Customized learning plan design

[2044] 1. Enter your study information

[2045] Users input their basic information (grade, desired school, strong and weak subjects, target score, etc.) via a communication application.

[2046] The terminal transmits the input information to the server.

[2047] 2. Plan Generation

[2048] The server uses AI algorithms to generate a learning plan tailored to each student based on the information it receives.

[2049] A generating means determines the schedule, material selection, etc., and formats the individual plan for a communication application.

[2050] 3. Send plan

[2051] The transmission means transmits the generated study plan to the user's terminal.

[2052] Examples:

[2053] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[2054] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[2055] Questions provided regularly every morning

[2056] 1. Problem generation

[2057] The server generates a problem set based on the study plan.

[2058] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[2059] 2. Submit your question

[2060] The server sends the problem to the user's communication application at a set time each morning.

[2061] The user checks the question on the communication application and submits the answer.

[2062] 3. Receiving answers and judging

[2063] The terminal transmits the user's answer to the server.

[2064] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[2065] Examples:

[2066] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application.

[2067] The user answers the questions and sends the answers via a communication application.

[2068] The server automatically checks the answers and immediately sends the results.

[2069] AI support to overcome weaknesses

[2070] 1. Analysis of learning results

[2071] The server analyzes the user's past answer data and identifies weak points.

[2072] 2. Supplementary lesson plan generation

[2073] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[2074] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[2075] Examples:

[2076] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics.

[2077] The server creates a supplementary lesson plan on electromagnetics and provides related questions and explanatory videos via a communication application.

[2078] Progress monitoring and feedback transfer

[2079] 1. Collecting training data

[2080] The server periodically collects the user's learning data (daily answer results, progress, etc.).

[2081] 2. Data analysis and feedback generation

[2082] The server analyzes the collected data and evaluates the user's progress.

[2083] A monitoring means generates progress reports.

[2084] 3. Send Feedback

[2085] The transmission means transmits the generated progress report to the user via a communication application.

[2086] Examples:

[2087] The server analyzes the learning data for one week and graphs the level of understanding and progress.

[2088] The server generates a weekly report every Monday and sends it to the user via a communication application.

[2089] Real-time question answering

[2090] 1. Receiving questions

[2091] Users use a communication application to submit questions they are studying.

[2092] The terminal sends a query to the server.

[2093] 2. Response Generation

[2094] The server uses AI to analyze the received questions and generate appropriate answers.

[2095] 3. Submit your response

[2096] The sending means returns the generated answer to the communication application in real time.

[2097] Examples:

[2098] A user sends a question via a communication application saying, "I don't know how to solve a quadratic equation."

[2099] The server receives the question and uses AI to generate a detailed answer.

[2100] The server sends the answer in real time via a communication application and replies, "Please try solving it by following the steps below."

[2101] The above is a detailed description of the embodiments of the invention based on the claims.

[2102] The processing flow will be explained below.

[2103] Customized learning plan design

[2104] Step 1:

[2105] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[2106] Step 2:

[2107] The terminal transmits the input user information to the server.

[2108] Step 3:

[2109] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[2110] The server selects the schedule and study materials and creates a customized learning plan.

[2111] Step 4:

[2112] The server formats the generated lesson plan and sends it to the communication application.

[2113] Questions provided regularly every morning

[2114] Step 1:

[2115] The server generates a problem set based on the user's study plan and progress.

[2116] Step 2:

[2117] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[2118] Step 3:

[2119] The server sends selected questions to the user's communication application at a set time every morning.

[2120] Step 4:

[2121] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[2122] Step 5:

[2123] The terminal sends the user's answer to the server.

[2124] Step 6:

[2125] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[2126] AI support to overcome weaknesses

[2127] Step 1:

[2128] The server collects the user's past answer data and uses analytical algorithms to identify weaknesses.

[2129] Step 2:

[2130] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[2131] Step 3:

[2132] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[2133] Step 4:

[2134] The user advances his / her studies using the supplementary learning materials sent to him / her.

[2135] Progress monitoring and feedback transfer

[2136] Step 1:

[2137] The server collects the user's daily learning data (answers to questions, time, progress, etc.).

[2138] Step 2:

[2139] The server analyzes the data collected and evaluates learning progress and level of understanding.

[2140] Step 3:

[2141] The server generates weekly and monthly progress reports.

[2142] Step 4:

[2143] The server formats the generated report and sends it to the communication application.

[2144] Step 5:

[2145] Users can check the reports and understand their own learning status.

[2146] Real-time question answering

[2147] Step 1:

[2148] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[2149] Step 2:

[2150] The terminal sends the user's question to the server.

[2151] Step 3:

[2152] The server receives the question and uses AI to generate an appropriate answer.

[2153] Step 4:

[2154] The server sends the generated answer in real time to the communication application.

[2155] Step 5:

[2156] The user checks the answer in the communication application and resolves the question.

[2157] The above are the specific steps of the program processing of this system.

[2158] Example 1

[2159] 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."

[2160] Conventional learning support systems have difficulty fully addressing the learning progress and weaknesses of individual students, and are limited to providing uniform learning plans and feedback. Furthermore, it is difficult to provide real-time question and answer sessions and progress reports, which prevents users from maintaining their motivation to learn and from providing effective learning support.

[2161] 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.

[2162] In this invention, the server includes an input means for a user to input basic information about their studies, a generation means for receiving the input information and generating an individualized study plan using a generative AI model, a transmission means for transmitting the generated study plan to the user's device, a question providing means for generating and transmitting daily questions based on the user's progress, an answer determining means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report, a question answering means for receiving the user's questions and generating and transmitting answers using the generative AI model, a collection means for periodically collecting user data, and a formatting means for formatting the user's study plan for a communication application, thereby enabling customized study support for individual students, real-time feedback, and question answering.

[2163] "Input means" refers to a device or interface for a user to input basic information related to learning.

[2164] A "generator" is a device or algorithm that uses a generative AI model to generate an individualized learning plan based on learning information received from a user.

[2165] "Transmission means" refers to a device or function for transmitting the generated study plan, answer determination results, and visual report to the user's terminal.

[2166] A "question provider" is a device or algorithm that generates and sends daily study questions based on the user's progress.

[2167] An "answer determination means" is a device or algorithm for immediately determining an answer received from a user and transmitting the result.

[2168] "Analysis means" refers to a device or algorithm that analyzes past learning data and identifies the user's weaknesses.

[2169] A "remedial action provider" is a device or algorithm for generating and delivering a remedial action plan to address identified weaknesses.

[2170] A "monitoring means" is a device or algorithm for continuously monitoring a user's learning progress and generating and transmitting visual reports.

[2171] A "question answering means" is a device or algorithm for receiving a user's question and generating and transmitting an answer using a generative AI model.

[2172] The "collection means" is a device or algorithm for periodically collecting user learning data.

[2173] A "formatting means" is a device or algorithm for formatting the generated lesson plan into a format suitable for communication applications.

[2174] The present invention provides a learning support system that is customized for each student. This system is realized through a communication application that users use on a daily basis.

[2175] Customized learning plan design

[2176] Entering study information

[2177] 1. Users enter basic information such as their grade, preferred school, strong and weak subjects, and target score via a communication application. This can be done using a device such as a smartphone, tablet, or PC.

[2178] 2. The terminal sends the entered information to the server.

[2179] Plan Generation

[2180] 1. The server uses a generative AI model (e.g., GPT-4) based on the received information to generate a learning plan appropriate for each student.

[2181] 2. A generating means determines the schedule, material selection, etc., and formats the individual plan for the communication application.

[2182] Send Plan

[2183] 1. The transmission means transmits the generated study plan to the user's terminal.

[2184] Examples:

[2185] The user inputs their grade ("third year high school student"), their preferred school ("top university"), and their weak subject ("math"), and the server uses an AI algorithm to create a "math focus plan" and sends it to the user via a communication application.

[2186] Questions provided regularly every morning

[2187] Problem generation

[2188] 1. The server generates a problem set based on the study plan.

[2189] 2. The problem provision method selects problems of a level of difficulty that corresponds to the user's progress and level of understanding.

[2190] Submit a problem

[2191] 1. The server sends a question to the user's communication application at a set time every morning.

[2192] 2. The user checks the question on the communication application and submits the answer.

[2193] Receiving answers and judging

[2194] 1. The terminal sends the user's answer to the server.

[2195] 2. The server receives the answer and uses the judging means to determine whether it is correct or incorrect.

[2196] Examples:

[2197] The server generates five math problems for the user every morning at 8:00 and sends them via a communication application. The user answers the problems and sends the answers via the communication application. The server automatically checks the answers and immediately sends the results.

[2198] AI support to overcome weaknesses

[2199] Analysis of learning results

[2200] 1. The server analyzes the user's past answer data and identifies weak points.

[2201] Supplementary lesson plan generation

[2202] 1. The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[2203] 2. A tutoring provider selects relevant questions and materials and sends them to the user's communication application.

[2204] Examples:

[2205] Based on past answer data, the server determines that the user is weak in the electromagnetic field of physics. The server then creates a supplementary study plan for electromagnetics and provides related questions and explanatory videos via a communication application.

[2206] Progress monitoring and feedback transfer

[2207] Collection of training data

[2208] 1. The server periodically collects the user's daily learning data.

[2209] Data analysis and feedback generation

[2210] 1. The server analyzes the collected data and evaluates the user's progress.

[2211] 2. Monitoring measures generate progress reports.

[2212] Send Feedback

[2213] 1. The transmission means transmits the generated progress report to the user via a communication application.

[2214] Examples:

[2215] The server analyzes the learning data for one week and graphs the level of understanding and progress. The server generates a weekly report every Monday and sends it to the user via a communication application.

[2216] Real-time question answering

[2217] Question received

[2218] 1. The user submits a question they are studying using a communication application.

[2219] 2. The device sends a query to the server.

[2220] Response Generation

[2221] 1. The server analyzes the received question using a generative AI model and generates an appropriate answer.

[2222] Send response

[2223] 1. The generated answer is returned to the communication application in real time by the transmitting means.

[2224] Examples:

[2225] A user sends a question via a communication application, such as "I don't know how to solve a quadratic equation." The server receives the question and uses AI to generate a detailed answer. The server then sends the answer in real time via the communication application and replies, "Try solving it by following the steps below."

[2226] Prompt Sentence Examples

[2227] 1. Lesson plan generation prompt:

[2228] "Generate a study plan for a high school senior who wants to attend a top university and whose weakest subject is math."

[2229] 2. AI Q&A prompt:

[2230] "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[2231] Through this, the system is able to provide personalized and effective learning support in real time.

[2232] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2233] Step 1:

[2234] The user starts the communication application and inputs basic information such as grade level, desired school, strong and weak subjects, target score, etc. This input information includes the user's learning background and goals.

[2235] Step 2:

[2236] The device sends the entered basic information to the server. This data is sent to the server in JSON format. Here, the entered basic information is used as the transmission data as is.

[2237] Step 3:

[2238] The server uses a generative AI model to generate an individualized learning plan based on the received basic information. Specifically, the server inputs a prompt statement (e.g., "Please generate a learning plan for a high school senior who is weak in math") into the generative AI model, which then generates an appropriate learning plan. The learning plan generated as the output includes a learning schedule and recommended learning materials.

[2239] Step 4:

[2240] The server sends the generated study plan to the user's terminal. The formatting means formats the study plan into a user-friendly format and transfers it to the user's terminal. The terminal then outputs the received study plan on the application's display screen.

[2241] Step 5:

[2242] The server generates a daily problem set based on the user's progress. Specifically, it uses the user's individual learning plan and past correct / incorrect data as input data and creates a problem set using an AI model. The problem set generated as output contains problems of a difficulty level appropriate to the user's level of understanding.

[2243] Step 6:

[2244] The server sends the generated problem set to the user's terminal at a fixed time every morning. A schedule management system is used so that the user can receive the problem. The terminal displays the received problem and notifies the user.

[2245] Step 7:

[2246] The user answers the received questions using a communication application, and the user's answers are entered using the application's input form.

[2247] Step 8:

[2248] The terminal transmits the user's answers to the server, which stores the transmitted answer data in its database.

[2249] Step 9:

[2250] The server immediately judges the received answers using a judgment method, automatically classifying the answers using a generative AI model and generating a correct / incorrect result, which includes feedback on correct and incorrect answers.

[2251] Step 10:

[2252] The server sends the result of the judgment to the user's device, which then displays the received feedback on the application and notifies the user.

[2253] Step 11:

[2254] The server analyzes the user's past answer data to identify weaknesses. Here, analytical means are used to find patterns in the past data to identify the user's weaknesses. The output includes information about the identified weaknesses.

[2255] Step 12:

[2256] The server generates a remedial plan based on the identified weaknesses. Using a generative AI model, the server generates a remedial plan based on a prompt such as, "Please generate a remedial plan for students who are weak in the electromagnetic field of physics." The remedial plan includes relevant questions and explanatory materials.

[2257] Step 13:

[2258] The server sends the supplementary lesson plan to the user's terminal, which receives the supplementary lesson plan and displays it in a manner that notifies the user.

[2259] Step 14:

[2260] The server periodically collects the user's daily learning data and continuously monitors their progress. It analyzes the collected data and generates a progress report for the user. This output includes graphs and charts that visualize the user's understanding and progress.

[2261] Step 15:

[2262] The server sends the generated progress report to the user's terminal, which displays the received report and notifies the user.

[2263] Step 16:

[2264] The user sends a question during the study using a communication application, and the terminal sends the question to the server.

[2265] Step 17:

[2266] The server analyzes the received question using a generative AI model and generates an appropriate answer. Specifically, it receives a prompt such as, "I don't know how to solve a quadratic equation, so please explain it to me step by step."

[2267] Step 18:

[2268] The server sends the generated answers back to the user in real time via a communication application, where the user can check the answers and use them for learning.

[2269] The above is the specific processing flow in this system.

[2270] (Application example 1)

[2271] 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."

[2272] Conventional learning support systems have difficulty providing customized learning plans based on each student's learning progress and level of understanding. They also face challenges in providing individual questions, feedback, and answering questions in real time. This can prevent students from learning efficiently and can result in insufficient support for overcoming weaknesses in specific subjects or areas.

[2273] 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.

[2274] In this invention, the server includes: an input means for a user to input basic information related to their studies; a generation means for receiving the input information and generating an individualized study plan using an AI algorithm; a transmission means for transmitting the generated study plan to the user's terminal; a question providing means for generating and transmitting daily questions based on the user's progress; an answer determination means for determining answers received from the user and transmitting the results; an analysis means for analyzing past study data and identifying the user's weaknesses; a supplementary study providing means for generating and transmitting a supplementary study plan to overcome the identified weaknesses; a monitoring means for continuously monitoring the user's study progress and generating and transmitting a visual report; a question answering means for receiving the user's questions and generating and transmitting answers using AI; a content providing means for delivering customized study content to the user based on a generative AI model; a notification means for generating a question set at a fixed time every morning and notifying the user; and an answer reception determination means for receiving the user's answers in real time and automatically determining them. This makes it possible to deliver learning plans and content optimized for each student, and by providing questions and feedback in real time according to individual progress, it is possible to improve learning efficiency and comprehension.

[2275] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[2276] A "generation method" is a process or system that uses AI algorithms to generate an individualized learning plan based on input information.

[2277] "Transmission means" refers to the communication technology or method for transmitting the generated learning plan and data to the user's terminal.

[2278] "Question providing means" refers to a function or system that generates and sends daily questions based on the user's progress.

[2279] An "answer determination means" is a process or algorithm for determining the answers received from the user and transmitting the results.

[2280] "Analysis means" refers to a method or system for analyzing past learning data and identifying the user's weaknesses.

[2281] A "remedial action delivery vehicle" is a process or system that generates and delivers a remedial action plan to address identified weaknesses.

[2282] "Monitoring means" refers to functions and methods for continuously monitoring a user's learning progress and generating and sending visual reports.

[2283] A "question answering tool" is a system or process that receives a user's question and uses AI to generate and send an answer.

[2284] "Content provision means" refers to the function or method for delivering customized learning content to users based on a generative AI model.

[2285] The "notification means" is a system or method for generating a problem set at a fixed time every morning and notifying the user.

[2286] The "answer reception and determination means" is a process or algorithm for receiving the user's answers in real time and automatically determining them.

[2287] This invention is a system that provides customized learning support to each student. This system has multiple functions and can be used by users using everyday devices such as smartphones. The main functions and their implementation modes are described below.

[2288] Entering study information

[2289] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[2290] Customized learning plan design

[2291] The server uses an AI algorithm to generate an individualized learning plan based on the received user information. The plan is then sent to the user's device, where the user can review it and work on their daily studies.

[2292] Questions provided regularly every morning

[2293] The server generates a set of questions based on the user's study plan and progress at a fixed time every morning and notifies the user's device. The user can then check the questions on the application and submit their answers.

[2294] Receiving answers and judging

[2295] The server receives the answers sent by the user in real time and automatically judges them. The results are immediately sent to the user's device, allowing the user to check their level of understanding as they continue their studies.

[2296] AI support to overcome weaknesses

[2297] The server analyzes past learning data to identify the user's weaknesses, then generates a supplementary study plan to overcome the identified weaknesses and provides the user with relevant learning materials and questions.

[2298] Progress monitoring and feedback transfer

[2299] The server continuously monitors the user's learning progress and generates visual reports, such as weekly or monthly progress reports, which are sent to the user's device, allowing the user to understand their own learning progress.

[2300] Real-time question answering

[2301] Users can send questions that arise during their studies to the server via a communication application. The server uses an AI algorithm to analyze the questions, generate appropriate answers in real time, and return them to the user.

[2302] Details of content delivery methods

[2303] The server delivers customized learning content to users based on generative AI models, making it possible to provide a learning experience optimized for each individual student, rather than the traditional one-size-fits-all approach.

[2304] Examples of prompt statements

[2305] "If a user inputs their preferred school as a 'top university' and their weakest subject as 'math', generate a 'math focus plan' based on the identified information and deliver it to them via a communication application. Also, deliver a set of math problems to the user every morning at 8:00, check the answers in real time, and provide feedback."

[2306] This system configuration enables learning support customized for each student, enabling efficient learning progress and improved understanding.

[2307] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2308] Step 1: Enter your study information

[2309] Users use devices such as smartphones to input basic information about their studies, including their grade, preferred school, favorite subjects, weak subjects, and target scores. The input information is then sent to a server using communication technology.

[2310] Input: Grade, desired school, strong subjects, weak subjects, target score

[2311] Output: User basic information sent to the server

[2312] Step 2: Generate a customized study plan

[2313] The server uses an AI algorithm to generate an individual learning plan based on the received user information. Specifically, the generative AI model analyzes the user's profile and plans the optimal learning materials, schedule, and learning progress method.

[2314] Input: User basic information

[2315] Output: A customized study plan

[2316] Step 3: Submit your study plan

[2317] The server sends the generated study plan to the user's device, and the user can open the application from the notification and view the provided study plan.

[2318] Input: Customized Study Plan

[2319] Output: The learning plan displayed on the user's device

[2320] Step 4: Provide regular questions every morning

[2321] The server generates a set of questions every morning at a fixed time based on the user's study plan and progress, and notifies the user's device. The difficulty of the questions is adjusted according to the user's level of understanding.

[2322] Input: Study plan, progress data

[2323] Output: A set of questions to be sent to you every morning

[2324] Step 5: Submit your answers and have them graded

[2325] The user answers questions provided on the application. The answers are sent from the device to the server, which judges the answers in real time and provides immediate visual feedback on whether the answer is correct or incorrect.

[2326] Input: User's answer

[2327] Output: Feedback of correct / incorrect results

[2328] Step 6: AI support to overcome weaknesses

[2329] The server analyzes past learning data to identify the user's weaknesses, generates a remedial study plan based on the identified weaknesses, and provides the user with relevant learning materials and questions.

[2330] Input: Past training data

[2331] Output: Supplementary lesson plan, related questions and materials

[2332] Step 7: Progress monitoring and feedback

[2333] The server continuously monitors the user's learning progress and generates visual reports, for example, weekly or monthly progress reports, which are sent to the user's device.

[2334] Input: Learning progress data

[2335] Output: Visual report

[2336] Step 8: Real-time question answering

[2337] Users can send questions that arise during their studies to the server via a communication application, and the server will analyze the questions using an AI algorithm, generate appropriate answers in real time, and send them back to the user.

[2338] Input: User question

[2339] Output: AI-generated answer

[2340] Step 9: Content Delivery Methods

[2341] The server delivers customized learning content based on generative AI models, providing users with individually optimized learning materials and tests.

[2342] Input: Database of learning content, user characteristics

[2343] Output: Customized learning content

[2344] Step 10: Set a notification method every morning

[2345] The server generates a set of questions every morning at a fixed time and notifies the user's device. When the user receives the notification, they can open the application and check the questions.

[2346] Input: Study schedule, question database

[2347] Output: Problem notification every morning

[2348] Step 11: Answer reception determination means

[2349] When a user submits their answer, the server receives it in real time and automatically judges it, with the results immediately sent to the user's device.

[2350] Input: User's answer

[2351] Output: Feedback of the judgement result

[2352] 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.

[2353] This invention is a system that provides flexible and efficient learning support tailored to the user's emotional state by combining an emotion engine with an individual learning support system. This system is implemented via a communication application that the user uses on a daily basis. Below, we will show an embodiment of each of the main functions.

[2354] Customized learning plan design

[2355] 1. Enter your study information

[2356] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[2357] The terminal transmits the input information to the server.

[2358] 2. Plan Generation

[2359] The server uses AI algorithms based on the received information to generate a personalized learning plan for the user.

[2360] The generation means selects schedules and learning materials to create customized learning plans.

[2361] 3. Send plan

[2362] The transmission means transmits the generated study plan to the user's terminal.

[2363] Examples:

[2364] The user enters his / her grade as "third year high school student," his / her desired school as "top university," and his / her weakest subject as "math."

[2365] The server uses an AI algorithm to create a "mathematics focus plan" and sends the contents to the user via a communication application.

[2366] Questions provided regularly every morning

[2367] 1. Problem generation

[2368] The server generates a problem set based on the study plan.

[2369] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[2370] 2. Submit your question

[2371] The server sends the problem to the user's communication application at a set time each morning.

[2372] The user checks the questions on the communication application and inputs the answers.

[2373] 3. Receiving answers and judging

[2374] The terminal transmits the user's answer to the server.

[2375] The server receives the answer and uses the determining means to determine whether it is correct or incorrect.

[2376] AI support to overcome weaknesses

[2377] 1. Analysis of learning results

[2378] The server collects the user's past answer data and uses analytical algorithms to identify weak spots.

[2379] 2. Supplementary lesson plan generation

[2380] The server uses AI to generate a remedial lesson plan that focuses on the identified weaknesses.

[2381] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[2382] Progress monitoring and feedback transfer

[2383] 1. Collecting training data

[2384] The server periodically collects the user's daily learning data (answers to questions, time, progress, etc.).

[2385] 2. Data analysis and feedback generation

[2386] The server analyzes the collected data and evaluates learning progress and level of understanding.

[2387] A monitoring means generates progress reports.

[2388] 3. Send Feedback

[2389] The transmission means transmits the generated progress report to the user via a communication application.

[2390] Real-time question answering

[2391] 1. Receiving questions

[2392] Users use a communication application to input and send questions or concerns they have about their studies.

[2393] 2. Response Generation

[2394] The server uses AI to analyze the received questions and generate appropriate answers.

[2395] 3. Submit your response

[2396] The sending means returns the generated answer to the communication application in real time.

[2397] Introducing the Emotion Engine

[2398] 1. Acquiring Emotion Data

[2399] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[2400] 2. Sentiment Analysis

[2401] The server uses emotion recognition means to analyze the user's emotions and identify the current emotional state.

[2402] 3. Plan adjustment

[2403] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[2404] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[2405] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[2406] Examples:

[2407] If it is determined that the user is feeling stressed by the questions, the server provides easier questions based on that data to maintain the user's motivation.

[2408] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[2409] The above is a detailed description of an embodiment of the present invention. This system provides personalized learning support that takes into account the user's emotions, thereby realizing a more effective learning environment.

[2410] The processing flow will be explained below.

[2411] Customized learning plan design

[2412] Step 1:

[2413] A user accesses a communication application and inputs basic information such as grade level, desired school, favorite subjects, weak subjects, and target score.

[2414] Step 2:

[2415] The terminal transmits the input user information to the server.

[2416] Step 3:

[2417] Based on the information received by the server, an AI algorithm is used to generate an individual learning plan for each user.

[2418] The server selects the schedule and study materials and creates a customized learning plan.

[2419] Step 4:

[2420] The server formats the generated lesson plan and sends it to the communication application.

[2421] Questions provided regularly every morning

[2422] Step 1:

[2423] The server generates a problem set based on the user's study plan and progress.

[2424] Step 2:

[2425] The server uses AI to select questions of a difficulty level that adapts to the user's learning progress.

[2426] Step 3:

[2427] The server sends selected questions to the user's communication application at a set time every morning.

[2428] Step 4:

[2429] The user opens the communication application in the morning, checks the questions that have been sent, and enters the answers.

[2430] Step 5:

[2431] The terminal sends the user's answer to the server.

[2432] Step 6:

[2433] The server receives the answer, makes an immediate decision, and sends the result to the communication application.

[2434] AI support to overcome weaknesses

[2435] Step 1:

[2436] The server collects the user's past answer data and learning outcomes and uses analytical algorithms to identify weak points.

[2437] Step 2:

[2438] The server uses AI to generate a supplementary study plan to overcome weaknesses.

[2439] Step 3:

[2440] The server selects questions and supplementary materials tailored to specific weaknesses and sends them to the communication application.

[2441] Step 4:

[2442] The user advances his / her studies using the supplementary learning materials sent to him / her.

[2443] Progress monitoring and feedback transfer

[2444] Step 1:

[2445] The server periodically collects the user's daily learning data (answer results, time spent studying, progress, etc.).

[2446] Step 2:

[2447] The server analyzes the data collected and evaluates the progress of learning and level of understanding.

[2448] Step 3:

[2449] The server generates weekly and monthly progress reports.

[2450] Step 4:

[2451] The server formats the generated report and sends it to the communication application.

[2452] Step 5:

[2453] Users can check the reports and understand their own learning status.

[2454] Real-time question answering

[2455] Step 1:

[2456] The user inputs and sends any doubts or questions they may have about their studies through a communication application.

[2457] Step 2:

[2458] The terminal transmits the user's question data to the server.

[2459] Step 3:

[2460] The server receives the question data and uses AI to generate appropriate answers.

[2461] Step 4:

[2462] The server returns the generated answer to the communication application in real time.

[2463] Step 5:

[2464] The user checks the answer in the communication application and resolves the question.

[2465] Introducing the Emotion Engine

[2466] Step 1:

[2467] The terminal acquires biometric signals such as the user's facial expressions and voice via a communication application and transmits them to the emotion recognition means.

[2468] Step 2:

[2469] The server uses emotion recognition means to analyze the user's emotions and identify their current emotional state.

[2470] Step 3:

[2471] The server adjusts the difficulty of the study plan and questions provided based on the emotional state.

[2472] If the user is feeling stressed, easier problems will be provided to help increase motivation.

[2473] If the user is in a positive emotional state, the current plan is maintained or made more difficult, promoting deeper learning.

[2474] Examples:

[2475] If it is determined that the user is feeling stressed by the questions, the server will provide easier questions based on that data, maintaining the user's motivation to study.

[2476] If the user is determined to be highly motivated, the server will provide them with moderately difficult problems with detailed explanations.

[2477] Example 2

[2478] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2479] Conventional learning support systems lack the ability to provide appropriate feedback based on individual users' progress and weaknesses, and are unable to flexibly adjust learning plans that take into account the user's emotional state. Furthermore, they lack the ability to provide real-time answer assessment and question response, making it difficult to maximize the user's motivation and efficiency in learning. These issues need to be addressed.

[2480] 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.

[2481] In this invention, the server includes a generation means for receiving input information and generating an individualized study plan using an AI algorithm, a transmission means for transmitting the generated study plan to the user's computer, a problem provision means for generating and transmitting daily problems based on the user's progress, an answer determination means for determining answers received from the user and transmitting the results, an analysis means for analyzing past study data and identifying the user's weaknesses, a supplementary study provision means for generating and transmitting a supplementary study plan to overcome the identified weaknesses, and an emotion engine means for acquiring and analyzing the user's emotional data and adjusting the study plan based on the user's current emotional state. This enables flexible adjustment of the study plan according to the individual's learning situation, real-time answer determination, and question response, thereby improving the user's learning efficiency and motivation.

[2482] "Input means" refers to a device or interface that allows a user to input basic information about their studies.

[2483] "Generation means" refers to a device or program that has the function of generating an individualized learning plan using an AI algorithm based on the information received.

[2484] "Transmission means" refers to a device or function for transmitting the generated lesson plan and other data to the user's computer.

[2485] A "problem provider" is a device or system that has the function of generating and transmitting daily problems based on the user's progress.

[2486] The "answer determination means" refers to a device or algorithm that has the function of analyzing the answer received from the user, determining whether it is correct or incorrect, and transmitting the result.

[2487] "Analysis means" refers to devices or algorithms that have the ability to analyze past learning data and identify the user's weaknesses.

[2488] The "tuition provision means" refers to a device or system for generating a tutoring plan to overcome the identified weaknesses and providing it to the user.

[2489] "Monitoring means" refers to a device or system that has the ability to continuously monitor a user's learning progress and generate visual reports.

[2490] A "question answering means" is a device or system that has the function of receiving a user's question, using AI to generate an appropriate answer, and sending it.

[2491] The "emotion engine means" refers to a device or system that has the function of acquiring and analyzing the user's emotional data and adjusting the learning plan and content based on the user's emotional state.

[2492] This invention is a system that provides flexible and efficient learning support according to the user's emotional state by combining an emotion engine with an individual learning support system. Below, an embodiment of each main function is shown.

[2493] Customized learning plan design

[2494] 1. Enter your study information

[2495] The user inputs their basic information (grade, desired school, favorite subjects, weak subjects, target score, etc.) via a communication application.

[2496] The terminal transmits the input information to the server.

[2497] 2. Create a learning plan

[2498] The server uses AI algorithms (e.g., TensorFlow, PyTorch) based on the received information to generate a personalized learning plan for the user.

[2499] The generation means selects schedules and learning materials to create customized learning plans.

[2500] 3. Submit your study plan

[2501] The server transmits the generated study plan to the user's terminal.

[2502] The user views the customized learning plan on the device.

[2503] Questions provided regularly every morning

[2504] 1. Problem Generation

[2505] The server generates daily problem sets based on the user's study plan.

[2506] The problem providing means selects problems of a level of difficulty according to the user's progress and level of understanding.

[2507] 2. Submit your issue

[2508] The server sends the problem to the user's communication application at a fixed time each morning.

[2509] The user checks the questions on the communication application and inputs the answers.

[2510] 3. Receiving and judging answers

[2511] The terminal transmits the user's answer to the server.

[2512] The server receives the answers and uses an AI algorithm to determine whether they are correct.

[2513] AI support to overcome weaknesses

[2514] 1. Analysis of learning results

[2515] The server collects the user's past answer data and uses analytical means to identify weak points.

[2516] 2. Generate a supplementary lesson plan

[2517] The server uses AI to generate a remedial plan to address identified weaknesses.

[2518] A tutoring provider selects relevant questions and materials and transmits them to the user's communication application.

[2519] Progress monitoring and feedback transfer

[2520] 1. Collecting training data

[2521] The server periodically collects the user's daily learning data (answers to questions, learning time, progress, etc.).

[2522] 2. Data analysis and feedback generation

[2523] The server analyzes the collected data and generates progress reports.

[2524] A monitoring means generates progress reports.

[2525] 3. Submitting Feedback

[2526] The server sends the generated progress report to the user via a communication application.

[2527] Real-time question answering

[2528] 1. Receiving Questions

[2529] Users use a communication applicat...

Claims

1. an input means for a user to input basic information about the study; a generating means for receiving the input information and generating a personalized learning plan using an AI algorithm; a transmitting means for transmitting the generated learning plan to the user's terminal; a problem providing means for generating and transmitting daily problems based on the user's progress; an answer determination means for determining the answer received from the user and transmitting the result; an analytical means for analyzing past learning data and identifying the user's weaknesses; a remedial training provision means for generating and transmitting a remedial training plan to address the identified weaknesses; monitoring means for continuously monitoring the user's learning progress and generating and transmitting visual reports; A question answering means for receiving a user's question, generating an answer using AI, and transmitting the answer; A system including:

2. 2. The system according to claim 1, wherein the daily problem providing means selects problems of an appropriate level of difficulty based on the user's study plan and progress.

3. 2. The system according to claim 1, wherein the answer determining means immediately determines the user's answer and transmits the result in real time.

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