System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving child demographic information based on input from a user; means for generating an initial educational program based on historical winner and deviation information using a generation AI; means for delivering the generated educational program to a client device; means for collecting test results and learning progress information; means for analyzing the collected information and adjusting the educational program; and means for delivering the adjusted educational program back to the client device.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 parents face the challenge of not knowing what specific learning plan to create when trying to provide their children with a good education and prepare them for university entrance in the future. Furthermore, in dual-income households, while they may have financial means, they lack the time to provide effective educational support within their limited time. Furthermore, parents' educational knowledge and experience vary widely, leading to inequality in children's educational opportunities. The purpose of this invention is to solve these issues and ensure that children can receive the best possible learning experience in any home environment. [Means for solving the problem]

[0005] The present invention is an educational support system that utilizes generative AI and solves the problems by the following means.

[0006] First, the system provides a means for users to input basic information about their child (age, current grades, target university, etc.) Based on this information, the AI ​​will refer to past successful applicant data and deviation score information to generate an initial educational program.

[0007] The generated educational program is then distributed to the client device, allowing the child to study according to the daily study plan. The device includes a means for collecting study results and test results and transmits the data to the server. The server provides a means for analyzing the data and automatically adjusting the study program based on the data.

[0008] The system also includes a means for delivering the adjusted learning program back to the client terminal and visually displaying the learning progress accordingly, allowing parents to efficiently check the learning progress and provide support as needed. In this way, the system provides a system that allows children in every household to receive the best educational opportunities.

[0009] "Users" refer to parents and children who use the educational program, and are the entities that provide input data to the system and progress through the learning process.

[0010] "Generative AI" refers to artificial intelligence technology that uses algorithms and data analysis to automatically generate and tailor children's learning programs.

[0011] A "client terminal" refers to a device that is primarily used by children for their daily learning, and is used to run educational programs and input results.

[0012] "Initial educational program" refers to a study plan created by the generation AI based on basic information provided by the user, with reference to past successful applicant data and deviation score information.

[0013] "Test Results" refers to data including scores and evaluations on tests administered by a child in accordance with a learning program.

[0014] "Learning Progress Data" refers to data containing information about the progress and achievement of daily learning activities.

[0015] "Analysis" refers to the data analysis process carried out by the generation AI based on collected test results and learning progress data, which is used to adjust the learning program.

[0016] "Visual display" refers to providing learning progress and results to users in a visual format such as graphs and charts, allowing parents to effectively check learning progress.

[0017] "Adjusting the learning program" refers to the process by which generative AI reviews the current learning program and makes necessary changes based on the collected data.

[0018] "Re-delivery" means sending a new, adjusted learning program back to the client terminal, allowing the child to follow the latest learning plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0027] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0040] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments of the invention are described below.

[0041] Server Operation

[0042] The server receives basic information about the child (age, current grades, target university, etc.) entered by the user, and uses this information to create an initial educational program using generative AI. This program is individually optimized by referencing past successful applicant data and deviation score information.

[0043] The generated educational program is distributed to the client device. As the child's learning progresses, the server collects daily learning progress data and test results from the client device. This data is analyzed and the learning program is automatically adjusted. The adjusted program is then distributed again to the client device, allowing the child to continue learning.

[0044] Client terminal operation

[0045] The client device receives the educational program distributed from the server and provides it to the child. Specifically, daily study plans and assignments are displayed on the device screen. As the child progresses with their studies and takes tests, the results are sent to the server via the device.

[0046] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Visual feedback such as graphs and charts is provided, allowing parents to intuitively understand the effectiveness of their children's learning.

[0047] User Actions

[0048] Parents and children use the system through client terminals. Parents first enter basic information about their children and then check the generated educational program. The children then study according to the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[0049] Specific examples

[0050] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[0051] On Monday morning, when a child opens their client device (iPad), the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. These results are sent to the server, which analyzes the data and adjusts the study program accordingly.

[0052] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[0053] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user uses a dedicated application to input basic information about their child (age, current grades, target university, etc.), and the server receives this information and stores it in a database.

[0057] Step 2:

[0058] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[0059] Step 3:

[0060] The server distributes the generated initial education program to the client terminal, which receives the program and displays it as a daily learning plan.

[0061] Step 4:

[0062] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal has the function of recording the learning progress and sending the results to the server.

[0063] Step 5:

[0064] The client terminal provides an interface for inputting the child's learning results (e.g., test scores), which are then sent to the server.

[0065] Step 6:

[0066] The server analyzes the received learning result data and evaluates the student's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[0067] Step 7:

[0068] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[0069] Step 8:

[0070] Users (parents) can visually check their child's learning progress data and test results through a client device. The data is displayed in graphs and charts, allowing them to intuitively understand their child's learning situation.

[0071] Step 9:

[0072] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[0073] Step 10:

[0074] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[0075] Example 1

[0076] 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."

[0077] In today's educational environment, it is difficult for parents to accurately track their children's learning progress and create optimal learning plans. It is also difficult to monitor children's learning habits and achievements in real time and provide timely feedback. This creates a problem of insufficient efficient learning support aimed at passing specific target universities.

[0078] 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.

[0079] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for using a generation AI to generate an initial educational program based on past successful applicant data and deviation score information, means for distributing the generated educational program to a client terminal, means for the terminal to visually display the educational program, means for the child to progress in their studies, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, and means for distributing the adjusted learning program again to the client terminal. This makes it possible to accurately grasp a child's learning progress and provide an optimal learning plan.

[0080] "User" refers to the parent or guardian who enters and manages their child's learning program.

[0081] "Child" refers to the student for whom the study program is being carried out.

[0082] "Basic information" refers to data necessary to generate a learning program, such as the child's age, current grades, and target university.

[0083] "Generative AI" refers to a system that uses artificial intelligence technology to create a learning program based on past successful applicant data and deviation score information.

[0084] "Successful applicant data" refers to information regarding the grades and learning history of students who have been accepted into specific target universities in the past.

[0085] "Standard deviation information" refers to statistical data that serves as a standard for evaluating learning content and test results.

[0086] "Initial educational program" refers to a learning plan created by the generative AI based on input data from the user.

[0087] "Client terminal" refers to a device used by a user or child to operate the system, and specifically includes a smartphone or tablet.

[0088] "Means for visually displaying educational programs" refers to the function of displaying learning plans and assignments on the device screen in an intuitive manner.

[0089] "Learning Progress Data" refers to information about a child's progress and achievement as they progress through their education.

[0090] "Test results" refers to the test scores and evaluation results a child has taken.

[0091] "Means for analyzing data" refers to computational processes for optimizing learning programs based on collected learning progress data and test results.

[0092] "Means of adjusting learning programs" refers to the process of making changes or additions to existing learning programs based on data analysis.

[0093] "Generative AI model" refers to the algorithms and data models used by generative AI.

[0094] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments are described below.

[0095] Overall system configuration

[0096] This educational support system consists of three components: a server, a client terminal, and a user. The server generates learning programs using a generative AI model and operates based on user input data. The client terminal provides learning programs to users and transmits progress data and test results to the server. Users utilize the system through system operations and learning activities.

[0097] Server Operation

[0098] The server performs the following series of processes.

[0099] 1. Receiving basic information:

[0100] The server receives basic information about the child (age, current grades, target university) entered by the user. For example, a parent might enter data such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points."

[0101] 2. Creation of an initial education program:

[0102] Based on the received basic information, the server uses a generative AI model to generate an initial educational program. The generative AI model references past successful applicant data and deviation score information to create an individually optimized study plan. Specifically, it generates a plan such as "Mathematics: 1 hour, English: 30 minutes, History: 45 minutes."

[0103] 3. Program Delivery:

[0104] The generated educational program is distributed from the server to the client terminal using cloud infrastructure (e.g., AWS or GCP).

[0105] 4. Progress data collection and analysis:

[0106] When a child's learning progress data and test results are sent from the device, the server collects and analyzes the data, and automatically adjusts the content of the learning program based on the analysis results.

[0107] 5. Adjusted Program Re-Delivery:

[0108] The new, adjusted learning program is then sent back to the client device, allowing the child to continue learning optimally.

[0109] Client terminal operation

[0110] The client terminal receives the educational program distributed from the server and operates as follows.

[0111] 1. Display of educational programs:

[0112] The device visually displays the delivered educational program to the child. The daily learning plan and assignments are displayed on the screen. For example, when a child opens a tablet (such as an iPad), the learning plan for that day (e.g., "30 minutes of English vocabulary, 60 minutes of math workbooks, 30 minutes of history") is displayed.

[0113] 2. Learning Progression:

[0114] Children learn by following a program displayed on the device, for example, memorizing a list of English words or solving math problems.

[0115] 3. Enter and submit test results:

[0116] Once the child has finished studying, they take a test and enter the results into the device. The entered test results are sent to the server via the device. For example, data such as "English vocabulary test: 90 points" is sent.

[0117] 4. View progress data:

[0118] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Graphs and charts can be used to intuitively understand the effectiveness of learning.

[0119] User Actions

[0120] 1. Enter basic information:

[0121] The user (parent) first enters basic information about their child into the client terminal, following prompts such as, "How old is your child?", "Please enter their current grades.", and "Please tell us your target university."

[0122] 2. Implementation of the learning program:

[0123] Children follow the program displayed on the device to study, and when they finish, they enter and submit their test results.

[0124] 3. Check your progress:

[0125] Parents can check their child's learning progress through the device and provide feedback as needed. For example, in the evening, a parent can check their child's learning progress on the device and see details such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding."

[0126] As described above, the present invention can reduce the burden on users and provide optimized learning support, thereby enabling efficient and effective learning support aimed at helping users pass the entrance exams to specific target universities.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] The user enters basic information about the child.

[0130] (Specific action)

[0131] The user (parent) enters basic information about their child (age, grades, target university, etc.) into the client terminal. For example, information such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points" is entered into the input form.

[0132] (input)

[0133] Age, grades, target university

[0134] (output)

[0135] Basic information is sent to the server

[0136] Step 2:

[0137] The server receives the basic information.

[0138] (Specific action)

[0139] The server receives basic information sent from the client terminal, which is then stored in a database.

[0140] (input)

[0141] Basic information sent from the client terminal

[0142] (output)

[0143] Basic information is stored on the server

[0144] Step 3:

[0145] The server generates the initial training program.

[0146] (Specific action)

[0147] The server uses a generative AI model to generate an initial educational program based on basic information. The generative AI model references past successful applicant data and deviation score information. For example, a plan such as "Math: 1 hour, English: 30 minutes, History: 45 minutes" may be generated.

[0148] (input)

[0149] Basic information, successful applicant data, deviation score information

[0150] (output)

[0151] Early Education Program

[0152] Step 4:

[0153] The server distributes the educational program to the terminal.

[0154] (Specific action)

[0155] The server distributes the generated educational programs to client devices via HTTPS protocol and AWS / GCP cloud infrastructure.

[0156] (input)

[0157] Early Education Program

[0158] (output)

[0159] The educational program is sent to the device.

[0160] Step 5:

[0161] The terminal displays the educational program.

[0162] (Specific action)

[0163] The client device displays the received educational program on its user interface. When a child opens the device, a learning plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history" appears on the screen.

[0164] (input)

[0165] Educational programs sent from the server

[0166] (output)

[0167] The educational program will be displayed on the device.

[0168] Step 6:

[0169] Children progress in their learning.

[0170] (Specific action)

[0171] Children learn by following educational programs displayed on the device, for example, memorizing a list of English words and solving math problems with a digital pen.

[0172] (input)

[0173] Educational Program

[0174] (output)

[0175] Learning progress data

[0176] Step 7:

[0177] The device sends the test results to the server.

[0178] (Specific action)

[0179] After the child has finished studying, they enter their test results into the device and send them to the server. For example, they might enter "90 points" as the test result for English vocabulary.

[0180] (input)

[0181] Test Results

[0182] (output)

[0183] Test results are sent to the server

[0184] Step 8:

[0185] The server analyzes the data and adjusts the learning program.

[0186] (Specific action)

[0187] The server analyzes the collected test results and learning progress data and uses a generative AI model to adjust the learning program, for example, increasing the difficulty level if the student performs well in English and adding review if the student performs poorly.

[0188] (input)

[0189] Test results, learning progress data

[0190] (output)

[0191] Tailored Learning Programs

[0192] Step 9:

[0193] The server distributes the updated educational program to the terminal.

[0194] (Specific action)

[0195] The adjusted new learning program is again distributed from the server to the client terminal.

[0196] (input)

[0197] Tailored Learning Programs

[0198] (output)

[0199] Updated educational programs are sent to the device.

[0200] Step 10:

[0201] The terminal displays the updated educational program.

[0202] (Specific action)

[0203] The client device will then display the redistributed educational program again. When the child opens the device, they will see a lesson plan for the next day, such as "40 minutes of English vocabulary, 70 minutes of math problems, and 30 minutes of reviewing new history."

[0204] (input)

[0205] Updated Educational Programs

[0206] (output)

[0207] The updated educational program will be displayed on the device.

[0208] These are the specific processing steps of this system, which makes it possible to properly manage a child's learning progress and provide an optimal learning plan.

[0209] (Application example 1)

[0210] 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."

[0211] Conventional educational support systems have struggled to efficiently track daily learning progress and provide real-time feedback. Furthermore, the lack of an optimal system for utilizing client devices such as smartphones and head-mounted displays made it difficult for students to follow an optimal learning plan. This left students with an unclear path to success at their desired educational institution.

[0212] 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.

[0213] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI, means for distributing the generated educational program to a client terminal, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, means for distributing the adjusted learning program again to the client terminal, means for visually displaying the learning progress data and test results, means for providing real-time visual feedback, and means for using a client terminal installed on a smartphone or head-mounted display. This allows users to efficiently track their learning progress and receive real-time feedback, enabling them to study effectively to pass their target educational institution according to an optimal learning plan.

[0214] "Users" refers to parents and children who use the system.

[0215] "Basic information" refers to information such as the child's age, current grades, and target university.

[0216] "Generative AI" refers to artificial intelligence that generates optimal educational programs based on user data, past successful applicant data, and deviation score information.

[0217] An "educational program" refers to a plan that outlines the learning plans and assignments a child needs to pass in order to be accepted into their desired educational institution.

[0218] "Client terminal" refers to a device that is directly operated by a user, such as a smartphone, tablet, or head-mounted display.

[0219] "Test results" refers to test scores and evaluation information taken by a child.

[0220] "Learning progress data" refers to a child's progress and achievements as they progress through their daily studies.

[0221] "Analysis" refers to the process of analyzing collected test results and learning progress data using machine learning and statistical methods.

[0222] "Feedback" refers to the improvements and advice that the AI ​​provides to children based on the analysis results.

[0223] "Visual feedback" refers to providing users with an intuitive understanding of their learning progress and results using graphs, charts, etc.

[0224] "Real-time" refers to data being processed almost instantly, with results and feedback provided without delay.

[0225] "Target university" refers to the specific educational institution that a child aspires to attend.

[0226] A "head-mounted display" refers to a display device that is worn on the user's head.

[0227] This invention is an educational support system that uses generative AI, and aims to provide an optimal learning plan based on input from users (parents and children) and to progress learning according to that plan. Specific embodiments of the invention are described below.

[0228] Server Operation

[0229] The server first receives basic information about the child (age, current grades, target university, etc.) entered by the user. Based on this information, the server uses a generation AI to reference data on past successful applicants and deviation scores to generate an initial educational program. The generated program is then distributed to the client device.

[0230] As the learning progresses, the server collects daily learning progress data and test results from the client device. This collected data is analyzed and the learning program is automatically adjusted. The adjusted program is then sent back to the client device, allowing the learning to continue.

[0231] The main software used is a generative AI model (e.g., OpenAI's API), which analyzes data and generates programs.

[0232] Client terminal operation

[0233] The client device (smartphone or head-mounted display) receives the educational program distributed from the server and provides it to the child. Specifically, the program displays daily learning plans and assignments on the device screen.

[0234] As children study and take tests, the results are sent to the server via the client device. The device also has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning status in real time. Visual feedback allows parents to intuitively understand the effectiveness of their children's learning.

[0235] User Actions

[0236] Parents and children use this system through client terminals. Parents first enter basic information about their children and then check the generated educational program. Children follow the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[0237] Specific examples

[0238] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[0239] On Monday morning, when a child opens their smartphone, the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. The results are sent to a server, which analyzes the data and adjusts the study program accordingly.

[0240] Specific examples of prompts include:

[0241] User information: Age 16, current grades: 85 in math, 78 in English, 90 in history, target university: University A

[0242] Generate the best study plan to get into your target university.

[0243] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[0244] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[0245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0246] Step 1:

[0247] The user uses a client device to input basic information about their child. The user enters information such as age, current grades, and target university into the device's input screen, and the information is sent to the server. The input information is used as basic data for the generative AI model to generate an educational program. Examples of input data include "age: 16 years old," "math: 85 points," "English: 78 points," "history: 90 points," and "target university: University A."

[0248] Step 2:

[0249] The server generates an educational program using a generative AI model. Based on the basic information received from the user, the server references past successful applicant data and deviation score information to generate a prompt. For example, a prompt such as "User information: age 16, current grades 85 points in math, 78 points in English, 90 points in history, target university A. Please generate the optimal study plan to pass the target university entrance exam." is generated. This prompt is input into the generative AI model, which outputs an optimized educational program.

[0250] Step 3:

[0251] The generated educational program is delivered to the client terminal. The server sends the plan obtained from the generative AI model to the client terminal. For example, the generated program is a specific study plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history." This program is displayed on the terminal screen, and the user can use it to proceed with their studies.

[0252] Step 4:

[0253] The user studies and inputs test results and learning progress data. The child progresses with their daily studies and takes tests, which are then entered into the client terminal. For example, data such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding" are entered. This input data is sent to the server.

[0254] Step 5:

[0255] The server collects and analyzes test results and learning progress data. The server collects and analyzes data sent from client devices. This analysis uses machine learning algorithms and statistical methods. For example, if a student's understanding of a particular subject is insufficient, the server adjusts the learning plan to strengthen the content of that subject. The analysis results become the basis for a new learning program.

[0256] Step 6:

[0257] The adjusted learning program is then sent back to the client device. The server generates a new learning program based on the collected and analyzed data and sends it to the client device. This allows the user to receive a new learning plan in real time and progress with their studies efficiently.

[0258] Step 7:

[0259] Visually displays learning progress data and test results. The client device visually displays new programs and progress data received from the server in graphs and charts. This allows parents and children to intuitively understand learning results and progress. For example, results such as "Math: 88 points," "English: 80 points," and "History: 92 points" are displayed visually.

[0260] Step 8:

[0261] The user adjusts the next lesson plan based on visual feedback. Parents and children consider the next lesson plan based on visually displayed progress data and test results. They provide feedback as needed to improve learning. This feedback is also sent to the server and used to optimize the next lesson plan using a generative AI model.

[0262] 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.

[0263] The present invention is an educational support system that combines generative AI and an emotion engine, and aims to optimize learning programs using emotion data from users (parents and children) and provide individually tailored learning support. Specific embodiments of this system are described below.

[0264] Server Operation

[0265] The server receives basic information about the child entered by the user (age, current grades, target university, etc.) and uses this information to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores. Furthermore, it is equipped with an emotion engine that acquires and analyzes the user's emotional data to adjust the learning program more effectively.

[0266] The generated educational program is distributed to the client terminal. As users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal. Emotional data is also collected at the same time, and the learning program is adjusted based on this. The readjusted learning program is then distributed again to the client terminal.

[0267] Client terminal operation

[0268] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's learning progress data and test results, and is equipped with a function for collecting emotional data. For example, it can use facial recognition and voice analysis technology to recognize a child's emotions while they are studying in real time.

[0269] The device also has the function of sending collected learning progress data and emotional data to a server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to grasp their child's learning status comprehensively.

[0270] User Actions

[0271] Parents and children use the system through client terminals. Parents enter basic information about their children during the initial setup and review the generated educational program. The children then follow the program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[0272] Parents can also check their child's learning progress data and emotional state through the client device, allowing them to provide support to maintain a better learning environment.

[0273] Specific examples

[0274] For example, when a child aiming for University B is entered as a target, the server uses generative AI to generate an appropriate early education program. At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the learning program.

[0275] When a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While the child is studying, the device recognizes the child's facial expressions and, if it detects stress or fatigue, sends that data to a server. The server analyzes the emotional data and adjusts the study program to make it more relaxing.

[0276] In the evening, parents can check their child's learning progress and emotional state on the device, and visual information such as "English Vocabulary: 90 points," "Math Problems: Completed," and "Emotional State: Relaxed" will be displayed. Based on this, parents can provide the child with the next learning plan and feedback to support their child's learning.

[0277] In this way, by combining generative AI and an emotion engine, the present invention provides an effective educational system that maximizes children's learning efficiency and reduces stress.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] The user (parent) uses a dedicated application to input basic information about their child (age, current grades, target university, etc.). The server receives this information and stores it in a database.

[0281] Step 2:

[0282] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[0283] Step 3:

[0284] The server uses an emotion engine to reflect the user's emotional data in the initial education program, which utilizes information on the user's stress level and concentration.

[0285] Step 4:

[0286] The generated educational program is distributed to the client terminal, which receives the program and displays it to the user (child) as a daily learning plan.

[0287] Step 5:

[0288] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal records the learning progress and collects emotional data as appropriate.

[0289] Step 6:

[0290] The client device captures emotional data using facial expression recognition and voice analysis while the child is learning, and this data is sent to the server in real time.

[0291] Step 7:

[0292] The server analyzes the received learning result data and emotional data to evaluate the child's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[0293] Step 8:

[0294] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[0295] Step 9:

[0296] Users (parents) can visually check their child's learning progress data and emotional state in graphs and charts on a client device, allowing them to get a comprehensive understanding of their child's learning situation.

[0297] Step 10:

[0298] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[0299] Step 11:

[0300] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[0301] Example 2

[0302] 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."

[0303] With conventional educational support systems, it was difficult to optimize learning programs that took into account each child's emotional state, resulting in problems such as reduced learning efficiency and excessive stress.In addition, the lack of visual feedback on learning progress and emotional state made it difficult for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[0304] 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.

[0305] In this invention, the server includes means for receiving basic information about a child based on user input, means for generating an initial educational program based on past successful candidate data and standardization information using a generation AI, means for distributing the generated educational program to a client terminal, means for acquiring and analyzing the user's emotional data, means for adjusting the educational program based on the emotional data and learning progress data, and means for analyzing the collected data and distributing the adjusted educational program back to the client terminal. This enables the creation and adjustment of educational programs that take into account each child's individual emotional state, thereby improving learning efficiency and reducing stress. Furthermore, visual feedback of learning progress data, test results, and emotional state makes it easier for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[0306] "User" refers to a parent or teacher who uses the system to assist in the education of their child.

[0307] "Child's basic information" refers to information necessary to generate and optimize a learning program, such as the child's age, current grades, and desired educational facilities.

[0308] "Generative AI" refers to artificial intelligence technology that generates educational programs based on past successful applicant data and standardization information.

[0309] "Educational program" refers to the learning plan and content constructed in line with a child's learning goals.

[0310] "Client Terminal" refers to the device used by users and children to receive and engage with educational programs.

[0311] "Emotional data" refers to data collected to understand a child's emotional state while learning, and refers to information obtained using facial recognition technology, voice analysis technology, etc.

[0312] "Learning Progress Data" means data that shows a child's learning progress and achievements, such as progress in a learning program and test results.

[0313] "Standardized information" refers to standard information necessary to optimize learning programs, such as educational evaluation criteria and entrance examination information for target educational facilities.

[0314] "Emotion engine" refers to an engine that analyzes the emotional data of users and children and adjusts learning programs more effectively.

[0315] "Visual feedback" refers to a function that visually displays learning progress data and emotional state in graphs and charts, providing it in a way that is easy for users to understand.

[0316] "Adjustment" refers to changing the content or difficulty of a learning program based on collected data.

[0317] "Delivery" refers to sending the generated educational program or the re-adjusted learning program to the client terminal.

[0318] MODE FOR CARRYING OUT THE INVENTION

[0319] The present invention is an educational support system that combines a generative AI model and an emotion engine, and aims to provide personalized learning support by optimizing learning programs using emotion data from users (parents and children). Specific embodiments of this system are described below.

[0320] Server Operation

[0321] The server receives basic information about the child (age, grades, target educational institution, etc.) entered by the user. Based on this information, the server creates an initial educational program using a generative AI model. An example of a generative AI model is an AI algorithm used to generate existing study plans. A prompt such as "Please create a study plan for target university B" is used.

[0322] The educational program is optimized by referencing data on past successful candidates and standardization information. Furthermore, the server is equipped with an emotion engine that acquires and analyzes the emotional data of users and children. This emotional data includes real-time emotion recognition results using facial recognition and voice analysis technology. This allows for more effective adjustment of the learning program.

[0323] The generated educational program is distributed to the client terminal. As the users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal and readjusts the program based on this data. This readjusted learning program is also distributed again to the client terminal.

[0324] Client terminal operation

[0325] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's study progress data and test results. Furthermore, the terminal has the function of recognizing the child's emotions in real time while studying using facial recognition and voice analysis technology and collecting emotional data.

[0326] For example, if a child feels stressed or tired while studying, the device will detect this and send it as emotional data to the server. The device also has the function of sending collected learning progress data and emotional data to the server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to have a comprehensive understanding of their child's learning situation.

[0327] User Actions

[0328] Users (parents and children) use the system through a client terminal. Parents enter basic information about their children during the initial setup and confirm the generated educational program. The children then follow this program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[0329] For example, the target university is set to University B, and the generative AI model generates an initial program using a prompt such as "Please create a study plan for University B." At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the study program.

[0330] Specifically, when a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While studying, the device recognizes the child's facial expressions, and if it detects stress or fatigue, it sends the data to a server. The server analyzes the emotional data and adjusts the study program to be more relaxing. In the evening, when a parent checks their child's study progress and emotional state on the device, information such as "English vocabulary: 90 points," "Math workbooks: completed," and "Emotional state: relaxed" is visually displayed. Based on this, parents can provide the child with the next study plan and feedback to support their child's learning.

[0331] In this way, the present invention provides an effective educational support system that maximizes children's learning efficiency and reduces stress by combining a generative AI model and an emotion engine.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] The server receives basic information about the child (age, grades, target educational institution, etc.) input by the user.

[0335] Input: Basic information about the child entered by the user

[0336] Output: A dataset of basic information about children

[0337] Specific operation: Parents operate the client terminal to enter their child's information, which is then sent to the server and stored in the database.

[0338] Step 2:

[0339] The server generates an initial educational program using a generative AI model based on the received basic information.

[0340] Input: Dataset of basic information about children

[0341] Output: Early Education Program

[0342] Specific operation: The server inputs a prompt such as "Please create a study plan for the target university, University B" into the generative AI model, receives the study plan output by the AI, and constructs it as an initial education program.

[0343] Step 3:

[0344] The server distributes the generated educational program to the client terminal.

[0345] Enter: Early Education Programs

[0346] Output: The training program sent to the client terminal

[0347] Specific operation: The server packages the educational program and transmits it to the client terminal via the Internet.

[0348] Step 4:

[0349] The server acquires and analyzes the user's emotional data.

[0350] Input: User emotion data

[0351] Output: Analyzed emotion data and adjustment instructions

[0352] How it works: Using facial recognition and voice analysis technology, the server collects and analyzes the user's emotional state in real time, and based on the results, determines whether the learning program needs to be adjusted.

[0353] Step 5:

[0354] The terminal provides the user with a daily study plan via the client terminal, and allows the user to start studying.

[0355] Input: Delivered educational program

[0356] Output: Learning progress data

[0357] Specific operation: The educational program is displayed on the device, and the child follows it to progress through the learning process. The child's learning progress is entered into the device.

[0358] Step 6:

[0359] The device collects learning progress data and emotional data.

[0360] Input: Learning progress data, user emotion data

[0361] Output: Collected dataset

[0362] Specific operation: Sensors and cameras built into the device collect learning progress and user emotions in real time and store them as data.

[0363] Step 7:

[0364] The terminal transmits the collected data to the server.

[0365] Input: Collected dataset

[0366] Output: Data sent to the server

[0367] Specific operation: The device sends the collected learning progress data and emotion data to the server via the Internet.

[0368] Step 8:

[0369] The server readjusts the learning program based on the collected data.

[0370] Input: Collected data (learning progress data and emotion data)

[0371] Output: Retuned learning program

[0372] How it works: The server uses an analytical engine to analyze the data and inputs prompts such as "Please provide a learning plan that will help my child relax" into the generative AI model. Based on the results output by the AI, the learning program is adjusted.

[0373] Step 9:

[0374] The server distributes the re-adjusted educational program to the client terminal.

[0375] Input: Recalibrated learning program

[0376] Output: The reworked program sent to the client terminal

[0377] Specific operation: The server packages the retuned learning program and transmits it to the client terminal via the Internet.

[0378] Step 10:

[0379] Users can check their learning progress data and emotional state, and provide their next learning plan and feedback.

[0380] Input: Visually displayed data

[0381] Output: Next lesson plan and feedback

[0382] Specific operation: Parents check learning progress data and emotional state through the device and provide feedback to their children based on information such as "English vocabulary: 90 points," "Math workbook: completed," and "Emotional state: relaxed."

[0383] The above is the specific processing flow of this system.

[0384] (Application example 2)

[0385] 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."

[0386] Conventional work planning systems do not take into account the emotional data of workers, which can lead to problems such as worker stress and reduced efficiency. In particular, when workers perform monotonous work over long periods of time, they do not take appropriate breaks or review their tasks, resulting in a decline in work efficiency.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving basic information about workers based on input from a user, means for generating an initial work plan based on past production data and efficiency information using a generation AI, means for distributing the generated work plan to a client terminal, means for collecting work results and progress data, means for acquiring and analyzing emotional data about workers using facial recognition technology, means for analyzing the collected data and adjusting the work program, and means for distributing the adjusted work program again to the client terminal. This makes it possible to optimize the work program and maintain efficiency while monitoring the emotional state of workers in real time.

[0388] "User" means a person who uses the system to input basic information to manage and monitor work plans.

[0389] A "worker" is a person who actually performs work according to the work plan generated by the system.

[0390] "Generative AI" is an artificial intelligence algorithm that automatically generates initial work plans using various data.

[0391] "Past production data" is a data set that includes information on past work performance and production.

[0392] "Efficiency information" is information that includes metrics and indicators for evaluating the efficiency of work.

[0393] A "client terminal" is a device through which a worker receives a work plan and inputs work results.

[0394] A "work plan" is a program generated by generative AI that contains specific instructions and goals for workers to carry out their daily work.

[0395] "Work results" is data related to the results and progress of work actually performed by a worker.

[0396] "Progress data" is information that indicates how well work is progressing according to plan.

[0397] "Facial recognition technology" is a technology that uses a camera to recognize a worker's face and analyze specific emotions.

[0398] "Emotion data" is data that indicates the emotional state of a worker, obtained using face recognition technology.

[0399] "Analysis" refers to the process by which a system analyzes information based on collected data and derives useful results.

[0400] An "adjusted work program" is a work plan that has been optimized based on collected data and analysis results from the initial work plan.

[0401] "Distributing" refers to the act of the server sending the generated or adjusted work program to the client terminal.

[0402] This invention is a work support system that combines generative AI and facial recognition technology, and uses the worker's emotional data to optimize the work program and provide individualized work support. Specific embodiments of this system are described below.

[0403] Server Operation

[0404] The server first receives basic information about the workers (roles, target production volume, etc.) entered by the user. Next, it uses generative AI to create an initial work plan based on this information. The work plan is optimized by referencing past production data and efficiency information.

[0405] The generated work plan is distributed to the client terminal. As the worker goes about their daily work, the server collects work progress data and work results from the client terminal. It also uses facial recognition technology to obtain worker emotion data and adjusts the work program based on this. The adjusted work program is then distributed again to the client terminal.

[0406] Client terminal operation

[0407] The client terminal receives the work plan distributed from the server and provides it to the worker as a daily work plan. It also provides an interface for inputting the worker's work results and progress data, and is equipped with facial recognition technology to collect emotion data.

[0408] The device also has the function of transmitting collected work progress data and emotion data to a server and visually displaying the work results. Progress and emotion changes are displayed in graph and chart format, allowing users to grasp the overall status of the worker.

[0409] User Actions

[0410] Users use the system through a client terminal. The user enters basic information about the worker during the initial setup and confirms the generated work plan. The worker then carries out their daily work according to the work plan. Work results and emotional data are entered into the terminal and sent to the server, which optimizes the work program.

[0411] In addition, users can check work progress data and the emotional state of workers through the client terminal, which can provide support to maintain a better work environment.

[0412] Specific examples

[0413] For example, when basic information about a welding worker in a manufacturing plant is entered, the server uses generative AI to generate an appropriate initial work plan. At the same time, facial recognition technology evaluates the worker's emotional state and reflects it in the program accordingly. If the worker is feeling stressed, the system will detect this and the server will readjust the work program.

[0414] On Monday morning, when a worker opens their device, the day's work plan (e.g., 3 hours of welding, 2 hours of parts assembly) is displayed. While working, the device recognizes the worker's facial expressions, and if it detects stress or fatigue, it sends that data to the server. The server analyzes the emotional data and adjusts the work program accordingly.

[0415] Examples of prompts based on this include:

[0416] Due to the worker's high stress level, the next task was changed to lighter work and a break was provided.

[0417] In this way, by combining generative AI and facial recognition technology, the present invention provides an effective work support system that maximizes workers' work efficiency and reduces stress.

[0418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] The user uses a terminal to input basic information about the worker (role, target production volume, etc.). The input data is sent to the server.

[0421] Input: Worker name, role, target production volume, etc.

[0422] Output: Basic information sent to the server

[0423] Step 2:

[0424] The server generates an initial work plan using a generative AI model based on the received basic information, and the generative AI model optimizes the work plan by referencing past production data and efficiency information.

[0425] Input: Basic information of workers, past production data, efficiency information

[0426] Output: Generated initial work plan

[0427] Step 3:

[0428] The server distributes the generated work plan to the terminal, which then displays the received work plan to the worker.

[0429] Input: Generated initial work plan

[0430] Output: Work plan delivered to the device

[0431] Step 4:

[0432] The worker starts working, and the terminal collects the work results and progress data, while simultaneously collecting the worker's emotional data using facial recognition technology.

[0433] Input: Work results, progress data, emotion data

[0434] Output: Collected work results, progress data, and emotion data

[0435] Step 5:

[0436] The terminal transmits the collected work results, progress data, and emotion data to the server.

[0437] Input: Collected work results, progress data, emotion data

[0438] Output: Work results, progress data, and emotion data sent to the server

[0439] Step 6:

[0440] The server analyzes the received data and readjusts the work program based on the emotion data and work progress. The generative AI model optimizes the work plan based on the new data.

[0441] Input: Work results, progress data, emotion data

[0442] Output: Reworked work program

[0443] Step 7:

[0444] The server again distributes the readjusted work program to the terminal, and the terminal displays the received readjusted work program to the worker.

[0445] Input: Reworked work program

[0446] Output: The re-adjusted working program delivered to the terminal

[0447] Step 8:

[0448] The user can check the work progress data and the worker's emotional state through the client terminal and provide support as needed.

[0449] Input: Work progress data, emotional state

[0450] Output: Feedback provided to the user based on their work progress and emotional state

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0466] 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."

[0467] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments of the invention are described below.

[0468] Server Operation

[0469] The server receives basic information about the child (age, current grades, target university, etc.) entered by the user, and uses this information to create an initial educational program using generative AI. This program is individually optimized by referencing past successful applicant data and deviation score information.

[0470] The generated educational program is distributed to the client device. As the child's learning progresses, the server collects daily learning progress data and test results from the client device. This data is analyzed and the learning program is automatically adjusted. The adjusted program is then distributed again to the client device, allowing the child to continue learning.

[0471] Client terminal operation

[0472] The client device receives the educational program distributed from the server and provides it to the child. Specifically, daily study plans and assignments are displayed on the device screen. As the child progresses with their studies and takes tests, the results are sent to the server via the device.

[0473] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Visual feedback such as graphs and charts is provided, allowing parents to intuitively understand the effectiveness of their children's learning.

[0474] User Actions

[0475] Parents and children use the system through client terminals. Parents first enter basic information about their children and then check the generated educational program. The children then study according to the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[0476] Specific examples

[0477] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[0478] On Monday morning, when a child opens their client device (iPad), the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. These results are sent to the server, which analyzes the data and adjusts the study program accordingly.

[0479] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[0480] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[0481] The processing flow will be explained below.

[0482] Step 1:

[0483] The user uses a dedicated application to input basic information about their child (age, current grades, target university, etc.), and the server receives this information and stores it in a database.

[0484] Step 2:

[0485] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[0486] Step 3:

[0487] The server distributes the generated initial education program to the client terminal, which receives the program and displays it as a daily learning plan.

[0488] Step 4:

[0489] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal has the function of recording the learning progress and sending the results to the server.

[0490] Step 5:

[0491] The client terminal provides an interface for inputting the child's learning results (e.g., test scores), which are then sent to the server.

[0492] Step 6:

[0493] The server analyzes the received learning result data and evaluates the student's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[0494] Step 7:

[0495] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[0496] Step 8:

[0497] Users (parents) can visually check their child's learning progress data and test results through a client device. The data is displayed in graphs and charts, allowing them to intuitively understand their child's learning situation.

[0498] Step 9:

[0499] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[0500] Step 10:

[0501] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[0502] Example 1

[0503] 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."

[0504] In today's educational environment, it is difficult for parents to accurately track their children's learning progress and create optimal learning plans. It is also difficult to monitor children's learning habits and achievements in real time and provide timely feedback. This creates a problem of insufficient efficient learning support aimed at passing specific target universities.

[0505] 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.

[0506] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for using a generation AI to generate an initial educational program based on past successful applicant data and deviation score information, means for distributing the generated educational program to a client terminal, means for the terminal to visually display the educational program, means for the child to progress in their studies, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, and means for distributing the adjusted learning program again to the client terminal. This makes it possible to accurately grasp a child's learning progress and provide an optimal learning plan.

[0507] "User" refers to the parent or guardian who enters and manages their child's learning program.

[0508] "Child" refers to the student for whom the study program is being carried out.

[0509] "Basic information" refers to data necessary to generate a learning program, such as the child's age, current grades, and target university.

[0510] "Generative AI" refers to a system that uses artificial intelligence technology to create a learning program based on past successful applicant data and deviation score information.

[0511] "Successful applicant data" refers to information regarding the grades and learning history of students who have been accepted into specific target universities in the past.

[0512] "Standard deviation information" refers to statistical data that serves as a standard for evaluating learning content and test results.

[0513] "Initial educational program" refers to a learning plan created by the generative AI based on input data from the user.

[0514] "Client terminal" refers to a device used by a user or child to operate the system, and specifically includes a smartphone or tablet.

[0515] "Means for visually displaying educational programs" refers to the function of displaying learning plans and assignments on the device screen in an intuitive manner.

[0516] "Learning Progress Data" refers to information about a child's progress and achievement as they progress through their education.

[0517] "Test results" refers to the test scores and evaluation results a child has taken.

[0518] "Means for analyzing data" refers to computational processes for optimizing learning programs based on collected learning progress data and test results.

[0519] "Means of adjusting learning programs" refers to the process of making changes or additions to existing learning programs based on data analysis.

[0520] "Generative AI model" refers to the algorithms and data models used by generative AI.

[0521] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments are described below.

[0522] Overall system configuration

[0523] This educational support system consists of three components: a server, a client terminal, and a user. The server generates learning programs using a generative AI model and operates based on user input data. The client terminal provides learning programs to users and transmits progress data and test results to the server. Users utilize the system through system operations and learning activities.

[0524] Server Operation

[0525] The server performs the following series of processes.

[0526] 1. Receiving basic information:

[0527] The server receives basic information about the child (age, current grades, target university) entered by the user. For example, a parent might enter data such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points."

[0528] 2. Creation of an initial education program:

[0529] Based on the received basic information, the server uses a generative AI model to generate an initial educational program. The generative AI model references past successful applicant data and deviation score information to create an individually optimized study plan. Specifically, it generates a plan such as "Mathematics: 1 hour, English: 30 minutes, History: 45 minutes."

[0530] 3. Program Delivery:

[0531] The generated educational program is distributed from the server to the client terminal using cloud infrastructure (e.g., AWS or GCP).

[0532] 4. Progress data collection and analysis:

[0533] When a child's learning progress data and test results are sent from the device, the server collects and analyzes the data, and automatically adjusts the content of the learning program based on the analysis results.

[0534] 5. Adjusted Program Re-Delivery:

[0535] The new, adjusted learning program is then sent back to the client device, allowing the child to continue learning optimally.

[0536] Client terminal operation

[0537] The client terminal receives the educational program distributed from the server and operates as follows.

[0538] 1. Display of educational programs:

[0539] The device visually displays the delivered educational program to the child. The daily learning plan and assignments are displayed on the screen. For example, when a child opens a tablet (such as an iPad), the learning plan for that day (e.g., "30 minutes of English vocabulary, 60 minutes of math workbooks, 30 minutes of history") is displayed.

[0540] 2. Learning Progression:

[0541] Children learn by following a program displayed on the device, for example, memorizing a list of English words or solving math problems.

[0542] 3. Enter and submit test results:

[0543] Once the child has finished studying, they take a test and enter the results into the device. The entered test results are sent to the server via the device. For example, data such as "English vocabulary test: 90 points" is sent.

[0544] 4. View progress data:

[0545] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Graphs and charts can be used to intuitively understand the effectiveness of learning.

[0546] User Actions

[0547] 1. Enter basic information:

[0548] The user (parent) first enters basic information about their child into the client terminal, following prompts such as, "How old is your child?", "Please enter their current grades.", and "Please tell us your target university."

[0549] 2. Implementation of the learning program:

[0550] Children follow the program displayed on the device to study, and when they finish, they enter and submit their test results.

[0551] 3. Check your progress:

[0552] Parents can check their child's learning progress through the device and provide feedback as needed. For example, in the evening, a parent can check their child's learning progress on the device and see details such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding."

[0553] As described above, the present invention can reduce the burden on users and provide optimized learning support, thereby enabling efficient and effective learning support aimed at helping users pass the entrance exams to specific target universities.

[0554] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0555] Step 1:

[0556] The user enters basic information about the child.

[0557] (Specific action)

[0558] The user (parent) enters basic information about their child (age, grades, target university, etc.) into the client terminal. For example, information such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points" is entered into the input form.

[0559] (input)

[0560] Age, grades, target university

[0561] (output)

[0562] Basic information is sent to the server

[0563] Step 2:

[0564] The server receives the basic information.

[0565] (Specific action)

[0566] The server receives basic information sent from the client terminal, which is then stored in a database.

[0567] (input)

[0568] Basic information sent from the client terminal

[0569] (output)

[0570] Basic information is stored on the server

[0571] Step 3:

[0572] The server generates the initial training program.

[0573] (Specific action)

[0574] The server uses a generative AI model to generate an initial educational program based on basic information. The generative AI model references past successful applicant data and deviation score information. For example, a plan such as "Math: 1 hour, English: 30 minutes, History: 45 minutes" may be generated.

[0575] (input)

[0576] Basic information, successful applicant data, deviation score information

[0577] (output)

[0578] Early Education Program

[0579] Step 4:

[0580] The server distributes the educational program to the terminal.

[0581] (Specific action)

[0582] The server distributes the generated educational programs to client devices via HTTPS protocol and AWS / GCP cloud infrastructure.

[0583] (input)

[0584] Early Education Program

[0585] (output)

[0586] The educational program is sent to the device.

[0587] Step 5:

[0588] The terminal displays the educational program.

[0589] (Specific action)

[0590] The client device displays the received educational program on its user interface. When a child opens the device, a learning plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history" appears on the screen.

[0591] (input)

[0592] Educational programs sent from the server

[0593] (output)

[0594] The educational program will be displayed on the device.

[0595] Step 6:

[0596] Children progress in their learning.

[0597] (Specific action)

[0598] Children learn by following educational programs displayed on the device, for example, memorizing a list of English words and solving math problems with a digital pen.

[0599] (input)

[0600] Educational Program

[0601] (output)

[0602] Learning progress data

[0603] Step 7:

[0604] The device sends the test results to the server.

[0605] (Specific action)

[0606] After the child has finished studying, they enter their test results into the device and send them to the server. For example, they might enter "90 points" as the test result for English vocabulary.

[0607] (input)

[0608] Test Results

[0609] (output)

[0610] Test results are sent to the server

[0611] Step 8:

[0612] The server analyzes the data and adjusts the learning program.

[0613] (Specific action)

[0614] The server analyzes the collected test results and learning progress data and uses a generative AI model to adjust the learning program, for example, increasing the difficulty level if the student performs well in English and adding review if the student performs poorly.

[0615] (input)

[0616] Test results, learning progress data

[0617] (output)

[0618] Tailored Learning Programs

[0619] Step 9:

[0620] The server distributes the updated educational program to the terminal.

[0621] (Specific action)

[0622] The adjusted new learning program is again distributed from the server to the client terminal.

[0623] (input)

[0624] Tailored Learning Programs

[0625] (output)

[0626] Updated educational programs are sent to the device.

[0627] Step 10:

[0628] The terminal displays the updated educational program.

[0629] (Specific action)

[0630] The client device will then display the redistributed educational program again. When the child opens the device, they will see a lesson plan for the next day, such as "40 minutes of English vocabulary, 70 minutes of math problems, and 30 minutes of reviewing new history."

[0631] (input)

[0632] Updated Educational Programs

[0633] (output)

[0634] The updated educational program will be displayed on the device.

[0635] These are the specific processing steps of this system, which makes it possible to properly manage a child's learning progress and provide an optimal learning plan.

[0636] (Application example 1)

[0637] 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."

[0638] Conventional educational support systems have struggled to efficiently track daily learning progress and provide real-time feedback. Furthermore, the lack of an optimal system for utilizing client devices such as smartphones and head-mounted displays made it difficult for students to follow an optimal learning plan. This left students with an unclear path to success at their desired educational institution.

[0639] 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.

[0640] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI, means for distributing the generated educational program to a client terminal, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, means for distributing the adjusted learning program again to the client terminal, means for visually displaying the learning progress data and test results, means for providing real-time visual feedback, and means for using a client terminal installed on a smartphone or head-mounted display. This allows users to efficiently track their learning progress and receive real-time feedback, enabling them to study effectively to pass their target educational institution according to an optimal learning plan.

[0641] "Users" refers to parents and children who use the system.

[0642] "Basic information" refers to information such as the child's age, current grades, and target university.

[0643] "Generative AI" refers to artificial intelligence that generates optimal educational programs based on user data, past successful applicant data, and deviation score information.

[0644] An "educational program" refers to a plan that outlines the learning plans and assignments a child needs to pass in order to be accepted into their desired educational institution.

[0645] "Client terminal" refers to a device that is directly operated by a user, such as a smartphone, tablet, or head-mounted display.

[0646] "Test results" refers to test scores and evaluation information taken by a child.

[0647] "Learning progress data" refers to a child's progress and achievements as they progress through their daily studies.

[0648] "Analysis" refers to the process of analyzing collected test results and learning progress data using machine learning and statistical methods.

[0649] "Feedback" refers to the improvements and advice that the AI ​​provides to children based on the analysis results.

[0650] "Visual feedback" refers to providing users with an intuitive understanding of their learning progress and results using graphs, charts, etc.

[0651] "Real-time" refers to data being processed almost instantly, with results and feedback provided without delay.

[0652] "Target university" refers to the specific educational institution that a child aspires to attend.

[0653] A "head-mounted display" refers to a display device that is worn on the user's head.

[0654] This invention is an educational support system that uses generative AI, and aims to provide an optimal learning plan based on input from users (parents and children) and to progress learning according to that plan. Specific embodiments of the invention are described below.

[0655] Server Operation

[0656] The server first receives basic information about the child (age, current grades, target university, etc.) entered by the user. Based on this information, the server uses a generation AI to reference data on past successful applicants and deviation scores to generate an initial educational program. The generated program is then distributed to the client device.

[0657] As the learning progresses, the server collects daily learning progress data and test results from the client device. This collected data is analyzed and the learning program is automatically adjusted. The adjusted program is then sent back to the client device, allowing the learning to continue.

[0658] The main software used is a generative AI model (e.g., OpenAI's API), which analyzes data and generates programs.

[0659] Client terminal operation

[0660] The client device (smartphone or head-mounted display) receives the educational program distributed from the server and provides it to the child. Specifically, the program displays daily learning plans and assignments on the device screen.

[0661] As children study and take tests, the results are sent to the server via the client device. The device also has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning status in real time. Visual feedback allows parents to intuitively understand the effectiveness of their children's learning.

[0662] User Actions

[0663] Parents and children use this system through client terminals. Parents first enter basic information about their children and then check the generated educational program. Children follow the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[0664] Specific examples

[0665] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[0666] On Monday morning, when a child opens their smartphone, the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. The results are sent to a server, which analyzes the data and adjusts the study program accordingly.

[0667] Specific examples of prompts include:

[0668] User information: Age 16, current grades: 85 in math, 78 in English, 90 in history, target university: University A

[0669] Generate the best study plan to get into your target university.

[0670] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[0671] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[0672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0673] Step 1:

[0674] The user uses a client device to input basic information about their child. The user enters information such as age, current grades, and target university into the device's input screen, and the information is sent to the server. The input information is used as basic data for the generative AI model to generate an educational program. Examples of input data include "age: 16 years old," "math: 85 points," "English: 78 points," "history: 90 points," and "target university: University A."

[0675] Step 2:

[0676] The server generates an educational program using a generative AI model. Based on the basic information received from the user, the server references past successful applicant data and deviation score information to generate a prompt. For example, a prompt such as "User information: age 16, current grades 85 points in math, 78 points in English, 90 points in history, target university A. Please generate the optimal study plan to pass the target university entrance exam." is generated. This prompt is input into the generative AI model, which outputs an optimized educational program.

[0677] Step 3:

[0678] The generated educational program is delivered to the client terminal. The server sends the plan obtained from the generative AI model to the client terminal. For example, the generated program is a specific study plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history." This program is displayed on the terminal screen, and the user can use it to proceed with their studies.

[0679] Step 4:

[0680] The user studies and inputs test results and learning progress data. The child progresses with their daily studies and takes tests, which are then entered into the client terminal. For example, data such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding" are entered. This input data is sent to the server.

[0681] Step 5:

[0682] The server collects and analyzes test results and learning progress data. The server collects and analyzes data sent from client devices. This analysis uses machine learning algorithms and statistical methods. For example, if a student's understanding of a particular subject is insufficient, the server adjusts the learning plan to strengthen the content of that subject. The analysis results become the basis for a new learning program.

[0683] Step 6:

[0684] The adjusted learning program is then sent back to the client device. The server generates a new learning program based on the collected and analyzed data and sends it to the client device. This allows the user to receive a new learning plan in real time and progress with their studies efficiently.

[0685] Step 7:

[0686] Visually displays learning progress data and test results. The client device visually displays new programs and progress data received from the server in graphs and charts. This allows parents and children to intuitively understand learning results and progress. For example, results such as "Math: 88 points," "English: 80 points," and "History: 92 points" are displayed visually.

[0687] Step 8:

[0688] The user adjusts the next lesson plan based on visual feedback. Parents and children consider the next lesson plan based on visually displayed progress data and test results. They provide feedback as needed to improve learning. This feedback is also sent to the server and used to optimize the next lesson plan using a generative AI model.

[0689] 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.

[0690] The present invention is an educational support system that combines generative AI and an emotion engine, and aims to optimize learning programs using emotion data from users (parents and children) and provide individually tailored learning support. Specific embodiments of this system are described below.

[0691] Server Operation

[0692] The server receives basic information about the child entered by the user (age, current grades, target university, etc.) and uses this information to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores. Furthermore, it is equipped with an emotion engine that acquires and analyzes the user's emotional data to adjust the learning program more effectively.

[0693] The generated educational program is distributed to the client terminal. As users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal. Emotional data is also collected at the same time, and the learning program is adjusted based on this. The readjusted learning program is then distributed again to the client terminal.

[0694] Client terminal operation

[0695] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's learning progress data and test results, and is equipped with a function for collecting emotional data. For example, it can use facial recognition and voice analysis technology to recognize a child's emotions while they are studying in real time.

[0696] The device also has the function of sending collected learning progress data and emotional data to a server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to grasp their child's learning status comprehensively.

[0697] User Actions

[0698] Parents and children use the system through client terminals. Parents enter basic information about their children during the initial setup and review the generated educational program. The children then follow the program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[0699] Parents can also check their child's learning progress data and emotional state through the client device, allowing them to provide support to maintain a better learning environment.

[0700] Specific examples

[0701] For example, when a child aiming for University B is entered as a target, the server uses generative AI to generate an appropriate early education program. At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the learning program.

[0702] When a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While the child is studying, the device recognizes the child's facial expressions and, if it detects stress or fatigue, sends that data to a server. The server analyzes the emotional data and adjusts the study program to make it more relaxing.

[0703] In the evening, parents can check their child's learning progress and emotional state on the device, and visual information such as "English Vocabulary: 90 points," "Math Problems: Completed," and "Emotional State: Relaxed" will be displayed. Based on this, parents can provide the child with the next learning plan and feedback to support their child's learning.

[0704] In this way, by combining generative AI and an emotion engine, the present invention provides an effective educational system that maximizes children's learning efficiency and reduces stress.

[0705] The processing flow will be explained below.

[0706] Step 1:

[0707] The user (parent) uses a dedicated application to input basic information about their child (age, current grades, target university, etc.). The server receives this information and stores it in a database.

[0708] Step 2:

[0709] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[0710] Step 3:

[0711] The server uses an emotion engine to reflect the user's emotional data in the initial education program, which utilizes information on the user's stress level and concentration.

[0712] Step 4:

[0713] The generated educational program is distributed to the client terminal, which receives the program and displays it to the user (child) as a daily learning plan.

[0714] Step 5:

[0715] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal records the learning progress and collects emotional data as appropriate.

[0716] Step 6:

[0717] The client device captures emotional data using facial expression recognition and voice analysis while the child is learning, and this data is sent to the server in real time.

[0718] Step 7:

[0719] The server analyzes the received learning result data and emotional data to evaluate the child's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[0720] Step 8:

[0721] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[0722] Step 9:

[0723] Users (parents) can visually check their child's learning progress data and emotional state in graphs and charts on a client device, allowing them to get a comprehensive understanding of their child's learning situation.

[0724] Step 10:

[0725] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[0726] Step 11:

[0727] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[0728] Example 2

[0729] 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."

[0730] With conventional educational support systems, it was difficult to optimize learning programs that took into account each child's emotional state, resulting in problems such as reduced learning efficiency and excessive stress.In addition, the lack of visual feedback on learning progress and emotional state made it difficult for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[0731] 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.

[0732] In this invention, the server includes means for receiving basic information about a child based on user input, means for generating an initial educational program based on past successful candidate data and standardization information using a generation AI, means for distributing the generated educational program to a client terminal, means for acquiring and analyzing the user's emotional data, means for adjusting the educational program based on the emotional data and learning progress data, and means for analyzing the collected data and distributing the adjusted educational program back to the client terminal. This enables the creation and adjustment of educational programs that take into account each child's individual emotional state, thereby improving learning efficiency and reducing stress. Furthermore, visual feedback of learning progress data, test results, and emotional state makes it easier for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[0733] "User" refers to a parent or teacher who uses the system to assist in the education of their child.

[0734] "Child's basic information" refers to information necessary to generate and optimize a learning program, such as the child's age, current grades, and desired educational facilities.

[0735] "Generative AI" refers to artificial intelligence technology that generates educational programs based on past successful applicant data and standardization information.

[0736] "Educational program" refers to the learning plan and content constructed in line with a child's learning goals.

[0737] "Client Terminal" refers to the device used by users and children to receive and engage with educational programs.

[0738] "Emotional data" refers to data collected to understand a child's emotional state while learning, and refers to information obtained using facial recognition technology, voice analysis technology, etc.

[0739] "Learning Progress Data" means data that shows a child's learning progress and achievements, such as progress in a learning program and test results.

[0740] "Standardized information" refers to standard information necessary to optimize learning programs, such as educational evaluation criteria and entrance examination information for target educational facilities.

[0741] "Emotion engine" refers to an engine that analyzes the emotional data of users and children and adjusts learning programs more effectively.

[0742] "Visual feedback" refers to a function that visually displays learning progress data and emotional state in graphs and charts, providing it in a way that is easy for users to understand.

[0743] "Adjustment" refers to changing the content or difficulty of a learning program based on collected data.

[0744] "Delivery" refers to sending the generated educational program or the re-adjusted learning program to the client terminal.

[0745] MODE FOR CARRYING OUT THE INVENTION

[0746] The present invention is an educational support system that combines a generative AI model and an emotion engine, and aims to provide personalized learning support by optimizing learning programs using emotion data from users (parents and children). Specific embodiments of this system are described below.

[0747] Server Operation

[0748] The server receives basic information about the child (age, grades, target educational institution, etc.) entered by the user. Based on this information, the server creates an initial educational program using a generative AI model. An example of a generative AI model is an AI algorithm used to generate existing study plans. A prompt such as "Please create a study plan for target university B" is used.

[0749] The educational program is optimized by referencing data on past successful candidates and standardization information. Furthermore, the server is equipped with an emotion engine that acquires and analyzes the emotional data of users and children. This emotional data includes real-time emotion recognition results using facial recognition and voice analysis technology. This allows for more effective adjustment of the learning program.

[0750] The generated educational program is distributed to the client terminal. As the users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal and readjusts the program based on this data. This readjusted learning program is also distributed again to the client terminal.

[0751] Client terminal operation

[0752] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's study progress data and test results. Furthermore, the terminal has the function of recognizing the child's emotions in real time while studying using facial recognition and voice analysis technology and collecting emotional data.

[0753] For example, if a child feels stressed or tired while studying, the device will detect this and send it as emotional data to the server. The device also has the function of sending collected learning progress data and emotional data to the server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to have a comprehensive understanding of their child's learning situation.

[0754] User Actions

[0755] Users (parents and children) use the system through a client terminal. Parents enter basic information about their children during the initial setup and confirm the generated educational program. The children then follow this program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[0756] For example, the target university is set to University B, and the generative AI model generates an initial program using a prompt such as "Please create a study plan for University B." At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the study program.

[0757] Specifically, when a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While studying, the device recognizes the child's facial expressions, and if it detects stress or fatigue, it sends the data to a server. The server analyzes the emotional data and adjusts the study program to be more relaxing. In the evening, when a parent checks their child's study progress and emotional state on the device, information such as "English vocabulary: 90 points," "Math workbooks: completed," and "Emotional state: relaxed" is visually displayed. Based on this, parents can provide the child with the next study plan and feedback to support their child's learning.

[0758] In this way, the present invention provides an effective educational support system that maximizes children's learning efficiency and reduces stress by combining a generative AI model and an emotion engine.

[0759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0760] Step 1:

[0761] The server receives basic information about the child (age, grades, target educational institution, etc.) input by the user.

[0762] Input: Basic information about the child entered by the user

[0763] Output: A dataset of basic information about children

[0764] Specific operation: Parents operate the client terminal to enter their child's information, which is then sent to the server and stored in the database.

[0765] Step 2:

[0766] The server generates an initial educational program using a generative AI model based on the received basic information.

[0767] Input: Dataset of basic information about children

[0768] Output: Early Education Program

[0769] Specific operation: The server inputs a prompt such as "Please create a study plan for the target university, University B" into the generative AI model, receives the study plan output by the AI, and constructs it as an initial education program.

[0770] Step 3:

[0771] The server distributes the generated educational program to the client terminal.

[0772] Enter: Early Education Programs

[0773] Output: The training program sent to the client terminal

[0774] Specific operation: The server packages the educational program and transmits it to the client terminal via the Internet.

[0775] Step 4:

[0776] The server acquires and analyzes the user's emotional data.

[0777] Input: User emotion data

[0778] Output: Analyzed emotion data and adjustment instructions

[0779] How it works: Using facial recognition and voice analysis technology, the server collects and analyzes the user's emotional state in real time, and based on the results, determines whether the learning program needs to be adjusted.

[0780] Step 5:

[0781] The terminal provides the user with a daily study plan via the client terminal, and allows the user to start studying.

[0782] Input: Delivered educational program

[0783] Output: Learning progress data

[0784] Specific operation: The educational program is displayed on the device, and the child follows it to progress through the learning process. The child's learning progress is entered into the device.

[0785] Step 6:

[0786] The device collects learning progress data and emotional data.

[0787] Input: Learning progress data, user emotion data

[0788] Output: Collected dataset

[0789] Specific operation: Sensors and cameras built into the device collect learning progress and user emotions in real time and store them as data.

[0790] Step 7:

[0791] The terminal transmits the collected data to the server.

[0792] Input: Collected dataset

[0793] Output: Data sent to the server

[0794] Specific operation: The device sends the collected learning progress data and emotion data to the server via the Internet.

[0795] Step 8:

[0796] The server readjusts the learning program based on the collected data.

[0797] Input: Collected data (learning progress data and emotion data)

[0798] Output: Retuned learning program

[0799] How it works: The server uses an analytical engine to analyze the data and inputs prompts such as "Please provide a learning plan that will help my child relax" into the generative AI model. Based on the results output by the AI, the learning program is adjusted.

[0800] Step 9:

[0801] The server distributes the re-adjusted educational program to the client terminal.

[0802] Input: Recalibrated learning program

[0803] Output: The reworked program sent to the client terminal

[0804] Specific operation: The server packages the retuned learning program and transmits it to the client terminal via the Internet.

[0805] Step 10:

[0806] Users can check their learning progress data and emotional state, and provide their next learning plan and feedback.

[0807] Input: Visually displayed data

[0808] Output: Next lesson plan and feedback

[0809] Specific operation: Parents check learning progress data and emotional state through the device and provide feedback to their children based on information such as "English vocabulary: 90 points," "Math workbook: completed," and "Emotional state: relaxed."

[0810] The above is the specific processing flow of this system.

[0811] (Application example 2)

[0812] 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."

[0813] Conventional work planning systems do not take into account the emotional data of workers, which can lead to problems such as worker stress and reduced efficiency. In particular, when workers perform monotonous work over long periods of time, they do not take appropriate breaks or review their tasks, resulting in a decline in work efficiency.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving basic information about workers based on input from a user, means for generating an initial work plan based on past production data and efficiency information using a generation AI, means for distributing the generated work plan to a client terminal, means for collecting work results and progress data, means for acquiring and analyzing emotional data about workers using facial recognition technology, means for analyzing the collected data and adjusting the work program, and means for distributing the adjusted work program again to the client terminal. This makes it possible to optimize the work program and maintain efficiency while monitoring the emotional state of workers in real time.

[0815] "User" means a person who uses the system to input basic information to manage and monitor work plans.

[0816] A "worker" is a person who actually performs work according to the work plan generated by the system.

[0817] "Generative AI" is an artificial intelligence algorithm that automatically generates initial work plans using various data.

[0818] "Past production data" is a data set that includes information on past work performance and production.

[0819] "Efficiency information" is information that includes metrics and indicators for evaluating the efficiency of work.

[0820] A "client terminal" is a device through which a worker receives a work plan and inputs work results.

[0821] A "work plan" is a program generated by generative AI that contains specific instructions and goals for workers to carry out their daily work.

[0822] "Work results" is data related to the results and progress of work actually performed by a worker.

[0823] "Progress data" is information that indicates how well work is progressing according to plan.

[0824] "Facial recognition technology" is a technology that uses a camera to recognize a worker's face and analyze specific emotions.

[0825] "Emotion data" is data that indicates the emotional state of a worker, obtained using face recognition technology.

[0826] "Analysis" refers to the process by which a system analyzes information based on collected data and derives useful results.

[0827] An "adjusted work program" is a work plan that has been optimized based on collected data and analysis results from the initial work plan.

[0828] "Distributing" refers to the act of the server sending the generated or adjusted work program to the client terminal.

[0829] This invention is a work support system that combines generative AI and facial recognition technology, and uses the worker's emotional data to optimize the work program and provide individualized work support. Specific embodiments of this system are described below.

[0830] Server Operation

[0831] The server first receives basic information about the workers (roles, target production volume, etc.) entered by the user. Next, it uses generative AI to create an initial work plan based on this information. The work plan is optimized by referencing past production data and efficiency information.

[0832] The generated work plan is distributed to the client terminal. As the worker goes about their daily work, the server collects work progress data and work results from the client terminal. It also uses facial recognition technology to obtain worker emotion data and adjusts the work program based on this. The adjusted work program is then distributed again to the client terminal.

[0833] Client terminal operation

[0834] The client terminal receives the work plan distributed from the server and provides it to the worker as a daily work plan. It also provides an interface for inputting the worker's work results and progress data, and is equipped with facial recognition technology to collect emotion data.

[0835] The device also has the function of transmitting collected work progress data and emotion data to a server and visually displaying the work results. Progress and emotion changes are displayed in graph and chart format, allowing users to grasp the overall status of the worker.

[0836] User Actions

[0837] Users use the system through a client terminal. The user enters basic information about the worker during the initial setup and confirms the generated work plan. The worker then carries out their daily work according to the work plan. Work results and emotional data are entered into the terminal and sent to the server, which optimizes the work program.

[0838] In addition, users can check work progress data and the emotional state of workers through the client terminal, which can provide support to maintain a better work environment.

[0839] Specific examples

[0840] For example, when basic information about a welding worker in a manufacturing plant is entered, the server uses generative AI to generate an appropriate initial work plan. At the same time, facial recognition technology evaluates the worker's emotional state and reflects it in the program accordingly. If the worker is feeling stressed, the system will detect this and the server will readjust the work program.

[0841] On Monday morning, when a worker opens their device, the day's work plan (e.g., 3 hours of welding, 2 hours of parts assembly) is displayed. While working, the device recognizes the worker's facial expressions, and if it detects stress or fatigue, it sends that data to the server. The server analyzes the emotional data and adjusts the work program accordingly.

[0842] Examples of prompts based on this include:

[0843] Due to the worker's high stress level, the next task was changed to lighter work and a break was provided.

[0844] In this way, by combining generative AI and facial recognition technology, the present invention provides an effective work support system that maximizes workers' work efficiency and reduces stress.

[0845] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0846] Step 1:

[0847] The user uses a terminal to input basic information about the worker (role, target production volume, etc.). The input data is sent to the server.

[0848] Input: Worker name, role, target production volume, etc.

[0849] Output: Basic information sent to the server

[0850] Step 2:

[0851] The server generates an initial work plan using a generative AI model based on the received basic information, and the generative AI model optimizes the work plan by referencing past production data and efficiency information.

[0852] Input: Basic information of workers, past production data, efficiency information

[0853] Output: Generated initial work plan

[0854] Step 3:

[0855] The server distributes the generated work plan to the terminal, which then displays the received work plan to the worker.

[0856] Input: Generated initial work plan

[0857] Output: Work plan delivered to the device

[0858] Step 4:

[0859] The worker starts working, and the terminal collects the work results and progress data, while simultaneously collecting the worker's emotional data using facial recognition technology.

[0860] Input: Work results, progress data, emotion data

[0861] Output: Collected work results, progress data, and emotion data

[0862] Step 5:

[0863] The terminal transmits the collected work results, progress data, and emotion data to the server.

[0864] Input: Collected work results, progress data, emotion data

[0865] Output: Work results, progress data, and emotion data sent to the server

[0866] Step 6:

[0867] The server analyzes the received data and readjusts the work program based on the emotion data and work progress. The generative AI model optimizes the work plan based on the new data.

[0868] Input: Work results, progress data, emotion data

[0869] Output: Reworked work program

[0870] Step 7:

[0871] The server again distributes the readjusted work program to the terminal, and the terminal displays the received readjusted work program to the worker.

[0872] Input: Reworked work program

[0873] Output: The re-adjusted working program delivered to the terminal

[0874] Step 8:

[0875] The user can check the work progress data and the worker's emotional state through the client terminal and provide support as needed.

[0876] Input: Work progress data, emotional state

[0877] Output: Feedback provided to the user based on their work progress and emotional state

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments of the invention are described below.

[0895] Server Operation

[0896] The server receives basic information about the child (age, current grades, target university, etc.) entered by the user, and uses this information to create an initial educational program using generative AI. This program is individually optimized by referencing past successful applicant data and deviation score information.

[0897] The generated educational program is distributed to the client device. As the child's learning progresses, the server collects daily learning progress data and test results from the client device. This data is analyzed and the learning program is automatically adjusted. The adjusted program is then distributed again to the client device, allowing the child to continue learning.

[0898] Client terminal operation

[0899] The client device receives the educational program distributed from the server and provides it to the child. Specifically, daily study plans and assignments are displayed on the device screen. As the child progresses with their studies and takes tests, the results are sent to the server via the device.

[0900] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Visual feedback such as graphs and charts is provided, allowing parents to intuitively understand the effectiveness of their children's learning.

[0901] User Actions

[0902] Parents and children use the system through client terminals. Parents first enter basic information about their children and then check the generated educational program. The children then study according to the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[0903] Specific examples

[0904] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[0905] On Monday morning, when a child opens their client device (iPad), the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. These results are sent to the server, which analyzes the data and adjusts the study program accordingly.

[0906] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[0907] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[0908] The processing flow will be explained below.

[0909] Step 1:

[0910] The user uses a dedicated application to input basic information about their child (age, current grades, target university, etc.), and the server receives this information and stores it in a database.

[0911] Step 2:

[0912] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[0913] Step 3:

[0914] The server distributes the generated initial education program to the client terminal, which receives the program and displays it as a daily learning plan.

[0915] Step 4:

[0916] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal has the function of recording the learning progress and sending the results to the server.

[0917] Step 5:

[0918] The client terminal provides an interface for inputting the child's learning results (e.g., test scores), which are then sent to the server.

[0919] Step 6:

[0920] The server analyzes the received learning result data and evaluates the student's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[0921] Step 7:

[0922] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[0923] Step 8:

[0924] Users (parents) can visually check their child's learning progress data and test results through a client device. The data is displayed in graphs and charts, allowing them to intuitively understand their child's learning situation.

[0925] Step 9:

[0926] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[0927] Step 10:

[0928] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[0929] Example 1

[0930] 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."

[0931] In today's educational environment, it is difficult for parents to accurately track their children's learning progress and create optimal learning plans. It is also difficult to monitor children's learning habits and achievements in real time and provide timely feedback. This creates a problem of insufficient efficient learning support aimed at passing specific target universities.

[0932] 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.

[0933] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for using a generation AI to generate an initial educational program based on past successful applicant data and deviation score information, means for distributing the generated educational program to a client terminal, means for the terminal to visually display the educational program, means for the child to progress in their studies, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, and means for distributing the adjusted learning program again to the client terminal. This makes it possible to accurately grasp a child's learning progress and provide an optimal learning plan.

[0934] "User" refers to the parent or guardian who enters and manages their child's learning program.

[0935] "Child" refers to the student for whom the study program is being carried out.

[0936] "Basic information" refers to data necessary to generate a learning program, such as the child's age, current grades, and target university.

[0937] "Generative AI" refers to a system that uses artificial intelligence technology to create a learning program based on past successful applicant data and deviation score information.

[0938] "Successful applicant data" refers to information regarding the grades and learning history of students who have been accepted into specific target universities in the past.

[0939] "Standard deviation information" refers to statistical data that serves as a standard for evaluating learning content and test results.

[0940] "Initial educational program" refers to a learning plan created by the generative AI based on input data from the user.

[0941] "Client terminal" refers to a device used by a user or child to operate the system, and specifically includes a smartphone or tablet.

[0942] "Means for visually displaying educational programs" refers to the function of displaying learning plans and assignments on the device screen in an intuitive manner.

[0943] "Learning Progress Data" refers to information about a child's progress and achievement as they progress through their education.

[0944] "Test results" refers to the test scores and evaluation results a child has taken.

[0945] "Means for analyzing data" refers to computational processes for optimizing learning programs based on collected learning progress data and test results.

[0946] "Means of adjusting learning programs" refers to the process of making changes or additions to existing learning programs based on data analysis.

[0947] "Generative AI model" refers to the algorithms and data models used by generative AI.

[0948] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments are described below.

[0949] Overall system configuration

[0950] This educational support system consists of three components: a server, a client terminal, and a user. The server generates learning programs using a generative AI model and operates based on user input data. The client terminal provides learning programs to users and transmits progress data and test results to the server. Users utilize the system through system operations and learning activities.

[0951] Server Operation

[0952] The server performs the following series of processes.

[0953] 1. Receiving basic information:

[0954] The server receives basic information about the child (age, current grades, target university) entered by the user. For example, a parent might enter data such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points."

[0955] 2. Creation of an initial education program:

[0956] Based on the received basic information, the server uses a generative AI model to generate an initial educational program. The generative AI model references past successful applicant data and deviation score information to create an individually optimized study plan. Specifically, it generates a plan such as "Mathematics: 1 hour, English: 30 minutes, History: 45 minutes."

[0957] 3. Program Delivery:

[0958] The generated educational program is distributed from the server to the client terminal using cloud infrastructure (e.g., AWS or GCP).

[0959] 4. Progress data collection and analysis:

[0960] When a child's learning progress data and test results are sent from the device, the server collects and analyzes the data, and automatically adjusts the content of the learning program based on the analysis results.

[0961] 5. Adjusted Program Re-Delivery:

[0962] The new, adjusted learning program is then sent back to the client device, allowing the child to continue learning optimally.

[0963] Client terminal operation

[0964] The client terminal receives the educational program distributed from the server and operates as follows.

[0965] 1. Display of educational programs:

[0966] The device visually displays the delivered educational program to the child. The daily learning plan and assignments are displayed on the screen. For example, when a child opens a tablet (such as an iPad), the learning plan for that day (e.g., "30 minutes of English vocabulary, 60 minutes of math workbooks, 30 minutes of history") is displayed.

[0967] 2. Learning Progression:

[0968] Children learn by following a program displayed on the device, for example, memorizing a list of English words or solving math problems.

[0969] 3. Enter and submit test results:

[0970] Once the child has finished studying, they take a test and enter the results into the device. The entered test results are sent to the server via the device. For example, data such as "English vocabulary test: 90 points" is sent.

[0971] 4. View progress data:

[0972] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Graphs and charts can be used to intuitively understand the effectiveness of learning.

[0973] User Actions

[0974] 1. Enter basic information:

[0975] The user (parent) first enters basic information about their child into the client terminal, following prompts such as, "How old is your child?", "Please enter their current grades.", and "Please tell us your target university."

[0976] 2. Implementation of the learning program:

[0977] Children follow the program displayed on the device to study, and when they finish, they enter and submit their test results.

[0978] 3. Check your progress:

[0979] Parents can check their child's learning progress through the device and provide feedback as needed. For example, in the evening, a parent can check their child's learning progress on the device and see details such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding."

[0980] As described above, the present invention can reduce the burden on users and provide optimized learning support, thereby enabling efficient and effective learning support aimed at helping users pass the entrance exams to specific target universities.

[0981] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0982] Step 1:

[0983] The user enters basic information about the child.

[0984] (Specific action)

[0985] The user (parent) enters basic information about their child (age, grades, target university, etc.) into the client terminal. For example, information such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points" is entered into the input form.

[0986] (input)

[0987] Age, grades, target university

[0988] (output)

[0989] Basic information is sent to the server

[0990] Step 2:

[0991] The server receives the basic information.

[0992] (Specific action)

[0993] The server receives basic information sent from the client terminal, which is then stored in a database.

[0994] (input)

[0995] Basic information sent from the client terminal

[0996] (output)

[0997] Basic information is stored on the server

[0998] Step 3:

[0999] The server generates the initial training program.

[1000] (Specific action)

[1001] The server uses a generative AI model to generate an initial educational program based on basic information. The generative AI model references past successful applicant data and deviation score information. For example, a plan such as "Math: 1 hour, English: 30 minutes, History: 45 minutes" may be generated.

[1002] (input)

[1003] Basic information, successful applicant data, deviation score information

[1004] (output)

[1005] Early Education Program

[1006] Step 4:

[1007] The server distributes the educational program to the terminal.

[1008] (Specific action)

[1009] The server distributes the generated educational programs to client devices via HTTPS protocol and AWS / GCP cloud infrastructure.

[1010] (input)

[1011] Early Education Program

[1012] (output)

[1013] The educational program is sent to the device.

[1014] Step 5:

[1015] The terminal displays the educational program.

[1016] (Specific action)

[1017] The client device displays the received educational program on its user interface. When a child opens the device, a learning plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history" appears on the screen.

[1018] (input)

[1019] Educational programs sent from the server

[1020] (output)

[1021] The educational program will be displayed on the device.

[1022] Step 6:

[1023] Children progress in their learning.

[1024] (Specific action)

[1025] Children learn by following educational programs displayed on the device, for example, memorizing a list of English words and solving math problems with a digital pen.

[1026] (input)

[1027] Educational Program

[1028] (output)

[1029] Learning progress data

[1030] Step 7:

[1031] The device sends the test results to the server.

[1032] (Specific action)

[1033] After the child has finished studying, they enter their test results into the device and send them to the server. For example, they might enter "90 points" as the test result for English vocabulary.

[1034] (input)

[1035] Test Results

[1036] (output)

[1037] Test results are sent to the server

[1038] Step 8:

[1039] The server analyzes the data and adjusts the learning program.

[1040] (Specific action)

[1041] The server analyzes the collected test results and learning progress data and uses a generative AI model to adjust the learning program, for example, increasing the difficulty level if the student performs well in English and adding review if the student performs poorly.

[1042] (input)

[1043] Test results, learning progress data

[1044] (output)

[1045] Tailored Learning Programs

[1046] Step 9:

[1047] The server distributes the updated educational program to the terminal.

[1048] (Specific action)

[1049] The adjusted new learning program is again distributed from the server to the client terminal.

[1050] (input)

[1051] Tailored Learning Programs

[1052] (output)

[1053] Updated educational programs are sent to the device.

[1054] Step 10:

[1055] The terminal displays the updated educational program.

[1056] (Specific action)

[1057] The client device will then display the redistributed educational program again. When the child opens the device, they will see a lesson plan for the next day, such as "40 minutes of English vocabulary, 70 minutes of math problems, and 30 minutes of reviewing new history."

[1058] (input)

[1059] Updated Educational Programs

[1060] (output)

[1061] The updated educational program will be displayed on the device.

[1062] These are the specific processing steps of this system, which makes it possible to properly manage a child's learning progress and provide an optimal learning plan.

[1063] (Application example 1)

[1064] 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."

[1065] Conventional educational support systems have struggled to efficiently track daily learning progress and provide real-time feedback. Furthermore, the lack of an optimal system for utilizing client devices such as smartphones and head-mounted displays made it difficult for students to follow an optimal learning plan. This left students with an unclear path to success at their desired educational institution.

[1066] 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.

[1067] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI, means for distributing the generated educational program to a client terminal, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, means for distributing the adjusted learning program again to the client terminal, means for visually displaying the learning progress data and test results, means for providing real-time visual feedback, and means for using a client terminal installed on a smartphone or head-mounted display. This allows users to efficiently track their learning progress and receive real-time feedback, enabling them to study effectively to pass their target educational institution according to an optimal learning plan.

[1068] "Users" refers to parents and children who use the system.

[1069] "Basic information" refers to information such as the child's age, current grades, and target university.

[1070] "Generative AI" refers to artificial intelligence that generates optimal educational programs based on user data, past successful applicant data, and deviation score information.

[1071] An "educational program" refers to a plan that outlines the learning plans and assignments a child needs to pass in order to be accepted into their desired educational institution.

[1072] "Client terminal" refers to a device that is directly operated by a user, such as a smartphone, tablet, or head-mounted display.

[1073] "Test results" refers to test scores and evaluation information taken by a child.

[1074] "Learning progress data" refers to a child's progress and achievements as they progress through their daily studies.

[1075] "Analysis" refers to the process of analyzing collected test results and learning progress data using machine learning and statistical methods.

[1076] "Feedback" refers to the improvements and advice that the AI ​​provides to children based on the analysis results.

[1077] "Visual feedback" refers to providing users with an intuitive understanding of their learning progress and results using graphs, charts, etc.

[1078] "Real-time" refers to data being processed almost instantly, with results and feedback provided without delay.

[1079] "Target university" refers to the specific educational institution that a child aspires to attend.

[1080] A "head-mounted display" refers to a display device that is worn on the user's head.

[1081] This invention is an educational support system that uses generative AI, and aims to provide an optimal learning plan based on input from users (parents and children) and to progress learning according to that plan. Specific embodiments of the invention are described below.

[1082] Server Operation

[1083] The server first receives basic information about the child (age, current grades, target university, etc.) entered by the user. Based on this information, the server uses a generation AI to reference data on past successful applicants and deviation scores to generate an initial educational program. The generated program is then distributed to the client device.

[1084] As the learning progresses, the server collects daily learning progress data and test results from the client device. This collected data is analyzed and the learning program is automatically adjusted. The adjusted program is then sent back to the client device, allowing the learning to continue.

[1085] The main software used is a generative AI model (e.g., OpenAI's API), which analyzes data and generates programs.

[1086] Client terminal operation

[1087] The client device (smartphone or head-mounted display) receives the educational program distributed from the server and provides it to the child. Specifically, the program displays daily learning plans and assignments on the device screen.

[1088] As children study and take tests, the results are sent to the server via the client device. The device also has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning status in real time. Visual feedback allows parents to intuitively understand the effectiveness of their children's learning.

[1089] User Actions

[1090] Parents and children use this system through client terminals. Parents first enter basic information about their children and then check the generated educational program. Children follow the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[1091] Specific examples

[1092] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[1093] On Monday morning, when a child opens their smartphone, the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. The results are sent to a server, which analyzes the data and adjusts the study program accordingly.

[1094] Specific examples of prompts include:

[1095] User information: Age 16, current grades: 85 in math, 78 in English, 90 in history, target university: University A

[1096] Generate the best study plan to get into your target university.

[1097] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[1098] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[1099] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1100] Step 1:

[1101] The user uses a client device to input basic information about their child. The user enters information such as age, current grades, and target university into the device's input screen, and the information is sent to the server. The input information is used as basic data for the generative AI model to generate an educational program. Examples of input data include "age: 16 years old," "math: 85 points," "English: 78 points," "history: 90 points," and "target university: University A."

[1102] Step 2:

[1103] The server generates an educational program using a generative AI model. Based on the basic information received from the user, the server references past successful applicant data and deviation score information to generate a prompt. For example, a prompt such as "User information: age 16, current grades 85 points in math, 78 points in English, 90 points in history, target university A. Please generate the optimal study plan to pass the target university entrance exam." is generated. This prompt is input into the generative AI model, which outputs an optimized educational program.

[1104] Step 3:

[1105] The generated educational program is delivered to the client terminal. The server sends the plan obtained from the generative AI model to the client terminal. For example, the generated program is a specific study plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history." This program is displayed on the terminal screen, and the user can use it to proceed with their studies.

[1106] Step 4:

[1107] The user studies and inputs test results and learning progress data. The child progresses with their daily studies and takes tests, which are then entered into the client terminal. For example, data such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding" are entered. This input data is sent to the server.

[1108] Step 5:

[1109] The server collects and analyzes test results and learning progress data. The server collects and analyzes data sent from client devices. This analysis uses machine learning algorithms and statistical methods. For example, if a student's understanding of a particular subject is insufficient, the server adjusts the learning plan to strengthen the content of that subject. The analysis results become the basis for a new learning program.

[1110] Step 6:

[1111] The adjusted learning program is then sent back to the client device. The server generates a new learning program based on the collected and analyzed data and sends it to the client device. This allows the user to receive a new learning plan in real time and progress with their studies efficiently.

[1112] Step 7:

[1113] Visually displays learning progress data and test results. The client device visually displays new programs and progress data received from the server in graphs and charts. This allows parents and children to intuitively understand learning results and progress. For example, results such as "Math: 88 points," "English: 80 points," and "History: 92 points" are displayed visually.

[1114] Step 8:

[1115] The user adjusts the next lesson plan based on visual feedback. Parents and children consider the next lesson plan based on visually displayed progress data and test results. They provide feedback as needed to improve learning. This feedback is also sent to the server and used to optimize the next lesson plan using a generative AI model.

[1116] 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.

[1117] The present invention is an educational support system that combines generative AI and an emotion engine, and aims to optimize learning programs using emotion data from users (parents and children) and provide individually tailored learning support. Specific embodiments of this system are described below.

[1118] Server Operation

[1119] The server receives basic information about the child entered by the user (age, current grades, target university, etc.) and uses this information to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores. Furthermore, it is equipped with an emotion engine that acquires and analyzes the user's emotional data to adjust the learning program more effectively.

[1120] The generated educational program is distributed to the client terminal. As users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal. Emotional data is also collected at the same time, and the learning program is adjusted based on this. The readjusted learning program is then distributed again to the client terminal.

[1121] Client terminal operation

[1122] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's learning progress data and test results, and is equipped with a function for collecting emotional data. For example, it can use facial recognition and voice analysis technology to recognize a child's emotions while they are studying in real time.

[1123] The device also has the function of sending collected learning progress data and emotional data to a server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to grasp their child's learning status comprehensively.

[1124] User Actions

[1125] Parents and children use the system through client terminals. Parents enter basic information about their children during the initial setup and review the generated educational program. The children then follow the program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[1126] Parents can also check their child's learning progress data and emotional state through the client device, allowing them to provide support to maintain a better learning environment.

[1127] Specific examples

[1128] For example, when a child aiming for University B is entered as a target, the server uses generative AI to generate an appropriate early education program. At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the learning program.

[1129] When a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While the child is studying, the device recognizes the child's facial expressions and, if it detects stress or fatigue, sends that data to a server. The server analyzes the emotional data and adjusts the study program to make it more relaxing.

[1130] In the evening, parents can check their child's learning progress and emotional state on the device, and visual information such as "English Vocabulary: 90 points," "Math Problems: Completed," and "Emotional State: Relaxed" will be displayed. Based on this, parents can provide the child with the next learning plan and feedback to support their child's learning.

[1131] In this way, by combining generative AI and an emotion engine, the present invention provides an effective educational system that maximizes children's learning efficiency and reduces stress.

[1132] The processing flow will be explained below.

[1133] Step 1:

[1134] The user (parent) uses a dedicated application to input basic information about their child (age, current grades, target university, etc.). The server receives this information and stores it in a database.

[1135] Step 2:

[1136] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[1137] Step 3:

[1138] The server uses an emotion engine to reflect the user's emotional data in the initial education program, which utilizes information on the user's stress level and concentration.

[1139] Step 4:

[1140] The generated educational program is distributed to the client terminal, which receives the program and displays it to the user (child) as a daily learning plan.

[1141] Step 5:

[1142] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal records the learning progress and collects emotional data as appropriate.

[1143] Step 6:

[1144] The client device captures emotional data using facial expression recognition and voice analysis while the child is learning, and this data is sent to the server in real time.

[1145] Step 7:

[1146] The server analyzes the received learning result data and emotional data to evaluate the child's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[1147] Step 8:

[1148] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[1149] Step 9:

[1150] Users (parents) can visually check their child's learning progress data and emotional state in graphs and charts on a client device, allowing them to get a comprehensive understanding of their child's learning situation.

[1151] Step 10:

[1152] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[1153] Step 11:

[1154] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[1155] Example 2

[1156] 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."

[1157] With conventional educational support systems, it was difficult to optimize learning programs that took into account each child's emotional state, resulting in problems such as reduced learning efficiency and excessive stress.In addition, the lack of visual feedback on learning progress and emotional state made it difficult for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[1158] 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.

[1159] In this invention, the server includes means for receiving basic information about a child based on user input, means for generating an initial educational program based on past successful candidate data and standardization information using a generation AI, means for distributing the generated educational program to a client terminal, means for acquiring and analyzing the user's emotional data, means for adjusting the educational program based on the emotional data and learning progress data, and means for analyzing the collected data and distributing the adjusted educational program back to the client terminal. This enables the creation and adjustment of educational programs that take into account each child's individual emotional state, thereby improving learning efficiency and reducing stress. Furthermore, visual feedback of learning progress data, test results, and emotional state makes it easier for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[1160] "User" refers to a parent or teacher who uses the system to assist in the education of their child.

[1161] "Child's basic information" refers to information necessary to generate and optimize a learning program, such as the child's age, current grades, and desired educational facilities.

[1162] "Generative AI" refers to artificial intelligence technology that generates educational programs based on past successful applicant data and standardization information.

[1163] "Educational program" refers to the learning plan and content constructed in line with a child's learning goals.

[1164] "Client Terminal" refers to the device used by users and children to receive and engage with educational programs.

[1165] "Emotional data" refers to data collected to understand a child's emotional state while learning, and refers to information obtained using facial recognition technology, voice analysis technology, etc.

[1166] "Learning Progress Data" means data that shows a child's learning progress and achievements, such as progress in a learning program and test results.

[1167] "Standardized information" refers to standard information necessary to optimize learning programs, such as educational evaluation criteria and entrance examination information for target educational facilities.

[1168] "Emotion engine" refers to an engine that analyzes the emotional data of users and children and adjusts learning programs more effectively.

[1169] "Visual feedback" refers to a function that visually displays learning progress data and emotional state in graphs and charts, providing it in a way that is easy for users to understand.

[1170] "Adjustment" refers to changing the content or difficulty of a learning program based on collected data.

[1171] "Delivery" refers to sending the generated educational program or the re-adjusted learning program to the client terminal.

[1172] MODE FOR CARRYING OUT THE INVENTION

[1173] The present invention is an educational support system that combines a generative AI model and an emotion engine, and aims to provide personalized learning support by optimizing learning programs using emotion data from users (parents and children). Specific embodiments of this system are described below.

[1174] Server Operation

[1175] The server receives basic information about the child (age, grades, target educational institution, etc.) entered by the user. Based on this information, the server creates an initial educational program using a generative AI model. An example of a generative AI model is an AI algorithm used to generate existing study plans. A prompt such as "Please create a study plan for target university B" is used.

[1176] The educational program is optimized by referencing data on past successful candidates and standardization information. Furthermore, the server is equipped with an emotion engine that acquires and analyzes the emotional data of users and children. This emotional data includes real-time emotion recognition results using facial recognition and voice analysis technology. This allows for more effective adjustment of the learning program.

[1177] The generated educational program is distributed to the client terminal. As the users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal and readjusts the program based on this data. This readjusted learning program is also distributed again to the client terminal.

[1178] Client terminal operation

[1179] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's study progress data and test results. Furthermore, the terminal has the function of recognizing the child's emotions in real time while studying using facial recognition and voice analysis technology and collecting emotional data.

[1180] For example, if a child feels stressed or tired while studying, the device will detect this and send it as emotional data to the server. The device also has the function of sending collected learning progress data and emotional data to the server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to have a comprehensive understanding of their child's learning situation.

[1181] User Actions

[1182] Users (parents and children) use the system through a client terminal. Parents enter basic information about their children during the initial setup and confirm the generated educational program. The children then follow this program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[1183] For example, the target university is set to University B, and the generative AI model generates an initial program using a prompt such as "Please create a study plan for University B." At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the study program.

[1184] Specifically, when a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While studying, the device recognizes the child's facial expressions, and if it detects stress or fatigue, it sends the data to a server. The server analyzes the emotional data and adjusts the study program to be more relaxing. In the evening, when a parent checks their child's study progress and emotional state on the device, information such as "English vocabulary: 90 points," "Math workbooks: completed," and "Emotional state: relaxed" is visually displayed. Based on this, parents can provide the child with the next study plan and feedback to support their child's learning.

[1185] In this way, the present invention provides an effective educational support system that maximizes children's learning efficiency and reduces stress by combining a generative AI model and an emotion engine.

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1:

[1188] The server receives basic information about the child (age, grades, target educational institution, etc.) input by the user.

[1189] Input: Basic information about the child entered by the user

[1190] Output: A dataset of basic information about children

[1191] Specific operation: Parents operate the client terminal to enter their child's information, which is then sent to the server and stored in the database.

[1192] Step 2:

[1193] The server generates an initial educational program using a generative AI model based on the received basic information.

[1194] Input: Dataset of basic information about children

[1195] Output: Early Education Program

[1196] Specific operation: The server inputs a prompt such as "Please create a study plan for the target university, University B" into the generative AI model, receives the study plan output by the AI, and constructs it as an initial education program.

[1197] Step 3:

[1198] The server distributes the generated educational program to the client terminal.

[1199] Enter: Early Education Programs

[1200] Output: The training program sent to the client terminal

[1201] Specific operation: The server packages the educational program and transmits it to the client terminal via the Internet.

[1202] Step 4:

[1203] The server acquires and analyzes the user's emotional data.

[1204] Input: User emotion data

[1205] Output: Analyzed emotion data and adjustment instructions

[1206] How it works: Using facial recognition and voice analysis technology, the server collects and analyzes the user's emotional state in real time, and based on the results, determines whether the learning program needs to be adjusted.

[1207] Step 5:

[1208] The terminal provides the user with a daily study plan via the client terminal, and allows the user to start studying.

[1209] Input: Delivered educational program

[1210] Output: Learning progress data

[1211] Specific operation: The educational program is displayed on the device, and the child follows it to progress through the learning process. The child's learning progress is entered into the device.

[1212] Step 6:

[1213] The device collects learning progress data and emotional data.

[1214] Input: Learning progress data, user emotion data

[1215] Output: Collected dataset

[1216] Specific operation: Sensors and cameras built into the device collect learning progress and user emotions in real time and store them as data.

[1217] Step 7:

[1218] The terminal transmits the collected data to the server.

[1219] Input: Collected dataset

[1220] Output: Data sent to the server

[1221] Specific operation: The device sends the collected learning progress data and emotion data to the server via the Internet.

[1222] Step 8:

[1223] The server readjusts the learning program based on the collected data.

[1224] Input: Collected data (learning progress data and emotion data)

[1225] Output: Retuned learning program

[1226] How it works: The server uses an analytical engine to analyze the data and inputs prompts such as "Please provide a learning plan that will help my child relax" into the generative AI model. Based on the results output by the AI, the learning program is adjusted.

[1227] Step 9:

[1228] The server distributes the re-adjusted educational program to the client terminal.

[1229] Input: Recalibrated learning program

[1230] Output: The reworked program sent to the client terminal

[1231] Specific operation: The server packages the retuned learning program and transmits it to the client terminal via the Internet.

[1232] Step 10:

[1233] Users can check their learning progress data and emotional state, and provide their next learning plan and feedback.

[1234] Input: Visually displayed data

[1235] Output: Next lesson plan and feedback

[1236] Specific operation: Parents check learning progress data and emotional state through the device and provide feedback to their children based on information such as "English vocabulary: 90 points," "Math workbook: completed," and "Emotional state: relaxed."

[1237] The above is the specific processing flow of this system.

[1238] (Application example 2)

[1239] 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."

[1240] Conventional work planning systems do not take into account the emotional data of workers, which can lead to problems such as worker stress and reduced efficiency. In particular, when workers perform monotonous work over long periods of time, they do not take appropriate breaks or review their tasks, resulting in a decline in work efficiency.

[1241] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving basic information about workers based on input from a user, means for generating an initial work plan based on past production data and efficiency information using a generation AI, means for distributing the generated work plan to a client terminal, means for collecting work results and progress data, means for acquiring and analyzing emotional data about workers using facial recognition technology, means for analyzing the collected data and adjusting the work program, and means for distributing the adjusted work program again to the client terminal. This makes it possible to optimize the work program and maintain efficiency while monitoring the emotional state of workers in real time.

[1242] "User" means a person who uses the system to input basic information to manage and monitor work plans.

[1243] A "worker" is a person who actually performs work according to the work plan generated by the system.

[1244] "Generative AI" is an artificial intelligence algorithm that automatically generates initial work plans using various data.

[1245] "Past production data" is a data set that includes information on past work performance and production.

[1246] "Efficiency information" is information that includes metrics and indicators for evaluating the efficiency of work.

[1247] A "client terminal" is a device through which a worker receives a work plan and inputs work results.

[1248] A "work plan" is a program generated by generative AI that contains specific instructions and goals for workers to carry out their daily work.

[1249] "Work results" is data related to the results and progress of work actually performed by a worker.

[1250] "Progress data" is information that indicates how well work is progressing according to plan.

[1251] "Facial recognition technology" is a technology that uses a camera to recognize a worker's face and analyze specific emotions.

[1252] "Emotion data" is data that indicates the emotional state of a worker, obtained using face recognition technology.

[1253] "Analysis" refers to the process by which a system analyzes information based on collected data and derives useful results.

[1254] An "adjusted work program" is a work plan that has been optimized based on collected data and analysis results from the initial work plan.

[1255] "Distributing" refers to the act of the server sending the generated or adjusted work program to the client terminal.

[1256] This invention is a work support system that combines generative AI and facial recognition technology, and uses the worker's emotional data to optimize the work program and provide individualized work support. Specific embodiments of this system are described below.

[1257] Server Operation

[1258] The server first receives basic information about the workers (roles, target production volume, etc.) entered by the user. Next, it uses generative AI to create an initial work plan based on this information. The work plan is optimized by referencing past production data and efficiency information.

[1259] The generated work plan is distributed to the client terminal. As the worker goes about their daily work, the server collects work progress data and work results from the client terminal. It also uses facial recognition technology to obtain worker emotion data and adjusts the work program based on this. The adjusted work program is then distributed again to the client terminal.

[1260] Client terminal operation

[1261] The client terminal receives the work plan distributed from the server and provides it to the worker as a daily work plan. It also provides an interface for inputting the worker's work results and progress data, and is equipped with facial recognition technology to collect emotion data.

[1262] The device also has the function of transmitting collected work progress data and emotion data to a server and visually displaying the work results. Progress and emotion changes are displayed in graph and chart format, allowing users to grasp the overall status of the worker.

[1263] User Actions

[1264] Users use the system through a client terminal. The user enters basic information about the worker during the initial setup and confirms the generated work plan. The worker then carries out their daily work according to the work plan. Work results and emotional data are entered into the terminal and sent to the server, which optimizes the work program.

[1265] In addition, users can check work progress data and the emotional state of workers through the client terminal, which can provide support to maintain a better work environment.

[1266] Specific examples

[1267] For example, when basic information about a welding worker in a manufacturing plant is entered, the server uses generative AI to generate an appropriate initial work plan. At the same time, facial recognition technology evaluates the worker's emotional state and reflects it in the program accordingly. If the worker is feeling stressed, the system will detect this and the server will readjust the work program.

[1268] On Monday morning, when a worker opens their device, the day's work plan (e.g., 3 hours of welding, 2 hours of parts assembly) is displayed. While working, the device recognizes the worker's facial expressions, and if it detects stress or fatigue, it sends that data to the server. The server analyzes the emotional data and adjusts the work program accordingly.

[1269] Examples of prompts based on this include:

[1270] Due to the worker's high stress level, the next task was changed to lighter work and a break was provided.

[1271] In this way, by combining generative AI and facial recognition technology, the present invention provides an effective work support system that maximizes workers' work efficiency and reduces stress.

[1272] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1273] Step 1:

[1274] The user uses a terminal to input basic information about the worker (role, target production volume, etc.). The input data is sent to the server.

[1275] Input: Worker name, role, target production volume, etc.

[1276] Output: Basic information sent to the server

[1277] Step 2:

[1278] The server generates an initial work plan using a generative AI model based on the received basic information, and the generative AI model optimizes the work plan by referencing past production data and efficiency information.

[1279] Input: Basic information of workers, past production data, efficiency information

[1280] Output: Generated initial work plan

[1281] Step 3:

[1282] The server distributes the generated work plan to the terminal, which then displays the received work plan to the worker.

[1283] Input: Generated initial work plan

[1284] Output: Work plan delivered to the device

[1285] Step 4:

[1286] The worker starts working, and the terminal collects the work results and progress data, while simultaneously collecting the worker's emotional data using facial recognition technology.

[1287] Input: Work results, progress data, emotion data

[1288] Output: Collected work results, progress data, and emotion data

[1289] Step 5:

[1290] The terminal transmits the collected work results, progress data, and emotion data to the server.

[1291] Input: Collected work results, progress data, emotion data

[1292] Output: Work results, progress data, and emotion data sent to the server

[1293] Step 6:

[1294] The server analyzes the received data and readjusts the work program based on the emotion data and work progress. The generative AI model optimizes the work plan based on the new data.

[1295] Input: Work results, progress data, emotion data

[1296] Output: Reworked work program

[1297] Step 7:

[1298] The server again distributes the readjusted work program to the terminal, and the terminal displays the received readjusted work program to the worker.

[1299] Input: Reworked work program

[1300] Output: The re-adjusted working program delivered to the terminal

[1301] Step 8:

[1302] The user can check the work progress data and the worker's emotional state through the client terminal and provide support as needed.

[1303] Input: Work progress data, emotional state

[1304] Output: Feedback provided to the user based on their work progress and emotional state

[1305] 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.

[1306] 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.

[1307] 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.

[1308] [Fourth embodiment]

[1309] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1310] 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.

[1311] 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).

[1312] 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.

[1313] 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.

[1314] 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).

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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."

[1322] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments of the invention are described below.

[1323] Server Operation

[1324] The server receives basic information about the child (age, current grades, target university, etc.) entered by the user, and uses this information to create an initial educational program using generative AI. This program is individually optimized by referencing past successful applicant data and deviation score information.

[1325] The generated educational program is distributed to the client device. As the child's learning progresses, the server collects daily learning progress data and test results from the client device. This data is analyzed and the learning program is automatically adjusted. The adjusted program is then distributed again to the client device, allowing the child to continue learning.

[1326] Client terminal operation

[1327] The client device receives the educational program distributed from the server and provides it to the child. Specifically, daily study plans and assignments are displayed on the device screen. As the child progresses with their studies and takes tests, the results are sent to the server via the device.

[1328] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Visual feedback such as graphs and charts is provided, allowing parents to intuitively understand the effectiveness of their children's learning.

[1329] User Actions

[1330] Parents and children use the system through client terminals. Parents first enter basic information about their children and then check the generated educational program. The children then study according to the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[1331] Specific examples

[1332] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[1333] On Monday morning, when a child opens their client device (iPad), the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. These results are sent to the server, which analyzes the data and adjusts the study program accordingly.

[1334] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[1335] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[1336] The processing flow will be explained below.

[1337] Step 1:

[1338] The user uses a dedicated application to input basic information about their child (age, current grades, target university, etc.), and the server receives this information and stores it in a database.

[1339] Step 2:

[1340] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[1341] Step 3:

[1342] The server distributes the generated initial education program to the client terminal, which receives the program and displays it as a daily learning plan.

[1343] Step 4:

[1344] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal has the function of recording the learning progress and sending the results to the server.

[1345] Step 5:

[1346] The client terminal provides an interface for inputting the child's learning results (e.g., test scores), which are then sent to the server.

[1347] Step 6:

[1348] The server analyzes the received learning result data and evaluates the student's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[1349] Step 7:

[1350] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[1351] Step 8:

[1352] Users (parents) can visually check their child's learning progress data and test results through a client device. The data is displayed in graphs and charts, allowing them to intuitively understand their child's learning situation.

[1353] Step 9:

[1354] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[1355] Step 10:

[1356] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[1357] Example 1

[1358] 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."

[1359] In today's educational environment, it is difficult for parents to accurately track their children's learning progress and create optimal learning plans. It is also difficult to monitor children's learning habits and achievements in real time and provide timely feedback. This creates a problem of insufficient efficient learning support aimed at passing specific target universities.

[1360] 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.

[1361] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for using a generation AI to generate an initial educational program based on past successful applicant data and deviation score information, means for distributing the generated educational program to a client terminal, means for the terminal to visually display the educational program, means for the child to progress in their studies, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, and means for distributing the adjusted learning program again to the client terminal. This makes it possible to accurately grasp a child's learning progress and provide an optimal learning plan.

[1362] "User" refers to the parent or guardian who enters and manages their child's learning program.

[1363] "Child" refers to the student for whom the study program is being carried out.

[1364] "Basic information" refers to data necessary to generate a learning program, such as the child's age, current grades, and target university.

[1365] "Generative AI" refers to a system that uses artificial intelligence technology to create a learning program based on past successful applicant data and deviation score information.

[1366] "Successful applicant data" refers to information regarding the grades and learning history of students who have been accepted into specific target universities in the past.

[1367] "Standard deviation information" refers to statistical data that serves as a standard for evaluating learning content and test results.

[1368] "Initial educational program" refers to a learning plan created by the generative AI based on input data from the user.

[1369] "Client terminal" refers to a device used by a user or child to operate the system, and specifically includes a smartphone or tablet.

[1370] "Means for visually displaying educational programs" refers to the function of displaying learning plans and assignments on the device screen in an intuitive manner.

[1371] "Learning Progress Data" refers to information about a child's progress and achievement as they progress through their education.

[1372] "Test results" refers to the test scores and evaluation results a child has taken.

[1373] "Means for analyzing data" refers to computational processes for optimizing learning programs based on collected learning progress data and test results.

[1374] "Means of adjusting learning programs" refers to the process of making changes or additions to existing learning programs based on data analysis.

[1375] "Generative AI model" refers to the algorithms and data models used by generative AI.

[1376] This invention is an educational support system that uses generative AI and aims to provide users (parents and children) with an optimal study plan to help them pass a specific target university and then execute that plan. Specific embodiments are described below.

[1377] Overall system configuration

[1378] This educational support system consists of three components: a server, a client terminal, and a user. The server generates learning programs using a generative AI model and operates based on user input data. The client terminal provides learning programs to users and transmits progress data and test results to the server. Users utilize the system through system operations and learning activities.

[1379] Server Operation

[1380] The server performs the following series of processes.

[1381] 1. Receiving basic information:

[1382] The server receives basic information about the child (age, current grades, target university) entered by the user. For example, a parent might enter data such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points."

[1383] 2. Creation of an initial education program:

[1384] Based on the received basic information, the server uses a generative AI model to generate an initial educational program. The generative AI model references past successful applicant data and deviation score information to create an individually optimized study plan. Specifically, it generates a plan such as "Mathematics: 1 hour, English: 30 minutes, History: 45 minutes."

[1385] 3. Program Delivery:

[1386] The generated educational program is distributed from the server to the client terminal using cloud infrastructure (e.g., AWS or GCP).

[1387] 4. Progress data collection and analysis:

[1388] When a child's learning progress data and test results are sent from the device, the server collects and analyzes the data, and automatically adjusts the content of the learning program based on the analysis results.

[1389] 5. Adjusted Program Re-Delivery:

[1390] The new, adjusted learning program is then sent back to the client device, allowing the child to continue learning optimally.

[1391] Client terminal operation

[1392] The client terminal receives the educational program distributed from the server and operates as follows.

[1393] 1. Display of educational programs:

[1394] The device visually displays the delivered educational program to the child. The daily learning plan and assignments are displayed on the screen. For example, when a child opens a tablet (such as an iPad), the learning plan for that day (e.g., "30 minutes of English vocabulary, 60 minutes of math workbooks, 30 minutes of history") is displayed.

[1395] 2. Learning Progression:

[1396] Children learn by following a program displayed on the device, for example, memorizing a list of English words or solving math problems.

[1397] 3. Enter and submit test results:

[1398] Once the child has finished studying, they take a test and enter the results into the device. The entered test results are sent to the server via the device. For example, data such as "English vocabulary test: 90 points" is sent.

[1399] 4. View progress data:

[1400] The device has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning situation in real time. Graphs and charts can be used to intuitively understand the effectiveness of learning.

[1401] User Actions

[1402] 1. Enter basic information:

[1403] The user (parent) first enters basic information about their child into the client terminal, following prompts such as, "How old is your child?", "Please enter their current grades.", and "Please tell us your target university."

[1404] 2. Implementation of the learning program:

[1405] Children follow the program displayed on the device to study, and when they finish, they enter and submit their test results.

[1406] 3. Check your progress:

[1407] Parents can check their child's learning progress through the device and provide feedback as needed. For example, in the evening, a parent can check their child's learning progress on the device and see details such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding."

[1408] As described above, the present invention can reduce the burden on users and provide optimized learning support, thereby enabling efficient and effective learning support aimed at helping users pass the entrance exams to specific target universities.

[1409] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1410] Step 1:

[1411] The user enters basic information about the child.

[1412] (Specific action)

[1413] The user (parent) enters basic information about their child (age, grades, target university, etc.) into the client terminal. For example, information such as "Age: 15 years old" and "Grades: English 80 points, Math 70 points" is entered into the input form.

[1414] (input)

[1415] Age, grades, target university

[1416] (output)

[1417] Basic information is sent to the server

[1418] Step 2:

[1419] The server receives the basic information.

[1420] (Specific action)

[1421] The server receives basic information sent from the client terminal, which is then stored in a database.

[1422] (input)

[1423] Basic information sent from the client terminal

[1424] (output)

[1425] Basic information is stored on the server

[1426] Step 3:

[1427] The server generates the initial training program.

[1428] (Specific action)

[1429] The server uses a generative AI model to generate an initial educational program based on basic information. The generative AI model references past successful applicant data and deviation score information. For example, a plan such as "Math: 1 hour, English: 30 minutes, History: 45 minutes" may be generated.

[1430] (input)

[1431] Basic information, successful applicant data, deviation score information

[1432] (output)

[1433] Early Education Program

[1434] Step 4:

[1435] The server distributes the educational program to the terminal.

[1436] (Specific action)

[1437] The server distributes the generated educational programs to client devices via HTTPS protocol and AWS / GCP cloud infrastructure.

[1438] (input)

[1439] Early Education Program

[1440] (output)

[1441] The educational program is sent to the device.

[1442] Step 5:

[1443] The terminal displays the educational program.

[1444] (Specific action)

[1445] The client device displays the received educational program on its user interface. When a child opens the device, a learning plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history" appears on the screen.

[1446] (input)

[1447] Educational programs sent from the server

[1448] (output)

[1449] The educational program will be displayed on the device.

[1450] Step 6:

[1451] Children progress in their learning.

[1452] (Specific action)

[1453] Children learn by following educational programs displayed on the device, for example, memorizing a list of English words and solving math problems with a digital pen.

[1454] (input)

[1455] Educational Program

[1456] (output)

[1457] Learning progress data

[1458] Step 7:

[1459] The device sends the test results to the server.

[1460] (Specific action)

[1461] After the child has finished studying, they enter their test results into the device and send them to the server. For example, they might enter "90 points" as the test result for English vocabulary.

[1462] (input)

[1463] Test Results

[1464] (output)

[1465] Test results are sent to the server

[1466] Step 8:

[1467] The server analyzes the data and adjusts the learning program.

[1468] (Specific action)

[1469] The server analyzes the collected test results and learning progress data and uses a generative AI model to adjust the learning program, for example, increasing the difficulty level if the student performs well in English and adding review if the student performs poorly.

[1470] (input)

[1471] Test results, learning progress data

[1472] (output)

[1473] Tailored Learning Programs

[1474] Step 9:

[1475] The server distributes the updated educational program to the terminal.

[1476] (Specific action)

[1477] The adjusted new learning program is again distributed from the server to the client terminal.

[1478] (input)

[1479] Tailored Learning Programs

[1480] (output)

[1481] Updated educational programs are sent to the device.

[1482] Step 10:

[1483] The terminal displays the updated educational program.

[1484] (Specific action)

[1485] The client device will then display the redistributed educational program again. When the child opens the device, they will see a lesson plan for the next day, such as "40 minutes of English vocabulary, 70 minutes of math problems, and 30 minutes of reviewing new history."

[1486] (input)

[1487] Updated Educational Programs

[1488] (output)

[1489] The updated educational program will be displayed on the device.

[1490] These are the specific processing steps of this system, which makes it possible to properly manage a child's learning progress and provide an optimal learning plan.

[1491] (Application example 1)

[1492] 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."

[1493] Conventional educational support systems have struggled to efficiently track daily learning progress and provide real-time feedback. Furthermore, the lack of an optimal system for utilizing client devices such as smartphones and head-mounted displays made it difficult for students to follow an optimal learning plan. This left students with an unclear path to success at their desired educational institution.

[1494] 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.

[1495] In this invention, the server includes means for receiving basic information about a child based on input from a user, means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI, means for distributing the generated educational program to a client terminal, means for collecting test results and learning progress data, means for analyzing the collected data and adjusting the learning program, means for distributing the adjusted learning program again to the client terminal, means for visually displaying the learning progress data and test results, means for providing real-time visual feedback, and means for using a client terminal installed on a smartphone or head-mounted display. This allows users to efficiently track their learning progress and receive real-time feedback, enabling them to study effectively to pass their target educational institution according to an optimal learning plan.

[1496] "Users" refers to parents and children who use the system.

[1497] "Basic information" refers to information such as the child's age, current grades, and target university.

[1498] "Generative AI" refers to artificial intelligence that generates optimal educational programs based on user data, past successful applicant data, and deviation score information.

[1499] An "educational program" refers to a plan that outlines the learning plans and assignments a child needs to pass in order to be accepted into their desired educational institution.

[1500] "Client terminal" refers to a device that is directly operated by a user, such as a smartphone, tablet, or head-mounted display.

[1501] "Test results" refers to test scores and evaluation information taken by a child.

[1502] "Learning progress data" refers to a child's progress and achievements as they progress through their daily studies.

[1503] "Analysis" refers to the process of analyzing collected test results and learning progress data using machine learning and statistical methods.

[1504] "Feedback" refers to the improvements and advice that the AI ​​provides to children based on the analysis results.

[1505] "Visual feedback" refers to providing users with an intuitive understanding of their learning progress and results using graphs, charts, etc.

[1506] "Real-time" refers to data being processed almost instantly, with results and feedback provided without delay.

[1507] "Target university" refers to the specific educational institution that a child aspires to attend.

[1508] A "head-mounted display" refers to a display device that is worn on the user's head.

[1509] This invention is an educational support system that uses generative AI, and aims to provide an optimal learning plan based on input from users (parents and children) and to progress learning according to that plan. Specific embodiments of the invention are described below.

[1510] Server Operation

[1511] The server first receives basic information about the child (age, current grades, target university, etc.) entered by the user. Based on this information, the server uses a generation AI to reference data on past successful applicants and deviation scores to generate an initial educational program. The generated program is then distributed to the client device.

[1512] As the learning progresses, the server collects daily learning progress data and test results from the client device. This collected data is analyzed and the learning program is automatically adjusted. The adjusted program is then sent back to the client device, allowing the learning to continue.

[1513] The main software used is a generative AI model (e.g., OpenAI's API), which analyzes data and generates programs.

[1514] Client terminal operation

[1515] The client device (smartphone or head-mounted display) receives the educational program distributed from the server and provides it to the child. Specifically, the program displays daily learning plans and assignments on the device screen.

[1516] As children study and take tests, the results are sent to the server via the client device. The device also has the function of visually displaying learning progress data and test results, allowing parents to understand their child's learning status in real time. Visual feedback allows parents to intuitively understand the effectiveness of their children's learning.

[1517] User Actions

[1518] Parents and children use this system through client terminals. Parents first enter basic information about their children and then check the generated educational program. Children follow the program and enter test results through the terminal. Parents can then check the learning progress data on the terminal and provide feedback as needed.

[1519] Specific examples

[1520] For example, if the target university is University A, parents input basic information such as their child's age, current grades, and desired school, and the server generates an initial educational program based on this information. This program references data and deviation score information from students who have previously been accepted into University A to create an optimal learning plan.

[1521] On Monday morning, when a child opens their smartphone, the day's study plan (e.g., 30 minutes of English vocabulary, 60 minutes of math workbooks, and 30 minutes of history) is displayed. The child studies according to this plan, takes an English vocabulary test, and enters the results into the device. The results are sent to a server, which analyzes the data and adjusts the study program accordingly.

[1522] Specific examples of prompts include:

[1523] User information: Age 16, current grades: 85 in math, 78 in English, 90 in history, target university: University A

[1524] Generate the best study plan to get into your target university.

[1525] In the evening, parents can check their child's learning progress on the device and see visual details such as "English Vocabulary: 90 points," "Math Workbook: Completed," and "History: Insufficient understanding." Based on this, parents can adjust the next lesson plan or provide feedback.

[1526] In this way, the invention is a system that does not rely on the educational knowledge of parents, but provides optimal learning support to children, helping them to get into their target university.

[1527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1528] Step 1:

[1529] The user uses a client device to input basic information about their child. The user enters information such as age, current grades, and target university into the device's input screen, and the information is sent to the server. The input information is used as basic data for the generative AI model to generate an educational program. Examples of input data include "age: 16 years old," "math: 85 points," "English: 78 points," "history: 90 points," and "target university: University A."

[1530] Step 2:

[1531] The server generates an educational program using a generative AI model. Based on the basic information received from the user, the server references past successful applicant data and deviation score information to generate a prompt. For example, a prompt such as "User information: age 16, current grades 85 points in math, 78 points in English, 90 points in history, target university A. Please generate the optimal study plan to pass the target university entrance exam." is generated. This prompt is input into the generative AI model, which outputs an optimized educational program.

[1532] Step 3:

[1533] The generated educational program is delivered to the client terminal. The server sends the plan obtained from the generative AI model to the client terminal. For example, the generated program is a specific study plan such as "30 minutes of English vocabulary, 60 minutes of math problems, and 30 minutes of history." This program is displayed on the terminal screen, and the user can use it to proceed with their studies.

[1534] Step 4:

[1535] The user studies and inputs test results and learning progress data. The child progresses with their daily studies and takes tests, which are then entered into the client terminal. For example, data such as "English vocabulary: 90 points," "Math workbook: completed," and "History: insufficient understanding" are entered. This input data is sent to the server.

[1536] Step 5:

[1537] The server collects and analyzes test results and learning progress data. The server collects and analyzes data sent from client devices. This analysis uses machine learning algorithms and statistical methods. For example, if a student's understanding of a particular subject is insufficient, the server adjusts the learning plan to strengthen the content of that subject. The analysis results become the basis for a new learning program.

[1538] Step 6:

[1539] The adjusted learning program is then sent back to the client device. The server generates a new learning program based on the collected and analyzed data and sends it to the client device. This allows the user to receive a new learning plan in real time and progress with their studies efficiently.

[1540] Step 7:

[1541] Visually displays learning progress data and test results. The client device visually displays new programs and progress data received from the server in graphs and charts. This allows parents and children to intuitively understand learning results and progress. For example, results such as "Math: 88 points," "English: 80 points," and "History: 92 points" are displayed visually.

[1542] Step 8:

[1543] The user adjusts the next lesson plan based on visual feedback. Parents and children consider the next lesson plan based on visually displayed progress data and test results. They provide feedback as needed to improve learning. This feedback is also sent to the server and used to optimize the next lesson plan using a generative AI model.

[1544] 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.

[1545] The present invention is an educational support system that combines generative AI and an emotion engine, and aims to optimize learning programs using emotion data from users (parents and children) and provide individually tailored learning support. Specific embodiments of this system are described below.

[1546] Server Operation

[1547] The server receives basic information about the child entered by the user (age, current grades, target university, etc.) and uses this information to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores. Furthermore, it is equipped with an emotion engine that acquires and analyzes the user's emotional data to adjust the learning program more effectively.

[1548] The generated educational program is distributed to the client terminal. As users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal. Emotional data is also collected at the same time, and the learning program is adjusted based on this. The readjusted learning program is then distributed again to the client terminal.

[1549] Client terminal operation

[1550] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's learning progress data and test results, and is equipped with a function for collecting emotional data. For example, it can use facial recognition and voice analysis technology to recognize a child's emotions while they are studying in real time.

[1551] The device also has the function of sending collected learning progress data and emotional data to a server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to grasp their child's learning status comprehensively.

[1552] User Actions

[1553] Parents and children use the system through client terminals. Parents enter basic information about their children during the initial setup and review the generated educational program. The children then follow the program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[1554] Parents can also check their child's learning progress data and emotional state through the client device, allowing them to provide support to maintain a better learning environment.

[1555] Specific examples

[1556] For example, when a child aiming for University B is entered as a target, the server uses generative AI to generate an appropriate early education program. At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the learning program.

[1557] When a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While the child is studying, the device recognizes the child's facial expressions and, if it detects stress or fatigue, sends that data to a server. The server analyzes the emotional data and adjusts the study program to make it more relaxing.

[1558] In the evening, parents can check their child's learning progress and emotional state on the device, and visual information such as "English Vocabulary: 90 points," "Math Problems: Completed," and "Emotional State: Relaxed" will be displayed. Based on this, parents can provide the child with the next learning plan and feedback to support their child's learning.

[1559] In this way, by combining generative AI and an emotion engine, the present invention provides an effective educational system that maximizes children's learning efficiency and reduces stress.

[1560] The processing flow will be explained below.

[1561] Step 1:

[1562] The user (parent) uses a dedicated application to input basic information about their child (age, current grades, target university, etc.). The server receives this information and stores it in a database.

[1563] Step 2:

[1564] The server uses the stored basic information of the child to create an initial educational program using generative AI. This program is optimized by referencing data on past successful applicants and deviation scores.

[1565] Step 3:

[1566] The server uses an emotion engine to reflect the user's emotional data in the initial education program, which utilizes information on the user's stress level and concentration.

[1567] Step 4:

[1568] The generated educational program is distributed to the client terminal, which receives the program and displays it to the user (child) as a daily learning plan.

[1569] Step 5:

[1570] The user (child) progresses through daily learning according to the learning plan displayed on the client terminal. The terminal records the learning progress and collects emotional data as appropriate.

[1571] Step 6:

[1572] The client device captures emotional data using facial expression recognition and voice analysis while the child is learning, and this data is sent to the server in real time.

[1573] Step 7:

[1574] The server analyzes the received learning result data and emotional data to evaluate the child's progress. Based on the evaluation results, the generative AI generates a new educational program with necessary adjustments.

[1575] Step 8:

[1576] The server redistributes the adjusted new educational program to the client terminal, which receives it and displays it as an updated learning plan.

[1577] Step 9:

[1578] Users (parents) can visually check their child's learning progress data and emotional state in graphs and charts on a client device, allowing them to get a comprehensive understanding of their child's learning situation.

[1579] Step 10:

[1580] If necessary, the user (parent) can provide feedback from the client terminal, which is sent to the server and helps adjust the next learning program.

[1581] Step 11:

[1582] The system repeats the above steps and provides continuous learning support to ultimately help the user (child) get into their target university.

[1583] Example 2

[1584] 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."

[1585] With conventional educational support systems, it was difficult to optimize learning programs that took into account each child's emotional state, resulting in problems such as reduced learning efficiency and excessive stress.In addition, the lack of visual feedback on learning progress and emotional state made it difficult for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[1586] 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.

[1587] In this invention, the server includes means for receiving basic information about a child based on user input, means for generating an initial educational program based on past successful candidate data and standardization information using a generation AI, means for distributing the generated educational program to a client terminal, means for acquiring and analyzing the user's emotional data, means for adjusting the educational program based on the emotional data and learning progress data, and means for analyzing the collected data and distributing the adjusted educational program back to the client terminal. This enables the creation and adjustment of educational programs that take into account each child's individual emotional state, thereby improving learning efficiency and reducing stress. Furthermore, visual feedback of learning progress data, test results, and emotional state makes it easier for parents and teachers to accurately grasp a child's learning situation and provide appropriate support.

[1588] "User" refers to a parent or teacher who uses the system to assist in the education of their child.

[1589] "Child's basic information" refers to information necessary to generate and optimize a learning program, such as the child's age, current grades, and desired educational facilities.

[1590] "Generative AI" refers to artificial intelligence technology that generates educational programs based on past successful applicant data and standardization information.

[1591] "Educational program" refers to the learning plan and content constructed in line with a child's learning goals.

[1592] "Client Terminal" refers to the device used by users and children to receive and engage with educational programs.

[1593] "Emotional data" refers to data collected to understand a child's emotional state while learning, and refers to information obtained using facial recognition technology, voice analysis technology, etc.

[1594] "Learning Progress Data" means data that shows a child's learning progress and achievements, such as progress in a learning program and test results.

[1595] "Standardized information" refers to standard information necessary to optimize learning programs, such as educational evaluation criteria and entrance examination information for target educational facilities.

[1596] "Emotion engine" refers to an engine that analyzes the emotional data of users and children and adjusts learning programs more effectively.

[1597] "Visual feedback" refers to a function that visually displays learning progress data and emotional state in graphs and charts, providing it in a way that is easy for users to understand.

[1598] "Adjustment" refers to changing the content or difficulty of a learning program based on collected data.

[1599] "Delivery" refers to sending the generated educational program or the re-adjusted learning program to the client terminal.

[1600] MODE FOR CARRYING OUT THE INVENTION

[1601] The present invention is an educational support system that combines a generative AI model and an emotion engine, and aims to provide personalized learning support by optimizing learning programs using emotion data from users (parents and children). Specific embodiments of this system are described below.

[1602] Server Operation

[1603] The server receives basic information about the child (age, grades, target educational institution, etc.) entered by the user. Based on this information, the server creates an initial educational program using a generative AI model. An example of a generative AI model is an AI algorithm used to generate existing study plans. A prompt such as "Please create a study plan for target university B" is used.

[1604] The educational program is optimized by referencing data on past successful candidates and standardization information. Furthermore, the server is equipped with an emotion engine that acquires and analyzes the emotional data of users and children. This emotional data includes real-time emotion recognition results using facial recognition and voice analysis technology. This allows for more effective adjustment of the learning program.

[1605] The generated educational program is distributed to the client terminal. As the users (parents and children) progress with their daily studies, the server collects learning progress data and test results from the client terminal and readjusts the program based on this data. This readjusted learning program is also distributed again to the client terminal.

[1606] Client terminal operation

[1607] The client terminal receives the educational program distributed from the server and provides it to the user as a daily study plan. It also provides an interface for inputting the user's study progress data and test results. Furthermore, the terminal has the function of recognizing the child's emotions in real time while studying using facial recognition and voice analysis technology and collecting emotional data.

[1608] For example, if a child feels stressed or tired while studying, the device will detect this and send it as emotional data to the server. The device also has the function of sending collected learning progress data and emotional data to the server and visually displaying learning results. Progress and emotional changes are displayed in graphs and charts, allowing parents to have a comprehensive understanding of their child's learning situation.

[1609] User Actions

[1610] Users (parents and children) use the system through a client terminal. Parents enter basic information about their children during the initial setup and confirm the generated educational program. The children then follow this program as they progress through their daily studies. Learning results and emotional data are entered into the terminal and sent to the server, which optimizes the program.

[1611] For example, the target university is set to University B, and the generative AI model generates an initial program using a prompt such as "Please create a study plan for University B." At the same time, the emotion engine evaluates the child's emotional state and reflects it in the program accordingly. If the child is feeling stressed, the emotion engine will detect this and the server will readjust the study program.

[1612] Specifically, when a child opens the device on Monday morning, the day's study plan (e.g., 30 minutes for English vocabulary, 60 minutes for math workbooks) is displayed. While studying, the device recognizes the child's facial expressions, and if it detects stress or fatigue, it sends the data to a server. The server analyzes the emotional data and adjusts the study program to be more relaxing. In the evening, when a parent checks their child's study progress and emotional state on the device, information such as "English vocabulary: 90 points," "Math workbooks: completed," and "Emotional state: relaxed" is visually displayed. Based on this, parents can provide the child with the next study plan and feedback to support their child's learning.

[1613] In this way, the present invention provides an effective educational support system that maximizes children's learning efficiency and reduces stress by combining a generative AI model and an emotion engine.

[1614] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1615] Step 1:

[1616] The server receives basic information about the child (age, grades, target educational institution, etc.) input by the user.

[1617] Input: Basic information about the child entered by the user

[1618] Output: A dataset of basic information about children

[1619] Specific operation: Parents operate the client terminal to enter their child's information, which is then sent to the server and stored in the database.

[1620] Step 2:

[1621] The server generates an initial educational program using a generative AI model based on the received basic information.

[1622] Input: Dataset of basic information about children

[1623] Output: Early Education Program

[1624] Specific operation: The server inputs a prompt such as "Please create a study plan for the target university, University B" into the generative AI model, receives the study plan output by the AI, and constructs it as an initial education program.

[1625] Step 3:

[1626] The server distributes the generated educational program to the client terminal.

[1627] Enter: Early Education Programs

[1628] Output: The training program sent to the client terminal

[1629] Specific operation: The server packages the educational program and transmits it to the client terminal via the Internet.

[1630] Step 4:

[1631] The server acquires and analyzes the user's emotional data.

[1632] Input: User emotion data

[1633] Output: Analyzed emotion data and adjustment instructions

[1634] How it works: Using facial recognition and voice analysis technology, the server collects and analyzes the user's emotional state in real time, and based on the results, determines whether the learning program needs to be adjusted.

[1635] Step 5:

[1636] The terminal provides the user with a daily study plan via the client terminal, and allows the user to start studying.

[1637] Input: Delivered educational program

[1638] Output: Learning progress data

[1639] Specific operation: The educational program is displayed on the device, and the child follows it to progress through the learning process. The child's learning progress is entered into the device.

[1640] Step 6:

[1641] The device collects learning progress data and emotional data.

[1642] Input: Learning progress data, user emotion data

[1643] Output: Collected dataset

[1644] Specific operation: Sensors and cameras built into the device collect learning progress and user emotions in real time and store them as data.

[1645] Step 7:

[1646] The terminal transmits the collected data to the server.

[1647] Input: Collected dataset

[1648] Output: Data sent to the server

[1649] Specific operation: The device sends the collected learning progress data and emotion data to the server via the Internet.

[1650] Step 8:

[1651] The server readjusts the learning program based on the collected data.

[1652] Input: Collected data (learning progress data and emotion data)

[1653] Output: Retuned learning program

[1654] How it works: The server uses an analytical engine to analyze the data and inputs prompts such as "Please provide a learning plan that will help my child relax" into the generative AI model. Based on the results output by the AI, the learning program is adjusted.

[1655] Step 9:

[1656] The server distributes the re-adjusted educational program to the client terminal.

[1657] Input: Recalibrated learning program

[1658] Output: The reworked program sent to the client terminal

[1659] Specific operation: The server packages the retuned learning program and transmits it to the client terminal via the Internet.

[1660] Step 10:

[1661] Users can check their learning progress data and emotional state, and provide their next learning plan and feedback.

[1662] Input: Visually displayed data

[1663] Output: Next lesson plan and feedback

[1664] Specific operation: Parents check learning progress data and emotional state through the device and provide feedback to their children based on information such as "English vocabulary: 90 points," "Math workbook: completed," and "Emotional state: relaxed."

[1665] The above is the specific processing flow of this system.

[1666] (Application example 2)

[1667] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1668] Conventional work planning systems do not take into account the emotional data of workers, which can lead to problems such as worker stress and reduced efficiency. In particular, when workers perform monotonous work over long periods of time, they do not take appropriate breaks or review their tasks, resulting in a decline in work efficiency.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving basic information about workers based on input from a user, means for generating an initial work plan based on past production data and efficiency information using a generation AI, means for distributing the generated work plan to a client terminal, means for collecting work results and progress data, means for acquiring and analyzing emotional data about workers using facial recognition technology, means for analyzing the collected data and adjusting the work program, and means for distributing the adjusted work program again to the client terminal. This makes it possible to optimize the work program and maintain efficiency while monitoring the emotional state of workers in real time.

[1670] "User" means a person who uses the system to input basic information to manage and monitor work plans.

[1671] A "worker" is a person who actually performs work according to the work plan generated by the system.

[1672] "Generative AI" is an artificial intelligence algorithm that automatically generates initial work plans using various data.

[1673] "Past production data" is a data set that includes information on past work performance and production.

[1674] "Efficiency information" is information that includes metrics and indicators for evaluating the efficiency of work.

[1675] A "client terminal" is a device through which a worker receives a work plan and inputs work results.

[1676] A "work plan" is a program generated by generative AI that contains specific instructions and goals for workers to carry out their daily work.

[1677] "Work results" is data related to the results and progress of work actually performed by a worker.

[1678] "Progress data" is information that indicates how well work is progressing according to plan.

[1679] "Facial recognition technology" is a technology that uses a camera to recognize a worker's face and analyze specific emotions.

[1680] "Emotion data" is data that indicates the emotional state of a worker, obtained using face recognition technology.

[1681] "Analysis" refers to the process by which a system analyzes information based on collected data and derives useful results.

[1682] An "adjusted work program" is a work plan that has been optimized based on collected data and analysis results from the initial work plan.

[1683] "Distributing" refers to the act of the server sending the generated or adjusted work program to the client terminal.

[1684] This invention is a work support system that combines generative AI and facial recognition technology, and uses the worker's emotional data to optimize the work program and provide individualized work support. Specific embodiments of this system are described below.

[1685] Server Operation

[1686] The server first receives basic information about the workers (roles, target production volume, etc.) entered by the user. Next, it uses generative AI to create an initial work plan based on this information. The work plan is optimized by referencing past production data and efficiency information.

[1687] The generated work plan is distributed to the client terminal. As the worker goes about their daily work, the server collects work progress data and work results from the client terminal. It also uses facial recognition technology to obtain worker emotion data and adjusts the work program based on this. The adjusted work program is then distributed again to the client terminal.

[1688] Client terminal operation

[1689] The client terminal receives the work plan distributed from the server and provides it to the worker as a daily work plan. It also provides an interface for inputting the worker's work results and progress data, and is equipped with facial recognition technology to collect emotion data.

[1690] The device also has the function of transmitting collected work progress data and emotion data to a server and visually displaying the work results. Progress and emotion changes are displayed in graph and chart format, allowing users to grasp the overall status of the worker.

[1691] User Actions

[1692] Users use the system through a client terminal. The user enters basic information about the worker during the initial setup and confirms the generated work plan. The worker then carries out their daily work according to the work plan. Work results and emotional data are entered into the terminal and sent to the server, which optimizes the work program.

[1693] In addition, users can check work progress data and the emotional state of workers through the client terminal, which can provide support to maintain a better work environment.

[1694] Specific examples

[1695] For example, when basic information about a welding worker in a manufacturing plant is entered, the server uses generative AI to generate an appropriate initial work plan. At the same time, facial recognition technology evaluates the worker's emotional state and reflects it in the program accordingly. If the worker is feeling stressed, the system will detect this and the server will readjust the work program.

[1696] On Monday morning, when a worker opens their device, the day's work plan (e.g., 3 hours of welding, 2 hours of parts assembly) is displayed. While working, the device recognizes the worker's facial expressions, and if it detects stress or fatigue, it sends that data to the server. The server analyzes the emotional data and adjusts the work program accordingly.

[1697] Examples of prompts based on this include:

[1698] Due to the worker's high stress level, the next task was changed to lighter work and a break was provided.

[1699] In this way, by combining generative AI and facial recognition technology, the present invention provides an effective work support system that maximizes workers' work efficiency and reduces stress.

[1700] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1701] Step 1:

[1702] The user uses a terminal to input basic information about the worker (role, target production volume, etc.). The input data is sent to the server.

[1703] Input: Worker name, role, target production volume, etc.

[1704] Output: Basic information sent to the server

[1705] Step 2:

[1706] The server generates an initial work plan using a generative AI model based on the received basic information, and the generative AI model optimizes the work plan by referencing past production data and efficiency information.

[1707] Input: Basic information of workers, past production data, efficiency information

[1708] Output: Generated initial work plan

[1709] Step 3:

[1710] The server distributes the generated work plan to the terminal, which then displays the received work plan to the worker.

[1711] Input: Generated initial work plan

[1712] Output: Work plan delivered to the device

[1713] Step 4:

[1714] The worker starts working, and the terminal collects the work results and progress data, while simultaneously collecting the worker's emotional data using facial recognition technology.

[1715] Input: Work results, progress data, emotion data

[1716] Output: Collected work results, progress data, and emotion data

[1717] Step 5:

[1718] The terminal transmits the collected work results, progress data, and emotion data to the server.

[1719] Input: Collected work results, progress data, emotion data

[1720] Output: Work results, progress data, and emotion data sent to the server

[1721] Step 6:

[1722] The server analyzes the received data and readjusts the work program based on the emotion data and work progress. The generative AI model optimizes the work plan based on the new data.

[1723] Input: Work results, progress data, emotion data

[1724] Output: Reworked work program

[1725] Step 7:

[1726] The server again distributes the readjusted work program to the terminal, and the terminal displays the received readjusted work program to the worker.

[1727] Input: Reworked work program

[1728] Output: The re-adjusted working program delivered to the terminal

[1729] Step 8:

[1730] The user can check the work progress data and the worker's emotional state through the client terminal and provide support as needed.

[1731] Input: Work progress data, emotional state

[1732] Output: Feedback provided to the user based on their work progress and emotional state

[1733] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1734] 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.

[1735] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1736] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1737] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1738] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1739] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1740] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1741] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1742] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1743] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1744] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1745] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1746] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1747] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1748] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1749] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1750] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1751] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1752] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1753] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1754] The following is further disclosed regarding the above embodiment.

[1755] (Claim 1)

[1756] means for receiving basic information about the child based on input from the user;

[1757] A means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI;

[1758] means for distributing the generated educational program to a client terminal;

[1759] a means of collecting test results and learning progress data;

[1760] a means of analyzing the collected data and adjusting the learning program;

[1761] a means for delivering the adjusted learning program to the client terminal again;

[1762] A system including:

[1763] (Claim 2)

[1764] 10. The system of claim 1, wherein the generative AI further comprises means for optimizing the learning program based on a particular target educational institution.

[1765] (Claim 3)

[1766] 10. The system of claim 1, further comprising means for visually displaying the learning progress data and test results.

[1767] "Example 1"

[1768] (Claim 1)

[1769] means for receiving basic information about the child based on input from the user;

[1770] A means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI;

[1771] means for distributing the generated educational program to a client terminal;

[1772] a means for the terminal to visually display the educational program;

[1773] The means by which children can advance their learning,

[1774] a means of collecting test results and learning progress data;

[1775] a means of analyzing the collected data and adjusting the learning program;

[1776] a means for delivering the adjusted learning program to the client terminal again;

[1777] A system including:

[1778] (Claim 2)

[1779] 10. The system of claim 1, wherein the generative AI further comprises means for optimizing the learning program based on a particular target educational institution.

[1780] (Claim 3)

[1781] 10. The system of claim 1, further comprising means for visually displaying the learning progress data and test results.

[1782] "Application Example 1"

[1783] (Claim 1)

[1784] means for receiving basic information about the child based on input from the user;

[1785] A means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI;

[1786] means for distributing the generated educational program to a client terminal;

[1787] a means of collecting test results and learning progress data;

[1788] a means of analyzing the collected data and adjusting the learning program;

[1789] a means for delivering the adjusted learning program to the client terminal again;

[1790] a means for visually displaying learning progress data and test results;

[1791] a means of providing real-time visual feedback;

[1792] A system including:

[1793] (Claim 2)

[1794] 10. The system of claim 1, wherein the generative AI further comprises means for optimizing the learning program based on a particular target educational institution.

[1795] (Claim 3)

[1796] 10. The system of claim 1, further comprising means for using a client terminal installed on a smartphone or a head-mounted display.

[1797] "Example 2: Combining Emotion Engines"

[1798] (Claim 1)

[1799] means for receiving basic information about the child based on input from the user;

[1800] A means for generating an initial educational program based on past successful applicant data and standardization information using a generation AI;

[1801] means for distributing the generated educational program to a client terminal;

[1802] A means for acquiring and analyzing user emotion data;

[1803] a means for adjusting a learning program based on the emotion data and the learning progress data;

[1804] A means for analyzing the collected data and delivering an adjusted learning program to the client terminal again;

[1805] A system including:

[1806] (Claim 2)

[1807] 10. The system of claim 1, wherein the generative AI further comprises means for optimizing the learning program based on a particular target educational institution.

[1808] (Claim 3)

[1809] 10. The system of claim 1, further comprising means for visually displaying the learning progress data, test results, and emotional state.

[1810] "Application example 2 when combining emotion engines"

[1811] (Claim 1)

[1812] means for receiving basic information of a worker based on input from a user;

[1813] a means for generating an initial work plan based on historical production data and efficiency information using a generative AI;

[1814] means for distributing the generated work plan to a client terminal;

[1815] a means for collecting work results and progress data;

[1816] A means for acquiring and analyzing worker emotion data using facial recognition technology;

[1817] a means for analyzing the collected data and adjusting the work program;

[1818] means for delivering the adjusted work program to the client terminal again;

[1819] A system including:

[1820] (Claim 2)

[1821] 10. The system of claim 1, wherein the generative AI further comprises means for optimizing the work program based on specific target production goals.

[1822] (Claim 3)

[1823] The system of claim 1 , further comprising means for visually displaying the work progress data and the emotion data. [Explanation of symbols]

[1824] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving basic information about the child based on input from the user; A means for generating an initial educational program based on past successful applicant data and deviation score information using a generation AI; means for distributing the generated educational program to a client terminal; a means of collecting test results and learning progress data; a means of analyzing the collected data and adjusting the learning program; a means for delivering the adjusted learning program to the client terminal again; A system including:

2. 10. The system of claim 1, wherein the generative AI further comprises means for optimizing the learning program based on a particular target educational institution.

3. The system of claim 1 further comprising means for visually displaying the learning progress data and test results.

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