System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a grade and learning history data of a learner; means for generating a test of an appropriate level based on the grade and the learning history data; means for presenting the generated test to the learner; means for collecting answers of the learner; means for analyzing the collected answers to evaluate the learner's understanding; means for generating an optimal learning plan for the learner based on the evaluation result; and means for presenting the generated learning plan to the learner.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] Conventional individualized learning systems lack the ability to provide optimal tests according to the learner's grade level or to analyze their level of understanding in detail, making it difficult to provide an optimal learning plan for each learner. Furthermore, the lack of detailed analysis of answer times and correct / incorrect answers makes it difficult to identify learners' weaknesses, hindering effective learning support. [Means for solving the problem]

[0005] This invention provides a system including means for inputting data on a learner's grade and learning history, means for generating a test at an appropriate level based on the grade and learning history data, means for presenting the generated test to the learner, means for collecting the learner's answers, means for analyzing the collected answers and evaluating the learner's level of understanding, means for generating an optimal learning plan for the learner based on the evaluation results, and means for presenting the generated learning plan to the learner. This makes it possible to provide tests suited to each learner's level, analyze the answers in detail, identify weak points, and propose an optimal learning plan based on the results, thereby effectively resolving conventional problems.

[0006] A "learner" is a user who participates in a particular learning program and completes tests and assignments provided by that program.

[0007] "Grade" refers to the educational stage of the educational institution to which the learner currently belongs, and refers to a specific educational level such as elementary school, junior high school, high school, or university.

[0008] "Learning history data" refers to data that includes the content that a learner has learned up to that point and the results of that learning (grades, answer history, etc.).

[0009] A "test" is a set of questions used to assess a learner's understanding or knowledge, and may include multiple question formats.

[0010] "Correctness assessment" is the process of evaluating whether the answers submitted by learners to test questions are correct or incorrect.

[0011] "Level of understanding" is an indicator of how well a learner has understood the learning content, and is evaluated based on factors such as the percentage of correct answers on tests and the time it took to answer.

[0012] A "learning plan" is a combination of optimal learning content and tasks that is individually created based on the learner's level of understanding and learning history.

[0013] "Response time" is the time it takes a learner to submit an answer to a particular test question.

[0014] A "terminal" is a device (computer, tablet, smartphone, etc.) that a learner uses to enter test answers.

[0015] A "server" is a central processing unit that stores learners' learning data and performs processes such as test generation, comprehension analysis, and learning plan creation. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's grade and learning history data, generates tests at an appropriate level based on the data, and analyzes the test results to create an individual learning plan, thereby providing efficient and effective learning support.

[0038] System Overview

[0039] The system consists of the following main components:

[0040] 1. User Device

[0041] 2. Server

[0042] 1. User Device

[0043] User Device: This is the device used by the learner (e.g., computer, tablet, smartphone, etc.). The user device provides the interface for the learner to enter grade and learning history data, view generated tests, and enter answers.

[0044] Examples:

[0045] The user inputs information such as "sixth grade elementary school student" and "I'm not good at fractions in math" through the terminal.

[0046] The terminal transmits the information to the server.

[0047] 2. Server

[0048] Server: The server is a central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[0049] Examples:

[0050] The server generates "6th grade math, Japanese, science, and social studies tests," including, for example, the math question "What is 3 x 4?"

[0051] The server sends the generated test to the device.

[0052] Program processing steps

[0053] The processing of the program will be explained in natural language below.

[0054] Test Generation

[0055] Server: Obtains the learner's grade and learning history data and generates the most appropriate test based on that.

[0056] For example, if the grade level is "sixth grade," standard test questions for sixth graders will be given.

[0057] Test presentation and answer collection

[0058] Terminal: Displays the test sent from the server to the learner.

[0059] User: Enter the answer to each test question.

[0060] For example, when asked "What is 3 x 4?", the answer is "12."

[0061] Terminal: Collects learners' answers and sends them to the server.

[0062] The terminal sends the answer to the server.

[0063] Comprehension analysis

[0064] Server: Analyzes the submitted answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[0065] For example, if the answer to the question about how to read "kan" is "kan," it is judged to be correct, and if it is "ou," it is judged to be incorrect. The time required for each answer is also recorded.

[0066] Create a personalized learning plan

[0067] Server: Generates the optimal learning plan for the learner based on the results of comprehension analysis.

[0068] For example, if a student's understanding of Japanese kanji is lacking, create a plan that includes additional practice questions and reference materials.

[0069] Server: Sends the generated learning plan to the user's device.

[0070] The server transmits the plan to the terminal and displays it to the learner.

[0071] Presentation of study plan

[0072] Device: Display the sent learning plan to the learner.

[0073] User: Proceed with your learning based on the plan provided.

[0074] Specific examples

[0075] For example, if a sixth-grade student has difficulty with fraction problems, the server will generate "basic fraction problems for sixth-grade students" based on that information. It will analyze the answers and, if there are many incorrect answers, provide an individualized learning plan that includes "basic fraction calculation drills" and "videos explaining the concept of fractions." If the student lacks understanding of how to read kanji, it will add "kanji writing" and "reading practice problems" to the learning plan.

[0076] This allows learners to efficiently overcome their weaknesses and improve their overall understanding. In this way, the present invention provides a learning experience optimized for each learner, effectively resolving conventional problems.

[0077] The processing flow will be explained below.

[0078] Program processing steps

[0079] Step 1:

[0080] User: Enter grade and learning history data into the terminal.

[0081] For example, enter information such as "sixth grade elementary school student" and "not good at fractions."

[0082] Step 2:

[0083] Terminal: Sends the entered grade and learning history data to the server.

[0084] The terminal uses its sending function to send this information to the server.

[0085] Step 3:

[0086] Server: Based on the received grade and learning history data, the server uses a test generation algorithm to generate appropriate tests.

[0087] Create test questions such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢" for sixth graders in elementary school.

[0088] Step 4:

[0089] Server: Send the generated test to the terminal.

[0090] By sending the created test to the terminal, enable learners to access it.

[0091] Step 5:

[0092] Terminal: Display the received test to the user.

[0093] The terminal displays the questions on the screen to allow the user to answer.

[0094] Step 6:

[0095] User: Input an answer to the presented test question.

[0096] For example, input "3×4 is 12".

[0097] Step 7:

[0098] Terminal: Collect the learner's answers and send the data to the server.

[0099] Packetize the collected answers and send them towards the server.

[0100] Step 8:

[0101] Server: Grade the received answers and analyze the correct / incorrect judgment and response time.

[0102] For example, if the answer is "3×4 is 12", record it as a correct answer and measure the time taken for the answer.

[0103] Step 9:

[0104] Server: Identify areas of high error and delay and assess learner comprehension.

[0105] For example, if a student gives the wrong answer "ou" to the question about how to read "kanji" many times, this is judged to be a lack of understanding.

[0106] Step 10:

[0107] Server: Generates an individual learning plan based on the results of comprehension analysis.

[0108] For example, if a child lacks understanding of kanji, a plan can be created that recommends the use of "kanji reading drills" or "kanji learning apps."

[0109] Step 11:

[0110] Server: Sends the generated learning plan to the device.

[0111] Send the created plan to the device so that learners can access it.

[0112] Step 12:

[0113] Device: Display the received learning plan to the user.

[0114] The device displays the learning plan on the screen and guides the user through the next steps.

[0115] Step 13:

[0116] User: Proceed with your studies based on the provided learning plan.

[0117] For example, students can study by solving the added kanji problems and using the provided learning resources.

[0118] Through this series of steps, the system provides learners with an efficient and effective learning experience.

[0119] Example 1

[0120] 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."

[0121] Conventional learning support systems have the problem of being unable to easily generate and present test questions and learning plans that correspond to each learner's level of understanding and learning history. This makes it difficult to provide effective learning guidance that is tailored to each learner's weaknesses and learning pace. Furthermore, the inability to provide appropriate feedback quickly makes it difficult to maintain learners' motivation.

[0122] 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.

[0123] In this invention, the server includes a means for inputting the learner's grade and learning history data, a means for generating a test at an appropriate level based on the grade and learning history data, and a means for customizing the test and learning plan using a generative AI model, thereby enabling the generation and presentation of optimal tests and learning plans that meet the individual needs of each learner.

[0124] "Grade" is data that indicates the stage of the educational curriculum to which a learner belongs.

[0125] "Learning history data" refers to data that indicates what a learner has learned so far, their grades, level of understanding, and their learning progress.

[0126] The "test generation means" is a means having the function of creating a test at an appropriate level for a learner based on the input grade and learning history data.

[0127] A "generative AI model" is a technology that uses artificial intelligence to generate and customize optimal test questions and study plans based on a learner's specific learning data.

[0128] The "test presentation means" is a means having a function for displaying the generated test to the learner.

[0129] The "answer collection means" is a means having a function for collecting answers entered by learners to test questions.

[0130] The "means for assessing comprehension" is a means that has the function of assessing the learner's level of comprehension by determining whether the answer is correct or incorrect and the time required to answer based on the answers collected from the learner.

[0131] The "learning plan generation means" is a means having the function of creating an optimal learning plan for each learner based on the results of the comprehension assessment.

[0132] The "study plan presentation means" is a means having a function for displaying the generated study plan to the learner.

[0133] This invention is a system that provides an optimized learning experience for each learner. The system's main hardware components are a user terminal and a server, and it uses a generative AI model to generate test questions and study plans based on the learner's grade and learning history data.

[0134] 1. User Device

[0135] A user terminal is a device used by a learner (for example, a computer, tablet, or smartphone). The user terminal provides an interface for the learner to input their grade and learning history data, view the generated test, and enter their answers. Specifically, the learner inputs information such as "sixth grade of elementary school" and "I'm not good at fractions in math" through the terminal, and the terminal sends this information to the server.

[0136] 2. Server

[0137] The server is a central component that processes data. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device. Specifically, the server generates "mathematics, Japanese, science, and social studies tests for sixth graders," and creates test questions that include, for example, the math question "What is 3 x 4?" The server then sends the generated tests to the device.

[0138] Specific examples of processing

[0139] For example, a learner might use a prompt such as, "I'm in sixth grade and I'm particularly bad at fraction problems. Based on this information, please generate fraction-related test questions." The user enters this information into their device, which then sends it to the server. The server uses a generative AI model to generate a test that includes fraction questions based on this information. For example, it creates a question like, "What is 3 / 5 + 2 / 3?" The generated test is sent to the user's device, and the user answers it.

[0140] The user's device collects the entered answers and sends them back to the server. The server analyzes the answers, determines whether they were correct and analyzes the time it took to answer, and then generates an individual study plan. For example, if there are few correct answers, a study plan including "basic drills for fraction calculations" is generated. The generated study plan is sent to the user's device, and the user proceeds with their studies based on it.

[0141] This makes it possible to provide optimal learning support tailored to each individual learner's level of understanding.The system aims to efficiently overcome learners' weaknesses and promote overall learning progress.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The user inputs grade and learning history data through the terminal.

[0145] Specific operation: The user enters the information "sixth grade of elementary school" and "I'm not good at fractions in math" into the input form on the device.

[0146] Input: Grade information, learning history data.

[0147] Output: Entered grade information and learning history data.

[0148] Step 2:

[0149] The terminal transmits the input data to the server.

[0150] Specific operation: The device sends information such as "sixth grade of elementary school" and "not good at fractions" to the server.

[0151] Input: Grade information, learning history data.

[0152] Output: Grade information and learning history data sent to the server.

[0153] Step 3:

[0154] The server generates appropriate tests based on the received grade and learning history data, using a generative AI model to identify questions.

[0155] Specific operation: The server generates "basic fraction problems for sixth graders," for example, "What is 3 / 5 + 2 / 3?"

[0156] Input: Grade information and learning history data received by the server.

[0157] Output: The generated test questions.

[0158] Step 4:

[0159] The server sends the generated test to the device.

[0160] Specific operation: The server sends the generated fraction problem to the terminal.

[0161] Input: The generated test questions.

[0162] Output: The test question sent to the device.

[0163] Step 5:

[0164] The terminal displays the received test to the user, who then inputs answers to each test question.

[0165] Specific operation: The user enters the answer "15 / 31" in response to the question "What is 3 / 5 + 2 / 3?" displayed on the terminal.

[0166] Input: Test questions displayed on the terminal, user answers.

[0167] Output: The user's answer typed into the terminal.

[0168] Step 6:

[0169] The terminal sends the answer entered by the user to the server.

[0170] Specific operation: The device sends the answer "15 / 31" to the server.

[0171] Input: The user's answer as entered into the terminal.

[0172] Output: The user's answer sent to the server.

[0173] Step 7:

[0174] The server analyzes the received answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[0175] Specific operation: The server confirms that the correct answer to "3 / 5 + 2 / 3" is "15 / 31", determines that the answer is correct, and records the time it took to answer.

[0176] Input: The user's answer sent to the server.

[0177] Output: Learner's comprehension assessment results (correct / incorrect, answer time).

[0178] Step 8:

[0179] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment, and customizes the plan using a generative AI model.

[0180] Specific operation: If the basic fraction questions are all answered correctly, the server creates a plan including applied fraction questions. If there are many incorrect answers, the server creates a plan including "basic fraction calculation drills" and "videos explaining the concept of fractions."

[0181] Input: Learner comprehension assessment results.

[0182] Output: The generated learning plan.

[0183] Step 9:

[0184] The server transmits the generated study plan to the terminal.

[0185] Specific operation: The server sends a learning plan including "Basic drills for fraction calculations" and "Videos explaining the concept of fractions" to the device.

[0186] Input: The generated learning plan.

[0187] Output: The study plan sent to the device.

[0188] Step 10:

[0189] The terminal displays the received learning plan to the user.

[0190] Specific operation: The device displays to the user "Basic fraction calculation drills" and "Video explaining the concept of fractions."

[0191] Input: The study plan sent to your device.

[0192] Output: The learning plan displayed to the user.

[0193] Step 11:

[0194] The user proceeds with their studies based on the presented study plan.

[0195] Specific operations: The user solves the displayed "Basic fraction calculation drills" and watches the "Video explaining the concept of fractions."

[0196] Input: The learning plan displayed to the user.

[0197] Output: User's learning progress.

[0198] (Application example 1)

[0199] 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."

[0200] Conventional learning support systems have the problem of being difficult to individually optimize, fully reflecting each learner's learning stage and weaknesses. This has prevented them from providing efficient and effective learning support. It has also been difficult to perform a detailed analysis of a learner's level of understanding and provide an appropriate learning plan based on that analysis.

[0201] 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.

[0202] In this invention, the server includes means for inputting the learner's educational stage and learning history data, means for generating assessment questions of appropriate difficulty based on the said educational stage and learning history data, means for collecting the learner's answers, analyzing the collected answers and evaluating the learner's level of understanding, means for generating and presenting an optimal learning plan for the learner, means for managing progress, and means for providing individual educational materials. This makes it possible to provide an individual learning plan optimized for the characteristics of each learner, thereby realizing efficient and effective learning support.

[0203] "Learner" refers to an individual in a position to receive education.

[0204] "Educational stage" refers to the grade or learning progression to which a learner is currently assigned.

[0205] "Learning history data" refers to information about a learner's past learning activities and grades.

[0206] "Assessment questions" refer to test questions created to measure a learner's level of understanding and ability.

[0207] "Answer collection means" refers to a function that collects answers provided by learners to assessment questions.

[0208] "Means for assessing comprehension" refers to a function that determines the level of comprehension based on the answers provided by the learner.

[0209] A "learning plan" refers to a plan created to efficiently advance future learning based on the learner's level of understanding and challenges.

[0210] "Progress management means" refers to the function of monitoring and managing a learner's learning situation and progress.

[0211] "Educational materials" refers to teaching materials and reference materials provided to support learners' learning activities.

[0212] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to process data and generate new information or suggestions.

[0213] A "prompt" refers to text that contains instructions or questions to be input into a generative AI model.

[0214] This invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's educational level and learning history data, generates assessment questions of appropriate difficulty based on that data, and generates an individual learning plan by collecting and analyzing the learner's answers. Finally, the system presents the generated learning plan to the learner and manages their progress, thereby providing efficient and effective learning support.

[0215] System Overview

[0216] The system consists of the following main components:

[0217] 1. User Device

[0218] 2. Server

[0219] 1. User Device

[0220] User terminal: A device used by learners (computer, tablet, smartphone, etc.). User terminals provide an interface for learners to input educational stage and learning history data, display generated assessment questions, and enter answers. They also have functions for presenting learning plans and managing progress.

[0221] Examples:

[0222] The learner enters information such as "second year junior high school student" and "not good at mathematical equations" through the user terminal.

[0223] The user terminal transmits the information to the server.

[0224] 2. Server

[0225] Server: The server is a central data processing component. It generates assessment questions based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[0226] Examples:

[0227] The server generates "assessment questions about mathematical equations for second-year junior high school students" and sends them to the user's terminal.

[0228] A learner answers the question "Solve the x in 2x + 3 = 7" with "x = 2."

[0229] Program processing explanation

[0230] User device:

[0231] The user terminal collects the learner's educational stage and learning history data through the input interface and transmits the data to the server. After the learner answers the assessment questions, the answer data is also transmitted to the server.

[0232] server:

[0233] The server generates appropriate assessment questions based on the received data. This is done using a generative AI model. It also analyzes the learner's answers and evaluates their level of understanding. The analysis results are reflected in the learner's learning plan via prompts. The following are example prompts:

[0234] Prompt Sentence Examples

[0235] Based on the data entered, such as "second-year junior high school student" and "not good at mathematical equations," the server generates the following assessment questions.

[0236] - "Solve the x in 2x + 3 = 7"

[0237] Examples:

[0238] The server generates "assessment questions about mathematical equations for second-year junior high school students."

[0239] The user device presents the learner with the problem "Solve the x in 2x + 3 = 7."

[0240] The learner answers "x = 2", and the server receives and evaluates the answer.

[0241] The server also generates an individualized learning plan based on the results of the comprehension assessment and transmits it to the user terminal. The learning plan may include, for example, "drills to strengthen the basics of equations" and "videos explaining equations."

[0242] This allows learners to receive individually optimized learning plans, enabling them to manage their own progress and study effectively.

[0243] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0244] Step 1:

[0245] The user inputs educational stage and learning history data into the user terminal. Examples of input data include "second year junior high school student" and "poor at mathematical equations." This input data is sent to the server by the user terminal. Because the input data includes specific areas of weakness and past grades, the server treats it as basic data for generating individual assessment questions.

[0246] Step 2:

[0247] The server generates appropriate assessment questions based on the received data. Using the generative AI model, it generates assessment questions for the prompts "junior high school second year student" and "not good at math equations." For example, a question like "solve the x in 2x + 3 = 7" is generated. This generated assessment question is sent from the server to the user's device.

[0248] Step 3:

[0249] The user terminal presents the generated assessment questions to the learner. The assessment questions are displayed on the screen of the user terminal, and an interface is provided that allows the learner to answer them.

[0250] Step 4:

[0251] The user inputs an answer to the presented evaluation question. For example, to the question "Solve x in 2x + 3 = 7," the user answers "x = 2." This answer is sent to the server by the user terminal.

[0252] Step 5:

[0253] The server analyzes the received answers. Using the comprehension assessment means, it analyzes whether the answers are correct and the time it took to answer them. For example, if the answer "x = 2" is correct, it is recorded as the correct answer, and the time it took to answer is also recorded. The results of this analysis are used to assess the learner's comprehension.

[0254] Step 6:

[0255] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment. Using a generative AI model, the learning plan is generated based on the prompt. For example, a plan including "Equation Basics Reinforcement Drills" and "Equation Explanation Videos" may be generated. This plan is then sent from the server to the user's device.

[0256] Step 7:

[0257] The user device presents the generated learning plan to the learner. The user device has the function of displaying specific learning activities and reference materials, allowing the learner to check their own progress as they study.

[0258] Step 8:

[0259] The user progresses through their studies based on the presented learning plan. The user's device records the learner's learning activities and sends progress information to the server, allowing the server to continuously monitor the learner's situation and update the learning plan as necessary.

[0260] Through the above processing steps, the learner receives an optimal learning plan based on his / her educational stage and learning history, and can proceed with his / her studies efficiently and effectively.

[0261] 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.

[0262] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, it aims to improve learning efficiency by combining it with an emotion engine that recognizes the user's emotions.

[0263] System Overview

[0264] The system consists of the following main components:

[0265] 1. User Device

[0266] 2. Server

[0267] 3. Emotion Engine

[0268] 1. User Device

[0269] User terminal: A device used by a learner (e.g., computer, tablet, smartphone, etc.). It provides an interface for learners to input grade and learning history data, view generated tests, and enter answers.

[0270] Examples:

[0271] The user uses the terminal to input information such as "sixth grade elementary school student" and "not good at fractions," and this information is sent to the server via the terminal.

[0272] 2. Server

[0273] Server: A central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes comprehension, and creates a learning plan based on the results and provides it to the user.

[0274] Examples:

[0275] The server generates a test for sixth graders in mathematics, Japanese, science, and social studies, and sends it to the device. For example, the test includes a math question such as "What is 3 x 4?"

[0276] 3. Emotion Engine

[0277] Emotion engine: This component uses a camera and microphone to recognize the learner's emotions. The emotion engine analyzes the learner's facial expressions and tone of voice and collects this data to understand their emotional state, such as whether they are feeling stressed or struggling to understand.

[0278] Examples:

[0279] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[0280] Program processing

[0281] The program processing will be explained in detail below in natural language.

[0282] Test Generation

[0283] Server: Generates optimal tests based on grade and learning history data, including multiple question formats.

[0284] For example, based on the input data of "sixth grade elementary school students," create a test that includes the basic math question "What is 3 x 4?"

[0285] Test presentation and answer collection

[0286] Terminal: Presents the generated test to the learner and allows them to enter their answers.

[0287] User: Enters answers to the test questions provided.

[0288] For example, answer "3 x 4 is 12."

[0289] Terminal: Collects the learner's answers and sends the data to the server.

[0290] Data collected along with the answers also includes time data.

[0291] Comprehension analysis and sentiment data collection

[0292] Server: Scores the received answers, analyzes whether they are correct or not, and analyzes the answering time. It also analyzes the emotion data collected from the emotion engine.

[0293] For example, the system associates correct and incorrect answers to the reading of the answer "kan" (Chinese character) with emotional data such as "anxiety" and "concentration" while answering.

[0294] Create a personalized learning plan

[0295] Server: Generates an optimal learning plan for the learner based on both comprehension and emotion data.

[0296] For example, if comprehension is low and the emotion engine identifies "anxiety," the plan might include a break to relax or instructions to start with an easy problem.

[0297] Presentation of study plan

[0298] Terminal: Presents the generated learning plan to the learner.

[0299] User: Proceed with your studies based on the provided learning plan.

[0300] For example, solve the added kanji practice problems and use the provided resources to advance your learning.

[0301] Specific examples

[0302] For example, if a sixth-grade student inputs that they have difficulty with fraction problems, the server uses that information to generate a test that includes basic fraction questions. After the student answers, the emotion engine recognizes states of "anxiety" or "lack of concentration" and reflects these in the analysis. The resulting study plan includes materials to gradually teach the basics of fractions and a stress management plan that includes short breaks.

[0303] In this way, the present invention provides a personalized learning experience that also takes emotional state into account, making it a more effective system for solving traditional problems.

[0304] The processing flow will be explained below.

[0305] Program processing steps

[0306] The processing steps of a system including an emotion engine are described in detail below.

[0307] Step 1:

[0308] User: Input the grade and learning history data into the terminal.

[0309] For example, input information such as "Sixth grade in primary school" and "Weak in fractions".

[0310] Step 2:

[0311] Terminal: Send the input grade and learning history data to the server.

[0312] The terminal uses the transmission function to send this information to the server.

[0313] Step 3:

[0314] Server: Based on the received grade and learning history data, use the test generation algorithm to generate appropriate tests.

[0315] Create test questions such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢" for sixth graders in primary school.

[0316] Step 4:

[0317] Server: Send the generated test to the terminal.

[0318] By sending the created test to the terminal, make it accessible to the learner.

[0319] Step 5:

[0320] Terminal: Display the received test to the user.

[0321] The terminal displays the questions on the screen to enable the user to answer.

[0322] Step ⑥:

[0323] User: Enters answers to the test questions provided.

[0324] For example, enter "3 x 4 is 12".

[0325] Step 7:

[0326] Terminal: Collects the learner's answers and sends the data to the server.

[0327] The collected answers are packetized and sent to the server.

[0328] Step 8:

[0329] Server: Scores the received answers and analyzes the correctness and time required to answer.

[0330] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[0331] Step 9:

[0332] Emotion Engine: Uses a camera and microphone to analyze the user's facial expressions and tone of voice during testing.

[0333] For example, if a person's facial expression is stern and their voice is low-pitched, this will be perceived as "anxiety."

[0334] Step 10:

[0335] Server: Based on the emotional data obtained from the emotion engine, the learner's emotional state is recorded and used to assess comprehension.

[0336] The results are compared with the scoring results, and for example, the times when the learner felt stressed are recorded.

[0337] Step 11:

[0338] Server: Generates personalized learning plans based on comprehension and emotion data.

[0339] For example, if the emotional data indicates "anxiety," the study plan could include breaks to relax and simple stress-reducing exercises.

[0340] Step 12:

[0341] Server: Sends the generated learning plan to the device.

[0342] Send the created plan to the device so that learners can access it.

[0343] Step 13:

[0344] Device: Display the received learning plan to the user.

[0345] The device displays the learning plan on the screen and guides the user through the next steps.

[0346] Step 14:

[0347] User: Proceed with your studies based on the provided learning plan.

[0348] For example, the system can solve additional kanji practice problems, utilize presented resources to advance learning, and even implement instructions based on emotion data, such as taking breaks.

[0349] Through this series of steps, the system not only provides learners with an efficient and effective learning experience, but also provides them with a personalized learning plan that takes their emotional state into account.

[0350] Example 2

[0351] 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."

[0352] In today's education system, it is difficult to provide an individually optimized learning experience by analyzing each learner's level of understanding and emotional state in detail. In particular, there is still a lack of technology that can recognize a learner's emotional state in real time and adjust the learning plan accordingly. As a result, learners may feel stressed or be left with a low level of understanding.

[0353] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the learner's grade and learning history data; means for generating a test at an appropriate level based on the grade and learning history data; means for presenting the generated test to the learner and collecting the learner's answers; means for evaluating the learner's level of understanding by analyzing the collected answers and answer times; means for recognizing the learner's emotional data and analyzing the emotional data; means for generating an optimal learning plan based on the evaluation results and the emotional data analysis results; and means for presenting the generated learning plan to the learner. This enables an individually optimized learning experience that simultaneously takes into account the learner's level of understanding and emotional state.

[0354] A "learner" is an individual receiving education, specifically someone who uses the system to carry out learning activities.

[0355] "Grade" refers to the stage at which a learner progresses in the educational curriculum.

[0356] "Learning history data" refers to data that records the learning content, grades, and progress of a learner to date.

[0357] A "server" is a part of a computer system that performs central data processing, and is a device that accepts requests from multiple clients and generates appropriate responses.

[0358] A "test" is a set of questions or tasks given to a learner to assess their understanding or knowledge.

[0359] The "test generation means" is a means having the function of automatically creating a test at an appropriate level based on the learner's grade and learning history data.

[0360] "Answer" is the answer or solution provided by the learner to the test.

[0361] The "answer collection means" is a means that has the function of collecting answers given by learners to tests and recording them as data.

[0362] "Response time" is the time taken to respond to one question.

[0363] The "means for assessing comprehension" is a means that has the function of analyzing the collected answers and answer times to assess the learner's level of comprehension.

[0364] "Emotional data" is data that indicates the emotional state of the learner during learning, extracted from the learner's facial expressions, tone of voice, etc.

[0365] "Emotion data recognition means" refers to a means that has the function of recognizing the learner's emotional state through a camera or microphone, and collecting and analyzing that data.

[0366] An "optimal learning plan" is a set of learning strategies and tasks that are most effective for a learner, created based on the learner's comprehension and emotional data.

[0367] The "study plan generation means" is a means having the function of automatically creating an optimal study plan based on the evaluation results and the emotion data analysis results.

[0368] The "study plan presentation means" is a means having the function of presenting the generated study plan to the learner.

[0369] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, the system aims to improve learning efficiency by combining an emotion engine that recognizes the learner's emotions.

[0370] The system consists of the following main components:

[0371] 1. User Device

[0372] 2. Server

[0373] 3. Emotion Engine

[0374] 1. User Device

[0375] A user terminal is a device used by a learner (e.g., a computer, tablet, smartphone, etc.). The terminal provides an interface for inputting the learner's grade and learning history data, and has the functionality to display generated tests and input answers.

[0376] Examples:

[0377] The user uses a terminal to input information such as "sixth grade elementary school student" and "I'm not good at fractions." This information is then sent to the server via the terminal.

[0378] 2. Server

[0379] The server is a central data processing component. It generates tests based on learner data and sends them to the user's device. It also collects answers and analyzes comprehension, and creates and provides a learning plan based on the results.

[0380] Examples:

[0381] The server generates a "sixth grade test" and sends it to the device. For example, the math question includes "What is 3 x 4?"

[0382] 3. Emotion Engine

[0383] The emotion engine is a component that uses a camera and microphone to recognize the learner's emotions. By analyzing the learner's facial expressions and tone of voice and collecting this data, the emotion engine can grasp the learner's emotional state, such as whether they are feeling stressed or struggling to understand.

[0384] Examples:

[0385] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[0386] Program processing

[0387] Next, the processing of the program is explained in detail in natural language.

[0388] Entering User Data

[0389] The terminal provides an interface for the learner to input their grade and learning history data. The learner inputs their grade and weak subjects on the terminal.

[0390] Sending data

[0391] The terminal transmits the learner's input data to the server.

[0392] Test Generation

[0393] The server generates appropriate tests based on the learner's data. The tests include multiple question formats to measure the learner's level of understanding.

[0394] Examples:

[0395] The server selects math problems centered around fractions for sixth grade students who feel they are "not good at fractions," and generates tests that include questions such as "3 / 4 + 1 / 2 = ?"

[0396] Test Delivery

[0397] The server transmits the generated test to the user terminal.

[0398] Presenting the test and collecting answers

[0399] The terminal presents the received test to the learner, who then answers each question on the terminal.

[0400] Submit your answer

[0401] The terminal transmits the learner's answer data to the server.

[0402] Answer analysis

[0403] The server scores the received answers, analyzes the accuracy and time of the answers, and also analyzes the emotion data collected by the emotion engine.

[0404] Examples:

[0405] The server scores the received answer "5 / 4," determines whether it is correct, and analyzes the time it took to answer. At the same time, it recognizes from the emotional data that the learner felt "anxiety" while answering.

[0406] Generate a personalized learning plan

[0407] The server generates an optimal learning plan for the learner based on the answer results and the emotion data analysis results.

[0408] Examples:

[0409] The server generates a study plan that includes videos explaining basic fraction problems for those with low comprehension, links to explanatory articles, and breaks for relaxation.

[0410] Delivery and presentation of learning plans

[0411] The server transmits the generated study plan to the user terminal, and the terminal presents it to the learner.

[0412] Prompt Sentence Examples

[0413] Use the following as a specific prompt:

[0414] "Please generate a test for a sixth-grader who has difficulty with fractions. Also, please suggest an appropriate learning plan based on the emotional data of the answers."

[0415] These prompts allow the generative AI model to generate tests and corresponding personalized study plans focused on specific grade levels and subjects.

[0416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0417] Step 1:

[0418] The user uses the device to input data such as grade level and learning history. The input information is "sixth grade" and "not good at fractions." The device prepares this data in JSON format and prepares to send it to the server.

[0419] Step 2:

[0420] The terminal sends the grade and learning history data entered by the user to the server. Specifically, when the send button is clicked, the terminal includes the information in the body of an HTTP request and posts it to the server.

[0421] Input: Grade and learning history data (e.g., "6th grade" or "I'm not good at fractions")

[0422] Output: Data sent to the server (JSON format)

[0423] Step 3:

[0424] The server generates appropriate tests based on the received learner data. The server retrieves questions from a database and selects questions according to grade and subject.

[0425] Examples:

[0426] The server generates tests for sixth graders who have difficulty with fractions, including questions such as "3 / 4 + 1 / 2 = ?"

[0427] Input: Grade and learning history data

[0428] Data processing: Extraction of relevant problems from the database

[0429] Output: Generated test data

[0430] Step 4:

[0431] The server sends the generated test to the user's device. The test data is formatted in JSON format and sent as an HTTP response.

[0432] Input: Generated test data

[0433] Output: Test transmission to user device (JSON format)

[0434] Step 5:

[0435] The device presents the received test to the learner. The device has the function of analyzing the test data and displaying the questions on the user interface.

[0436] Input: Test data received from the server

[0437] Output: Display of test questions

[0438] Step 6:

[0439] The user enters answers to the test displayed on the terminal, for example, "3 / 4 + 1 / 2 = 5 / 4."

[0440] Input: Test questions and user answers

[0441] Output: Learner's answer data

[0442] Step 7:

[0443] The terminal sends the answer data entered by the learner to the server, including the answer content and the time it took to answer.

[0444] Input: Learner's answer data

[0445] Output: Send the learner's answer data to the server

[0446] Step 8:

[0447] The server analyzes the received answer data and answer time. It judges whether the answer was correct or incorrect, and evaluates the accuracy of the answer and the time required. It also adds emotional data collected by the emotion engine to the analysis.

[0448] Examples:

[0449] The server scores the answer "5 / 4" and determines whether it is correct. It also recognizes from the emotional data that the learner felt "anxiety" while answering.

[0450] Input: Answer data and emotion data

[0451] Data processing: Correct / incorrect judgment and emotional data analysis

[0452] Output: Analysis results (comprehension assessment and emotional state)

[0453] Step 9:

[0454] The server generates an optimal learning plan for each learner based on the answers and the results of the emotion data analysis. If the learner's level of understanding is deemed low, the server creates a plan that includes supplementary materials, basic questions, and breaks for relaxation.

[0455] Input: Analysis results (comprehension assessment and emotional state)

[0456] Data processing: generating learning plans

[0457] Output: Generated lesson plan

[0458] Step 10:

[0459] The server sends the generated learning plan to the user's device. The learning plan is also formatted in JSON format and sent to the user's device as an HTTP response.

[0460] Input: Generated lesson plan

[0461] Output: Study plan sent to user device (JSON format)

[0462] Step 11:

[0463] The terminal presents the received learning plan to the learner. The terminal analyzes the learning plan and displays it on the user interface. The learner then proceeds with the next learning activity based on the presented plan.

[0464] Input: Study plan received from the server

[0465] Output: Display of learning plan

[0466] The above are the specific processing steps and flow of the entire system.

[0467] (Application example 2)

[0468] 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."

[0469] Conventional learning and factory training systems have had difficulty providing optimal plans that fully consider the learning ability and operational proficiency of individual learners and operators. Furthermore, they were unable to adjust training or learning to take into account the user's emotional state. This resulted in problems such as reduced learning and operational efficiency and increased stress and anxiety.

[0470] 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 generating an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, means for presenting the generated training protocol and collecting operation data, means for collecting emotional data during operation and adjusting the training plan, means for analyzing the level of understanding and operation data and generating an appropriate learning plan, and means for presenting the generated learning plan. This makes it possible to provide optimal learning plans and training plans for individual learners and operators, thereby improving learning efficiency and operation efficiency.

[0471] "Learner" refers to students and educational subjects who are the main participants in learning activities.

[0472] "Grade" is an indicator of the progress of the educational curriculum to which a learner belongs.

[0473] "Learning history data" refers to data that records the learning activities that a learner has undertaken and the results of those activities.

[0474] A "test" is an exam or question set used to assess a learner's understanding or knowledge.

[0475] A "training protocol" is a plan that outlines the procedures and content of the training that operators and learners should carry out.

[0476] "Proficiency" is an indicator of the operator's or learner's level of proficiency in skills and knowledge.

[0477] "Operation history data" refers to data that records the operation activities that an operator has performed up to now and the results of those activities.

[0478] "Emotional data" is data used to collect and analyze the emotional state of learners and operators.

[0479] A "training plan" is a specific learning or training plan established based on the proficiency and emotional state of an operator or learner.

[0480] "Operation data" refers to data that records the details of operations performed by an operator.

[0481] "Analysis results" are the results of analysis conducted based on collected data.

[0482] A "server" is a computer system that centrally processes data and performs various calculations and data generation.

[0483] The present invention is a system that generates an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, and provides an individually customized training plan based on the results. Furthermore, it aims to recognize the emotions of the learner or operator using an emotion engine and adjust the training plan accordingly.

[0484] System Overview

[0485] The system consists of the following main components:

[0486] 1. User Device

[0487] 2. Server

[0488] 3. Emotion Engine

[0489] 1. User Device

[0490] User terminal: A device (e.g., tablet, smartphone) used by an operator or learner. The user terminal provides an interface for inputting proficiency and operation history data, displaying generated training protocols, and collecting operation and emotion data.

[0491] 2. Server

[0492] Server: A central data processing component. It generates training protocols based on the proficiency and operation history data of operators and learners, and sends them to the user's device. It also collects operation and emotion data, and adjusts and generates training plans based on this data, which it then provides to the user.

[0493] 3. Emotion Engine

[0494] Emotion Engine: A component with the ability to recognize the emotions of operators and learners using a camera and microphone. The emotion engine analyzes facial expressions and tone of voice to understand the emotional state of operators and learners.

[0495] Program processing

[0496] Training protocol generation

[0497] The server generates an optimal training protocol based on the operator's or learner's proficiency and past operation history data, which includes multiple operation procedures and training items.

[0498] For example, based on information such as "beginner level" or "difficulty with basic robot operations," a training protocol can be created that progresses from basic arm operations.

[0499] Training presentation and operation data collection

[0500] The user terminal presents the generated training protocol to the operator and collects operation data and emotion data.

[0501] The operator performs the operation according to the protocol, and emotional data (e.g., facial expressions and tone of voice collected by a camera or microphone) is also collected.

[0502] Analysis of operational and sentiment data

[0503] The server scores and analyzes the received operation data, and simultaneously analyzes the emotion data collected from the emotion engine.

[0504] For example, if a particular operation is frequently subject to operational errors and "anxiety" is recognized in the emotional data, that operation will be included as a focus in the next training session.

[0505] Generate personalized training plans

[0506] The server generates optimal training plans for operators and learners based on operation data and emotion data.

[0507] For example, if the emotional data indicates "anxiety," it will create a training plan with increased step-by-step guidance and short breaks to relax.

[0508] Training plan presentation

[0509] The user terminal presents the generated training plan to the operator, allowing them to continue learning and training.

[0510] The operator continues to operate based on the presented training plan, and the results and emotional data are sent back to the server.

[0511] Hardware and software used

[0512] The hardware used includes a smartphone or tablet (user device) with a camera and microphone (for the emotion engine). The software uses a training program generation algorithm for the server and emotion recognition AI (e.g., Microsoft® Azure® Emotion API).

[0513] Examples and prompts

[0514] For example, here is a specific example of new operator A learning the basics of operating a factory robot:

[0515] Person A uses the control terminal to input "new employee level" and "not good at operating the arm."

[0516] The server generates a training protocol including basic arm operations and sends it to the terminal.

[0517] As Mr. A continues his training, his emotion recognition data is evaluated as "anxiety."

[0518] Based on the analysis, the server will provide more tips for the next training session and add breaks to improve concentration.

[0519] Example prompts to input to the generative AI model

[0520] Enter the new operator's skill level and past operating history.

[0521] Create a specific training protocol based on information such as "rookie level" or "poor arm operation", and generate an individual training plan taking into account emotional data during operation.

[0522] In this way, the present invention provides a personalized, optimized training experience that also takes into account emotional state, improving the operator's learning and operating efficiency.

[0523] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0524] Step 1:

[0525] The user uses a user terminal to input their own proficiency level and past operation history data. The input data includes information such as "rookie level" or "poor at arm operation." All of this data is sent to the server.

[0526] Input: User's skill level and operation history data

[0527] Output: Skill level and operation history data sent to the server

[0528] Step 2:

[0529] The server generates an optimal training protocol based on the received proficiency and operation history data. Using a generative AI model, it creates a specific training protocol while conducting comparative analysis with past training data.

[0530] Input: Skill level and operation history data

[0531] Output: The generated training protocol

[0532] Step 3:

[0533] The server transmits the generated training protocol to the user terminal, which presents the training protocol to the operator.

[0534] Input: Generated training protocol

[0535] Output: The training protocol sent to the user's device.

[0536] Step 4:

[0537] The user performs operations according to the training protocol displayed on the user device. During the operations, the user device collects emotional data (facial expressions, tone of voice, etc.) using a camera and microphone.

[0538] Input: User actions and emotion data

[0539] Output: Collected operation data and emotion data

[0540] Step 5:

[0541] The user terminal transmits the collected operation data and emotion data to the server, which can then perform detailed evaluation and analysis.

[0542] Input: Collected operation data and emotion data

[0543] Output: Operation data and emotion data sent to the server

[0544] Step 6:

[0545] The server analyzes the received operation data, judges the success or failure and analyzes the operation efficiency, and simultaneously analyzes the emotion data to grasp the user's emotional state during operation.

[0546] Input: Operation data and emotion data

[0547] Output: Success / failure judgment, analysis of operational efficiency, and analysis of emotional state

[0548] Step 7:

[0549] The server generates an individual training plan based on the evaluation of the user's operation data and the analysis of the user's emotional data. It adds special guidance for difficult operations and incorporates rest periods for relaxation.

[0550] Input: Success / failure judgment, analysis results of operational efficiency, analysis results of emotional state

[0551] Output: Generated personalized training plan

[0552] Step 8:

[0553] The server transmits the generated individual training plan to the user terminal, which presents the individual training plan to the user.

[0554] Input: Generated personalized training plan

[0555] Output: Individual training plan sent to the user's device

[0556] Step 9:

[0557] The user continues learning or operating in accordance with the individual training plan provided, thereby maximizing the training effect.

[0558] Input: Generated personalized training plan

[0559] Output: User interaction and continued learning

[0560] 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.

[0561] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0562] 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.

[0563] [Second embodiment]

[0564] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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. 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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."

[0576] The present invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's grade and learning history data, generates tests at an appropriate level based on the data, and analyzes the test results to create an individual learning plan, thereby providing efficient and effective learning support.

[0577] System Overview

[0578] The system consists of the following main components:

[0579] 1. User Device

[0580] 2. Server

[0581] 1. User Device

[0582] User Device: This is the device used by the learner (e.g., computer, tablet, smartphone, etc.). The user device provides the interface for the learner to enter grade and learning history data, view generated tests, and enter answers.

[0583] Examples:

[0584] The user inputs information such as "sixth grade elementary school student" and "I'm not good at fractions in math" through the terminal.

[0585] The terminal transmits the information to the server.

[0586] 2. Server

[0587] Server: The server is a central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[0588] Examples:

[0589] The server generates "6th grade math, Japanese, science, and social studies tests," including, for example, the math question "What is 3 x 4?"

[0590] The server sends the generated test to the device.

[0591] Program processing steps

[0592] The processing of the program will be explained in natural language below.

[0593] Test Generation

[0594] Server: Obtains the learner's grade and learning history data and generates the most appropriate test based on that.

[0595] For example, if the grade level is "sixth grade," standard test questions for sixth graders will be given.

[0596] Test presentation and answer collection

[0597] Terminal: Displays the test sent from the server to the learner.

[0598] User: Enter the answer to each test question.

[0599] For example, when asked "What is 3 x 4?", the answer is "12."

[0600] Terminal: Collects learners' answers and sends them to the server.

[0601] The terminal sends the answer to the server.

[0602] Comprehension analysis

[0603] Server: Analyzes the submitted answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[0604] For example, if the answer to the question about how to read "kan" is "kan," it is judged to be correct, and if it is "ou," it is judged to be incorrect. The time required for each answer is also recorded.

[0605] Create a personalized learning plan

[0606] Server: Generates the optimal learning plan for the learner based on the results of comprehension analysis.

[0607] For example, if a student's understanding of Japanese kanji is lacking, create a plan that includes additional practice questions and reference materials.

[0608] Server: Sends the generated learning plan to the user's device.

[0609] The server transmits the plan to the terminal and displays it to the learner.

[0610] Presentation of study plan

[0611] Device: Display the sent learning plan to the learner.

[0612] User: Proceed with your learning based on the plan provided.

[0613] Specific examples

[0614] For example, if a sixth-grade student has difficulty with fraction problems, the server will generate "basic fraction problems for sixth-grade students" based on that information. It will analyze the answers and, if there are many incorrect answers, provide an individualized learning plan that includes "basic fraction calculation drills" and "videos explaining the concept of fractions." If the student lacks understanding of how to read kanji, it will add "kanji writing" and "reading practice problems" to the learning plan.

[0615] This allows learners to efficiently overcome their weaknesses and improve their overall understanding. In this way, the present invention provides a learning experience optimized for each learner, effectively resolving conventional problems.

[0616] The processing flow will be explained below.

[0617] Program processing steps

[0618] Step 1:

[0619] User: Enter grade and learning history data into the terminal.

[0620] For example, enter information such as "sixth grade elementary school student" and "not good at fractions."

[0621] Step 2:

[0622] Terminal: Transmit the input school year and learning history data to the server.

[0623] The terminal uses the transmission function to send this information to the server.

[0624] Step 3:

[0625] Server: Based on the received school year and learning history data, use a test generation algorithm to generate appropriate tests.

[0626] Create test questions such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢" for sixth graders.

[0627] Step 4:

[0628] Server: Transmit the generated test to the terminal.

[0629] By sending the created test to the terminal, make it accessible to the learner.

[0630] Step 5:

[0631] Terminal: Display the received test to the user.

[0632] The terminal displays the questions on the screen to enable the user to answer.

[0633] Step 6:

[0634] User: Input an answer to the presented test question.

[0635] For example, input "3×4 is 12".

[0636] Step 7:

[0637] Terminal: Collect the learner's answers and transmit the data to the server.

[0638] The collected answers are packetized and sent to the server.

[0639] Step 8:

[0640] Server: Scores the received answers and analyzes the correctness and time required to answer.

[0641] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[0642] Step 9:

[0643] Server: Identify areas of high error and delay and assess learner comprehension.

[0644] For example, if a student gives the wrong answer "ou" to the question about how to read "kanji" many times, this is judged to be a lack of understanding.

[0645] Step 10:

[0646] Server: Generates an individual learning plan based on the results of comprehension analysis.

[0647] For example, if a child lacks understanding of kanji, a plan can be created that recommends the use of "kanji reading drills" or "kanji learning apps."

[0648] Step 11:

[0649] Server: Sends the generated learning plan to the device.

[0650] Send the created plan to the device so that learners can access it.

[0651] Step 12:

[0652] Device: Display the received learning plan to the user.

[0653] The device displays the learning plan on the screen and guides the user through the next steps.

[0654] Step 13:

[0655] User: Proceed with your studies based on the provided learning plan.

[0656] For example, students can study by solving the added kanji problems and using the provided learning resources.

[0657] Through this series of steps, the system provides learners with an efficient and effective learning experience.

[0658] Example 1

[0659] 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."

[0660] Conventional learning support systems have the problem of being unable to easily generate and present test questions and learning plans that correspond to each learner's level of understanding and learning history. This makes it difficult to provide effective learning guidance that is tailored to each learner's weaknesses and learning pace. Furthermore, the inability to provide appropriate feedback quickly makes it difficult to maintain learners' motivation.

[0661] 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.

[0662] In this invention, the server includes a means for inputting the learner's grade and learning history data, a means for generating a test at an appropriate level based on the grade and learning history data, and a means for customizing the test and learning plan using a generative AI model, thereby enabling the generation and presentation of optimal tests and learning plans that meet the individual needs of each learner.

[0663] "Grade" is data that indicates the stage of the educational curriculum to which a learner belongs.

[0664] "Learning history data" refers to data that indicates what a learner has learned so far, their grades, level of understanding, and their learning progress.

[0665] The "test generation means" is a means having the function of creating a test at an appropriate level for a learner based on the input grade and learning history data.

[0666] A "generative AI model" is a technology that uses artificial intelligence to generate and customize optimal test questions and study plans based on a learner's specific learning data.

[0667] The "test presentation means" is a means having a function for displaying the generated test to the learner.

[0668] The "answer collection means" is a means having a function for collecting answers entered by learners to test questions.

[0669] The "means for assessing comprehension" is a means that has the function of assessing the learner's level of comprehension by determining whether the answer is correct or incorrect and the time required to answer based on the answers collected from the learner.

[0670] The "learning plan generation means" is a means having the function of creating an optimal learning plan for each learner based on the results of the comprehension assessment.

[0671] The "study plan presentation means" is a means having a function for displaying the generated study plan to the learner.

[0672] This invention is a system that provides an optimized learning experience for each learner. The system's main hardware components are a user terminal and a server, and it uses a generative AI model to generate test questions and study plans based on the learner's grade and learning history data.

[0673] 1. User Device

[0674] A user terminal is a device used by a learner (for example, a computer, tablet, or smartphone). The user terminal provides an interface for the learner to input their grade and learning history data, view the generated test, and enter their answers. Specifically, the learner inputs information such as "sixth grade of elementary school" and "I'm not good at fractions in math" through the terminal, and the terminal sends this information to the server.

[0675] 2. Server

[0676] The server is a central component that processes data. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device. Specifically, the server generates "mathematics, Japanese, science, and social studies tests for sixth graders," and creates test questions that include, for example, the math question "What is 3 x 4?" The server then sends the generated tests to the device.

[0677] Specific examples of processing

[0678] For example, a learner might use a prompt such as, "I'm in sixth grade and I'm particularly bad at fraction problems. Based on this information, please generate fraction-related test questions." The user enters this information into their device, which then sends it to the server. The server uses a generative AI model to generate a test that includes fraction questions based on this information. For example, it creates a question like, "What is 3 / 5 + 2 / 3?" The generated test is sent to the user's device, and the user answers it.

[0679] The user's device collects the entered answers and sends them back to the server. The server analyzes the answers, determines whether they were correct and analyzes the time it took to answer, and then generates an individual study plan. For example, if there are few correct answers, a study plan including "basic drills for fraction calculations" is generated. The generated study plan is sent to the user's device, and the user proceeds with their studies based on it.

[0680] This makes it possible to provide optimal learning support tailored to each individual learner's level of understanding.The system aims to efficiently overcome learners' weaknesses and promote overall learning progress.

[0681] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0682] Step 1:

[0683] The user inputs grade and learning history data through the terminal.

[0684] Specific operation: The user enters the information "sixth grade of elementary school" and "I'm not good at fractions in math" into the input form on the device.

[0685] Input: Grade information, learning history data.

[0686] Output: Entered grade information and learning history data.

[0687] Step 2:

[0688] The terminal transmits the input data to the server.

[0689] Specific operation: The device sends information such as "sixth grade of elementary school" and "not good at fractions" to the server.

[0690] Input: Grade information, learning history data.

[0691] Output: Grade information and learning history data sent to the server.

[0692] Step 3:

[0693] The server generates appropriate tests based on the received grade and learning history data, using a generative AI model to identify questions.

[0694] Specific operation: The server generates "basic fraction problems for sixth graders," for example, "What is 3 / 5 + 2 / 3?"

[0695] Input: Grade information and learning history data received by the server.

[0696] Output: The generated test questions.

[0697] Step 4:

[0698] The server sends the generated test to the device.

[0699] Specific operation: The server sends the generated fraction problem to the terminal.

[0700] Input: The generated test questions.

[0701] Output: The test question sent to the device.

[0702] Step 5:

[0703] The terminal displays the received test to the user, who then inputs answers to each test question.

[0704] Specific operation: The user enters the answer "15 / 31" in response to the question "What is 3 / 5 + 2 / 3?" displayed on the terminal.

[0705] Input: Test questions displayed on the terminal, user answers.

[0706] Output: The user's answer typed into the terminal.

[0707] Step 6:

[0708] The terminal sends the answer entered by the user to the server.

[0709] Specific operation: The device sends the answer "15 / 31" to the server.

[0710] Input: The user's answer as entered into the terminal.

[0711] Output: The user's answer sent to the server.

[0712] Step 7:

[0713] The server analyzes the received answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[0714] Specific operation: The server confirms that the correct answer to "3 / 5 + 2 / 3" is "15 / 31", determines that the answer is correct, and records the time it took to answer.

[0715] Input: The user's answer sent to the server.

[0716] Output: Learner's comprehension assessment results (correct / incorrect, answer time).

[0717] Step 8:

[0718] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment, and customizes the plan using a generative AI model.

[0719] Specific operation: If the basic fraction questions are all answered correctly, the server creates a plan including applied fraction questions. If there are many incorrect answers, the server creates a plan including "basic fraction calculation drills" and "videos explaining the concept of fractions."

[0720] Input: Learner comprehension assessment results.

[0721] Output: The generated learning plan.

[0722] Step 9:

[0723] The server transmits the generated study plan to the terminal.

[0724] Specific operation: The server sends a learning plan including "Basic drills for fraction calculations" and "Videos explaining the concept of fractions" to the device.

[0725] Input: The generated learning plan.

[0726] Output: The study plan sent to the device.

[0727] Step 10:

[0728] The terminal displays the received learning plan to the user.

[0729] Specific operation: The device displays to the user "Basic fraction calculation drills" and "Video explaining the concept of fractions."

[0730] Input: The study plan sent to your device.

[0731] Output: The learning plan displayed to the user.

[0732] Step 11:

[0733] The user proceeds with their studies based on the presented study plan.

[0734] Specific operations: The user solves the displayed "Basic fraction calculation drills" and watches the "Video explaining the concept of fractions."

[0735] Input: The learning plan displayed to the user.

[0736] Output: User's learning progress.

[0737] (Application example 1)

[0738] 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."

[0739] Conventional learning support systems have the problem of being difficult to individually optimize, fully reflecting each learner's learning stage and weaknesses. This has prevented them from providing efficient and effective learning support. It has also been difficult to perform a detailed analysis of a learner's level of understanding and provide an appropriate learning plan based on that analysis.

[0740] 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.

[0741] In this invention, the server includes means for inputting the learner's educational stage and learning history data, means for generating assessment questions of appropriate difficulty based on the said educational stage and learning history data, means for collecting the learner's answers, analyzing the collected answers and evaluating the learner's level of understanding, means for generating and presenting an optimal learning plan for the learner, means for managing progress, and means for providing individual educational materials. This makes it possible to provide an individual learning plan optimized for the characteristics of each learner, thereby realizing efficient and effective learning support.

[0742] "Learner" refers to an individual in a position to receive education.

[0743] "Educational stage" refers to the grade or learning progression to which a learner is currently assigned.

[0744] "Learning history data" refers to information about a learner's past learning activities and grades.

[0745] "Assessment questions" refer to test questions created to measure a learner's level of understanding and ability.

[0746] "Answer collection means" refers to a function that collects answers provided by learners to assessment questions.

[0747] "Means for assessing comprehension" refers to a function that determines the level of comprehension based on the answers provided by the learner.

[0748] A "learning plan" refers to a plan created to efficiently advance future learning based on the learner's level of understanding and challenges.

[0749] "Progress management means" refers to the function of monitoring and managing a learner's learning situation and progress.

[0750] "Educational materials" refers to teaching materials and reference materials provided to support learners' learning activities.

[0751] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to process data and generate new information or suggestions.

[0752] A "prompt" refers to text that contains instructions or questions to be input into a generative AI model.

[0753] This invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's educational level and learning history data, generates assessment questions of appropriate difficulty based on that data, and generates an individual learning plan by collecting and analyzing the learner's answers. Finally, the system presents the generated learning plan to the learner and manages their progress, thereby providing efficient and effective learning support.

[0754] System Overview

[0755] The system consists of the following main components:

[0756] 1. User Device

[0757] 2. Server

[0758] 1. User Device

[0759] User terminal: A device used by learners (computer, tablet, smartphone, etc.). User terminals provide an interface for learners to input educational stage and learning history data, display generated assessment questions, and enter answers. They also have functions for presenting learning plans and managing progress.

[0760] Examples:

[0761] The learner enters information such as "second year junior high school student" and "not good at mathematical equations" through the user terminal.

[0762] The user terminal transmits the information to the server.

[0763] 2. Server

[0764] Server: The server is a central data processing component. It generates assessment questions based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[0765] Examples:

[0766] The server generates "assessment questions about mathematical equations for second-year junior high school students" and sends them to the user's terminal.

[0767] A learner answers the question "Solve the x in 2x + 3 = 7" with "x = 2."

[0768] Program processing explanation

[0769] User device:

[0770] The user terminal collects the learner's educational stage and learning history data through the input interface and transmits the data to the server. After the learner answers the assessment questions, the answer data is also transmitted to the server.

[0771] server:

[0772] The server generates appropriate assessment questions based on the received data. This is done using a generative AI model. It also analyzes the learner's answers and evaluates their level of understanding. The analysis results are reflected in the learner's learning plan via prompts. The following are example prompts:

[0773] Prompt Sentence Examples

[0774] Based on the data entered, such as "second-year junior high school student" and "not good at mathematical equations," the server generates the following assessment questions.

[0775] - "Solve the x in 2x + 3 = 7"

[0776] Examples:

[0777] The server generates "assessment questions about mathematical equations for second-year junior high school students."

[0778] The user device presents the learner with the problem "Solve the x in 2x + 3 = 7."

[0779] The learner answers "x = 2", and the server receives and evaluates the answer.

[0780] The server also generates an individualized learning plan based on the results of the comprehension assessment and transmits it to the user terminal. The learning plan may include, for example, "drills to strengthen the basics of equations" and "videos explaining equations."

[0781] This allows learners to receive individually optimized learning plans, enabling them to manage their own progress and study effectively.

[0782] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0783] Step 1:

[0784] The user inputs educational stage and learning history data into the user terminal. Examples of input data include "second year junior high school student" and "poor at mathematical equations." This input data is sent to the server by the user terminal. Because the input data includes specific areas of weakness and past grades, the server treats it as basic data for generating individual assessment questions.

[0785] Step 2:

[0786] The server generates appropriate assessment questions based on the received data. Using the generative AI model, it generates assessment questions for the prompts "junior high school second year student" and "not good at math equations." For example, a question like "solve the x in 2x + 3 = 7" is generated. This generated assessment question is sent from the server to the user's device.

[0787] Step 3:

[0788] The user terminal presents the generated assessment questions to the learner. The assessment questions are displayed on the screen of the user terminal, and an interface is provided that allows the learner to answer them.

[0789] Step 4:

[0790] The user inputs an answer to the presented evaluation question. For example, to the question "Solve x in 2x + 3 = 7," the user answers "x = 2." This answer is sent to the server by the user terminal.

[0791] Step 5:

[0792] The server analyzes the received answers. Using the comprehension assessment means, it analyzes whether the answers are correct and the time it took to answer them. For example, if the answer "x = 2" is correct, it is recorded as the correct answer, and the time it took to answer is also recorded. The results of this analysis are used to assess the learner's comprehension.

[0793] Step 6:

[0794] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment. Using a generative AI model, the learning plan is generated based on the prompt. For example, a plan including "Equation Basics Reinforcement Drills" and "Equation Explanation Videos" may be generated. This plan is then sent from the server to the user's device.

[0795] Step 7:

[0796] The user device presents the generated learning plan to the learner. The user device has the function of displaying specific learning activities and reference materials, allowing the learner to check their own progress as they study.

[0797] Step 8:

[0798] The user progresses through their studies based on the presented learning plan. The user's device records the learner's learning activities and sends progress information to the server, allowing the server to continuously monitor the learner's situation and update the learning plan as necessary.

[0799] Through the above processing steps, the learner receives an optimal learning plan based on his / her educational stage and learning history, and can proceed with his / her studies efficiently and effectively.

[0800] 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.

[0801] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, it aims to improve learning efficiency by combining it with an emotion engine that recognizes the user's emotions.

[0802] System Overview

[0803] The system consists of the following main components:

[0804] 1. User Device

[0805] 2. Server

[0806] 3. Emotion Engine

[0807] 1. User Device

[0808] User terminal: A device used by a learner (e.g., computer, tablet, smartphone, etc.). It provides an interface for learners to input grade and learning history data, view generated tests, and enter answers.

[0809] Examples:

[0810] The user uses the terminal to input information such as "sixth grade elementary school student" and "not good at fractions," and this information is sent to the server via the terminal.

[0811] 2. Server

[0812] Server: A central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes comprehension, and creates a learning plan based on the results and provides it to the user.

[0813] Examples:

[0814] The server generates a test for sixth graders in mathematics, Japanese, science, and social studies, and sends it to the device. For example, the test includes a math question such as "What is 3 x 4?"

[0815] 3. Emotion Engine

[0816] Emotion engine: This component uses a camera and microphone to recognize the learner's emotions. The emotion engine analyzes the learner's facial expressions and tone of voice and collects this data to understand their emotional state, such as whether they are feeling stressed or struggling to understand.

[0817] Examples:

[0818] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[0819] Program processing

[0820] The program processing will be explained in detail below in natural language.

[0821] Test Generation

[0822] Server: Generates optimal tests based on grade and learning history data, including multiple question formats.

[0823] For example, based on the input data of "sixth grade elementary school students," create a test that includes the basic math question "What is 3 x 4?"

[0824] Test presentation and answer collection

[0825] Terminal: Presents the generated test to the learner and allows them to enter their answers.

[0826] User: Enters answers to the test questions provided.

[0827] For example, answer "3 x 4 is 12."

[0828] Terminal: Collects the learner's answers and sends the data to the server.

[0829] Data collected along with the answers also includes time data.

[0830] Comprehension analysis and sentiment data collection

[0831] Server: Scores the received answers, analyzes whether they are correct or not, and analyzes the answering time. It also analyzes the emotion data collected from the emotion engine.

[0832] For example, the system associates correct and incorrect answers to the reading of the answer "kan" (Chinese character) with emotional data such as "anxiety" and "concentration" while answering.

[0833] Create a personalized learning plan

[0834] Server: Generates an optimal learning plan for the learner based on both comprehension and emotion data.

[0835] For example, if comprehension is low and the emotion engine identifies "anxiety," the plan might include a break to relax or instructions to start with an easy problem.

[0836] Presentation of study plan

[0837] Terminal: Presents the generated learning plan to the learner.

[0838] User: Proceed with your studies based on the provided learning plan.

[0839] For example, solve the added kanji practice problems and use the provided resources to advance your learning.

[0840] Specific examples

[0841] For example, if a sixth-grade student inputs that they have difficulty with fraction problems, the server uses that information to generate a test that includes basic fraction questions. After the student answers, the emotion engine recognizes states of "anxiety" or "lack of concentration" and reflects these in the analysis. The resulting study plan includes materials to gradually teach the basics of fractions and a stress management plan that includes short breaks.

[0842] In this way, the present invention provides an individually optimized learning experience that also takes into account the emotional state, and has become a system that more effectively solves conventional problems.

[0843] The processing flow will be described below.

[0844] Processing steps of the program

[0845] The processing steps of the system including the emotion engine will be described in detail below.

[0846] Step 1:

[0847] User: Inputs grade and learning history data into the terminal.

[0848] For example, inputs information such as "sixth grade in primary school" and "weak in fractions".

[0849] Step 2:

[0850] Terminal: Transmits the input grade and learning history data to the server.

[0851] The terminal uses the transmission function to send this information to the server.

[0852] Step 3:

[0853] Server: Based on the received grade and learning history data, uses a test generation algorithm to generate appropriate tests.

[0854] Creates test questions such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢" for sixth graders in primary school.

[0855] Step 4:

[0856] Server: Transmits the generated tests to the terminal.

[0857] The created test can be sent to a device so that learners can access it.

[0858] Step 5:

[0859] Terminal: Displays received tests to the user.

[0860] The terminal displays questions on the screen and allows the user to answer them.

[0861] Step 6:

[0862] User: Enters answers to the test questions provided.

[0863] For example, enter "3 x 4 is 12".

[0864] Step 7:

[0865] Terminal: Collects the learner's answers and sends the data to the server.

[0866] The collected answers are packetized and sent to the server.

[0867] Step 8:

[0868] Server: Scores the received answers and analyzes the correctness and time required to answer.

[0869] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[0870] Step 9:

[0871] Emotion Engine: Uses a camera and microphone to analyze the user's facial expressions and tone of voice during testing.

[0872] For example, if a person's facial expression is stern and their voice is low-pitched, this will be perceived as "anxiety."

[0873] Step 10:

[0874] Server: Based on the emotional data obtained from the emotion engine, the learner's emotional state is recorded and used to assess comprehension.

[0875] The results are compared with the scoring results, and for example, the times when the learner felt stressed are recorded.

[0876] Step 11:

[0877] Server: Generates personalized learning plans based on comprehension and emotion data.

[0878] For example, if the emotional data indicates "anxiety," the study plan could include breaks to relax and simple stress-reducing exercises.

[0879] Step 12:

[0880] Server: Sends the generated learning plan to the device.

[0881] Send the created plan to the device so that learners can access it.

[0882] Step 13:

[0883] Device: Display the received learning plan to the user.

[0884] The device displays the learning plan on the screen and guides the user through the next steps.

[0885] Step 14:

[0886] User: Proceed with your studies based on the provided learning plan.

[0887] For example, the system can solve additional kanji practice problems, utilize presented resources to advance learning, and even implement instructions based on emotion data, such as taking breaks.

[0888] Through this series of steps, the system not only provides learners with an efficient and effective learning experience, but also provides them with a personalized learning plan that takes their emotional state into account.

[0889] Example 2

[0890] 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."

[0891] In today's education system, it is difficult to provide an individually optimized learning experience by analyzing each learner's level of understanding and emotional state in detail. In particular, there is still a lack of technology that can recognize a learner's emotional state in real time and adjust the learning plan accordingly. As a result, learners may feel stressed or be left with a low level of understanding.

[0892] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the learner's grade and learning history data; means for generating a test at an appropriate level based on the grade and learning history data; means for presenting the generated test to the learner and collecting the learner's answers; means for evaluating the learner's level of understanding by analyzing the collected answers and answer times; means for recognizing the learner's emotional data and analyzing the emotional data; means for generating an optimal learning plan based on the evaluation results and the emotional data analysis results; and means for presenting the generated learning plan to the learner. This enables an individually optimized learning experience that simultaneously takes into account the learner's level of understanding and emotional state.

[0893] A "learner" is an individual receiving education, specifically someone who uses the system to carry out learning activities.

[0894] "Grade" refers to the stage at which a learner progresses in the educational curriculum.

[0895] "Learning history data" refers to data that records the learning content, grades, and progress of a learner to date.

[0896] A "server" is a part of a computer system that performs central data processing, and is a device that accepts requests from multiple clients and generates appropriate responses.

[0897] A "test" is a set of questions or tasks given to a learner to assess their understanding or knowledge.

[0898] The "test generation means" is a means having the function of automatically creating a test at an appropriate level based on the learner's grade and learning history data.

[0899] "Answer" is the answer or solution provided by the learner to the test.

[0900] The "answer collection means" is a means that has the function of collecting answers given by learners to tests and recording them as data.

[0901] "Response time" is the time taken to respond to one question.

[0902] The "means for assessing comprehension" is a means that has the function of analyzing the collected answers and answer times to assess the learner's level of comprehension.

[0903] "Emotional data" is data that indicates the emotional state of the learner during learning, extracted from the learner's facial expressions, tone of voice, etc.

[0904] "Emotion data recognition means" refers to a means that has the function of recognizing the learner's emotional state through a camera or microphone, and collecting and analyzing that data.

[0905] An "optimal learning plan" is a set of learning strategies and tasks that are most effective for a learner, created based on the learner's comprehension and emotional data.

[0906] The "study plan generation means" is a means having the function of automatically creating an optimal study plan based on the evaluation results and the emotion data analysis results.

[0907] The "study plan presentation means" is a means having the function of presenting the generated study plan to the learner.

[0908] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, the system aims to improve learning efficiency by combining an emotion engine that recognizes the learner's emotions.

[0909] The system consists of the following main components:

[0910] 1. User Device

[0911] 2. Server

[0912] 3. Emotion Engine

[0913] 1. User Device

[0914] A user terminal is a device used by a learner (e.g., a computer, tablet, smartphone, etc.). The terminal provides an interface for inputting the learner's grade and learning history data, and has the functionality to display generated tests and input answers.

[0915] Examples:

[0916] The user uses a terminal to input information such as "sixth grade elementary school student" and "I'm not good at fractions." This information is then sent to the server via the terminal.

[0917] 2. Server

[0918] The server is a central data processing component. It generates tests based on learner data and sends them to the user's device. It also collects answers and analyzes comprehension, and creates and provides a learning plan based on the results.

[0919] Examples:

[0920] The server generates a "sixth grade test" and sends it to the device. For example, the math question includes "What is 3 x 4?"

[0921] 3. Emotion Engine

[0922] The emotion engine is a component that uses a camera and microphone to recognize the learner's emotions. By analyzing the learner's facial expressions and tone of voice and collecting this data, the emotion engine can grasp the learner's emotional state, such as whether they are feeling stressed or struggling to understand.

[0923] Examples:

[0924] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[0925] Program processing

[0926] Next, the processing of the program is explained in detail in natural language.

[0927] Entering User Data

[0928] The terminal provides an interface for the learner to input their grade and learning history data. The learner inputs their grade and weak subjects on the terminal.

[0929] Sending data

[0930] The terminal transmits the learner's input data to the server.

[0931] Test Generation

[0932] The server generates appropriate tests based on the learner's data. The tests include multiple question formats to measure the learner's level of understanding.

[0933] Examples:

[0934] The server selects math problems centered around fractions for sixth grade students who feel they are "not good at fractions," and generates tests that include questions such as "3 / 4 + 1 / 2 = ?"

[0935] Test Delivery

[0936] The server transmits the generated test to the user terminal.

[0937] Presenting the test and collecting answers

[0938] The terminal presents the received test to the learner, who then answers each question on the terminal.

[0939] Submit your answer

[0940] The terminal transmits the learner's answer data to the server.

[0941] Answer analysis

[0942] The server scores the received answers, analyzes the accuracy and time of the answers, and also analyzes the emotion data collected by the emotion engine.

[0943] Examples:

[0944] The server scores the received answer "5 / 4," determines whether it is correct, and analyzes the time it took to answer. At the same time, it recognizes from the emotional data that the learner felt "anxiety" while answering.

[0945] Generate a personalized learning plan

[0946] The server generates an optimal learning plan for the learner based on the answer results and the emotion data analysis results.

[0947] Examples:

[0948] The server generates a study plan that includes videos explaining basic fraction problems for those with low comprehension, links to explanatory articles, and breaks for relaxation.

[0949] Delivery and presentation of learning plans

[0950] The server transmits the generated study plan to the user terminal, and the terminal presents it to the learner.

[0951] Prompt Sentence Examples

[0952] Use the following as a specific prompt:

[0953] "Please generate a test for a sixth-grader who has difficulty with fractions. Also, please suggest an appropriate learning plan based on the emotional data of the answers."

[0954] These prompts allow the generative AI model to generate tests and corresponding personalized study plans focused on specific grade levels and subjects.

[0955] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0956] Step 1:

[0957] The user uses the device to input data such as grade level and learning history. The input information is "sixth grade" and "not good at fractions." The device prepares this data in JSON format and prepares to send it to the server.

[0958] Step 2:

[0959] The terminal sends the grade and learning history data entered by the user to the server. Specifically, when the send button is clicked, the terminal includes the information in the body of an HTTP request and posts it to the server.

[0960] Input: Grade and learning history data (e.g., "6th grade" or "I'm not good at fractions")

[0961] Output: Data sent to the server (JSON format)

[0962] Step 3:

[0963] The server generates appropriate tests based on the received learner data. The server retrieves questions from a database and selects questions according to grade and subject.

[0964] Examples:

[0965] The server generates tests for sixth graders who have difficulty with fractions, including questions such as "3 / 4 + 1 / 2 = ?"

[0966] Input: Grade and learning history data

[0967] Data processing: Extraction of relevant problems from the database

[0968] Output: Generated test data

[0969] Step 4:

[0970] The server sends the generated test to the user's device. The test data is formatted in JSON format and sent as an HTTP response.

[0971] Input: Generated test data

[0972] Output: Test transmission to user device (JSON format)

[0973] Step 5:

[0974] The device presents the received test to the learner. The device has the function of analyzing the test data and displaying the questions on the user interface.

[0975] Input: Test data received from the server

[0976] Output: Display of test questions

[0977] Step 6:

[0978] The user enters answers to the test displayed on the terminal, for example, "3 / 4 + 1 / 2 = 5 / 4."

[0979] Input: Test questions and user answers

[0980] Output: Learner's answer data

[0981] Step 7:

[0982] The terminal sends the answer data entered by the learner to the server, including the answer content and the time it took to answer.

[0983] Input: Learner's answer data

[0984] Output: Send the learner's answer data to the server

[0985] Step 8:

[0986] The server analyzes the received answer data and answer time. It judges whether the answer was correct or incorrect, and evaluates the accuracy of the answer and the time required. It also adds emotional data collected by the emotion engine to the analysis.

[0987] Examples:

[0988] The server scores the answer "5 / 4" and determines whether it is correct. It also recognizes from the emotional data that the learner felt "anxiety" while answering.

[0989] Input: Answer data and emotion data

[0990] Data processing: Correct / incorrect judgment and emotional data analysis

[0991] Output: Analysis results (comprehension assessment and emotional state)

[0992] Step 9:

[0993] The server generates an optimal learning plan for each learner based on the answers and the results of the emotion data analysis. If the learner's level of understanding is deemed low, the server creates a plan that includes supplementary materials, basic questions, and breaks for relaxation.

[0994] Input: Analysis results (comprehension assessment and emotional state)

[0995] Data processing: generating learning plans

[0996] Output: Generated lesson plan

[0997] Step 10:

[0998] The server sends the generated learning plan to the user's device. The learning plan is also formatted in JSON format and sent to the user's device as an HTTP response.

[0999] Input: Generated lesson plan

[1000] Output: Study plan sent to user device (JSON format)

[1001] Step 11:

[1002] The terminal presents the received learning plan to the learner. The terminal analyzes the learning plan and displays it on the user interface. The learner then proceeds with the next learning activity based on the presented plan.

[1003] Input: Study plan received from the server

[1004] Output: Display of learning plan

[1005] The above are the specific processing steps and flow of the entire system.

[1006] (Application example 2)

[1007] 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."

[1008] Conventional learning and factory training systems have had difficulty providing optimal plans that fully consider the learning ability and operational proficiency of individual learners and operators. Furthermore, they were unable to adjust training or learning to take into account the user's emotional state. This resulted in problems such as reduced learning and operational efficiency and increased stress and anxiety.

[1009] 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 generating an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, means for presenting the generated training protocol and collecting operation data, means for collecting emotional data during operation and adjusting the training plan, means for analyzing the level of understanding and operation data and generating an appropriate learning plan, and means for presenting the generated learning plan. This makes it possible to provide optimal learning plans and training plans for individual learners and operators, thereby improving learning efficiency and operation efficiency.

[1010] "Learner" refers to students and educational subjects who are the main participants in learning activities.

[1011] "Grade" is an indicator of the progress of the educational curriculum to which a learner belongs.

[1012] "Learning history data" refers to data that records the learning activities that a learner has undertaken and the results of those activities.

[1013] A "test" is an exam or question set used to assess a learner's understanding or knowledge.

[1014] A "training protocol" is a plan that outlines the procedures and content of the training that operators and learners should carry out.

[1015] "Proficiency" is an indicator of the operator's or learner's level of proficiency in skills and knowledge.

[1016] "Operation history data" refers to data that records the operation activities that an operator has performed up to now and the results of those activities.

[1017] "Emotional data" is data used to collect and analyze the emotional state of learners and operators.

[1018] A "training plan" is a specific learning or training plan established based on the proficiency and emotional state of an operator or learner.

[1019] "Operation data" refers to data that records the details of operations performed by an operator.

[1020] "Analysis results" are the results of analysis conducted based on collected data.

[1021] A "server" is a computer system that centrally processes data and performs various calculations and data generation.

[1022] The present invention is a system that generates an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, and provides an individually customized training plan based on the results. Furthermore, it aims to recognize the emotions of the learner or operator using an emotion engine and adjust the training plan accordingly.

[1023] System Overview

[1024] The system consists of the following main components:

[1025] 1. User Device

[1026] 2. Server

[1027] 3. Emotion Engine

[1028] 1. User Device

[1029] User terminal: A device (e.g., tablet, smartphone) used by an operator or learner. The user terminal provides an interface for inputting proficiency and operation history data, displaying generated training protocols, and collecting operation and emotion data.

[1030] 2. Server

[1031] Server: A central data processing component. It generates training protocols based on the proficiency and operation history data of operators and learners, and sends them to the user's device. It also collects operation and emotion data, and adjusts and generates training plans based on this data, which it then provides to the user.

[1032] 3. Emotion Engine

[1033] Emotion Engine: A component with the ability to recognize the emotions of operators and learners using a camera and microphone. The emotion engine analyzes facial expressions and tone of voice to understand the emotional state of operators and learners.

[1034] Program processing

[1035] Training protocol generation

[1036] The server generates an optimal training protocol based on the operator's or learner's proficiency and past operation history data, which includes multiple operation procedures and training items.

[1037] For example, based on information such as "beginner level" or "difficulty with basic robot operations," a training protocol can be created that progresses from basic arm operations.

[1038] Training presentation and operation data collection

[1039] The user terminal presents the generated training protocol to the operator and collects operation data and emotion data.

[1040] The operator performs the operation according to the protocol, and emotional data (e.g., facial expressions and tone of voice collected by a camera or microphone) is also collected.

[1041] Analysis of operational and sentiment data

[1042] The server scores and analyzes the received operation data, and simultaneously analyzes the emotion data collected from the emotion engine.

[1043] For example, if a particular operation is frequently subject to operational errors and "anxiety" is recognized in the emotional data, that operation will be included as a focus in the next training session.

[1044] Generate personalized training plans

[1045] The server generates optimal training plans for operators and learners based on operation data and emotion data.

[1046] For example, if the emotional data indicates "anxiety," it will create a training plan with increased step-by-step guidance and short breaks to relax.

[1047] Training plan presentation

[1048] The user terminal presents the generated training plan to the operator, allowing them to continue learning and training.

[1049] The operator continues to operate based on the presented training plan, and the results and emotional data are sent back to the server.

[1050] Hardware and software used

[1051] The hardware used includes a smartphone or tablet (user device) with a camera and microphone (for the emotion engine). The software uses a training program generation algorithm for the server and emotion recognition AI (e.g., Microsoft Azure Emotion API).

[1052] Examples and prompts

[1053] For example, here is a specific example of new operator A learning the basics of operating a factory robot:

[1054] Person A uses the control terminal to input "new employee level" and "not good at operating the arm."

[1055] The server generates a training protocol including basic arm operations and sends it to the terminal.

[1056] As Mr. A continues his training, his emotion recognition data is evaluated as "anxiety."

[1057] Based on the analysis, the server will provide more tips for the next training session and add breaks to improve concentration.

[1058] Example prompts to input to the generative AI model

[1059] Enter the new operator's skill level and past operating history.

[1060] Create a specific training protocol based on information such as "rookie level" or "poor arm operation", and generate an individual training plan taking into account emotional data during operation.

[1061] In this way, the present invention provides a personalized, optimized training experience that also takes into account emotional state, improving the operator's learning and operating efficiency.

[1062] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1063] Step 1:

[1064] The user uses a user terminal to input their own proficiency level and past operation history data. The input data includes information such as "rookie level" or "poor at arm operation." All of this data is sent to the server.

[1065] Input: User's skill level and operation history data

[1066] Output: Skill level and operation history data sent to the server

[1067] Step 2:

[1068] The server generates an optimal training protocol based on the received proficiency and operation history data. Using a generative AI model, it creates a specific training protocol while conducting comparative analysis with past training data.

[1069] Input: Skill level and operation history data

[1070] Output: The generated training protocol

[1071] Step 3:

[1072] The server transmits the generated training protocol to the user terminal, which presents the training protocol to the operator.

[1073] Input: Generated training protocol

[1074] Output: The training protocol sent to the user's device.

[1075] Step 4:

[1076] The user performs operations according to the training protocol displayed on the user device. During the operations, the user device collects emotional data (facial expressions, tone of voice, etc.) using a camera and microphone.

[1077] Input: User actions and emotion data

[1078] Output: Collected operation data and emotion data

[1079] Step 5:

[1080] The user terminal transmits the collected operation data and emotion data to the server, which can then perform detailed evaluation and analysis.

[1081] Input: Collected operation data and emotion data

[1082] Output: Operation data and emotion data sent to the server

[1083] Step 6:

[1084] The server analyzes the received operation data, judges the success or failure and analyzes the operation efficiency, and simultaneously analyzes the emotion data to grasp the user's emotional state during operation.

[1085] Input: Operation data and emotion data

[1086] Output: Success / failure judgment, analysis of operational efficiency, and analysis of emotional state

[1087] Step 7:

[1088] The server generates an individual training plan based on the evaluation of the user's operation data and the analysis of the user's emotional data. It adds special guidance for difficult operations and incorporates rest periods for relaxation.

[1089] Input: Success / failure judgment, analysis results of operational efficiency, analysis results of emotional state

[1090] Output: Generated personalized training plan

[1091] Step 8:

[1092] The server transmits the generated individual training plan to the user terminal, which presents the individual training plan to the user.

[1093] Input: Generated personalized training plan

[1094] Output: Individual training plan sent to the user's device

[1095] Step 9:

[1096] The user continues learning or operating in accordance with the individual training plan provided, thereby maximizing the training effect.

[1097] Input: Generated personalized training plan

[1098] Output: User interaction and continued learning

[1099] 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.

[1100] 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.

[1101] 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.

[1102] [Third embodiment]

[1103] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1104] 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.

[1105] 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).

[1106] 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.

[1107] 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.

[1108] 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).

[1109] 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. 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.

[1110] 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.

[1111] 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.

[1112] 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.

[1113] 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.

[1114] 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."

[1115] The present invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's grade and learning history data, generates tests at an appropriate level based on the data, and analyzes the test results to create an individual learning plan, thereby providing efficient and effective learning support.

[1116] System Overview

[1117] The system consists of the following main components:

[1118] 1. User Device

[1119] 2. Server

[1120] 1. User Device

[1121] User Device: This is the device used by the learner (e.g., computer, tablet, smartphone, etc.). The user device provides the interface for the learner to enter grade and learning history data, view generated tests, and enter answers.

[1122] Examples:

[1123] The user inputs information such as "sixth grade elementary school student" and "I'm not good at fractions in math" through the terminal.

[1124] The terminal transmits the information to the server.

[1125] 2. Server

[1126] Server: The server is a central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[1127] Examples:

[1128] The server generates "6th grade math, Japanese, science, and social studies tests," including, for example, the math question "What is 3 x 4?"

[1129] The server sends the generated test to the device.

[1130] Program processing steps

[1131] The processing of the program will be explained in natural language below.

[1132] Test Generation

[1133] Server: Obtains the learner's grade and learning history data and generates the most appropriate test based on that.

[1134] For example, if the grade level is "sixth grade," standard test questions for sixth graders will be given.

[1135] Test presentation and answer collection

[1136] Terminal: Displays the test sent from the server to the learner.

[1137] User: Enter the answer to each test question.

[1138] For example, when asked "What is 3 x 4?", the answer is "12."

[1139] Terminal: Collects learners' answers and sends them to the server.

[1140] The terminal sends the answer to the server.

[1141] Comprehension analysis

[1142] Server: Analyzes the submitted answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[1143] For example, if the answer to the question about how to read "kan" is "kan," it is judged to be correct, and if it is "ou," it is judged to be incorrect. The time required for each answer is also recorded.

[1144] Create a personalized learning plan

[1145] Server: Generates the optimal learning plan for the learner based on the results of comprehension analysis.

[1146] For example, if a student's understanding of Japanese kanji is lacking, create a plan that includes additional practice questions and reference materials.

[1147] Server: Sends the generated learning plan to the user's device.

[1148] The server transmits the plan to the terminal and displays it to the learner.

[1149] Presentation of study plan

[1150] Device: Display the sent learning plan to the learner.

[1151] User: Proceed with your learning based on the plan provided.

[1152] Specific examples

[1153] For example, if a sixth-grade student has difficulty with fraction problems, the server will generate "basic fraction problems for sixth-grade students" based on that information. It will analyze the answers and, if there are many incorrect answers, provide an individualized learning plan that includes "basic fraction calculation drills" and "videos explaining the concept of fractions." If the student lacks understanding of how to read kanji, it will add "kanji writing" and "reading practice problems" to the learning plan.

[1154] This allows learners to efficiently overcome their weaknesses and improve their overall understanding. In this way, the present invention provides a learning experience optimized for each learner, effectively resolving conventional problems.

[1155] The processing flow will be explained below.

[1156] Program processing steps

[1157] Step 1:

[1158] User: Enter grade and learning history data into the terminal.

[1159] For example, enter information such as "sixth grade elementary school student" and "not good at fractions."

[1160] Step 2:

[1161] Terminal: Transmit the entered school year and learning history data to the server.

[1162] The terminal uses the transmission function to send this information to the server.

[1163] Step 3:

[1164] Server: Based on the received school year and learning history data, use a test generation algorithm to generate appropriate tests.

[1165] Create test questions such as "What is 3 × 4?" and "Write the reading of the following Chinese character: 漢" for sixth graders.

[1166] Step 4:

[1167] Server: Transmit the generated test to the terminal.

[1168] By sending the created test to the terminal, make it accessible to the learner.

[1169] Step 5:

[1170] Terminal: Display the received test to the user.

[1171] The terminal displays the questions on the screen to enable the user to answer.

[1172] Step 6:

[1173] User: Enter an answer to the presented test question.

[1174] For example, enter "3 × 4 is 12".

[1175] Step 7:

[1176] Terminal: Collect the learner's answers and transmit the data to the server.

[1177] The collected answers are packetized and sent to the server.

[1178] Step 8:

[1179] Server: Scores the received answers and analyzes the correctness and time required to answer.

[1180] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[1181] Step 9:

[1182] Server: Identify areas of high error and delay and assess learner comprehension.

[1183] For example, if a student gives the wrong answer "ou" to the question about how to read "kanji" many times, this is judged to be a lack of understanding.

[1184] Step 10:

[1185] Server: Generates an individual learning plan based on the results of comprehension analysis.

[1186] For example, if a child lacks understanding of kanji, a plan can be created that recommends the use of "kanji reading drills" or "kanji learning apps."

[1187] Step 11:

[1188] Server: Sends the generated learning plan to the device.

[1189] Send the created plan to the device so that learners can access it.

[1190] Step 12:

[1191] Device: Display the received learning plan to the user.

[1192] The device displays the learning plan on the screen and guides the user through the next steps.

[1193] Step 13:

[1194] User: Proceed with your studies based on the provided learning plan.

[1195] For example, students can study by solving the added kanji problems and using the provided learning resources.

[1196] Through this series of steps, the system provides learners with an efficient and effective learning experience.

[1197] Example 1

[1198] 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."

[1199] Conventional learning support systems have the problem of being unable to easily generate and present test questions and learning plans that correspond to each learner's level of understanding and learning history. This makes it difficult to provide effective learning guidance that is tailored to each learner's weaknesses and learning pace. Furthermore, the inability to provide appropriate feedback quickly makes it difficult to maintain learners' motivation.

[1200] 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.

[1201] In this invention, the server includes a means for inputting the learner's grade and learning history data, a means for generating a test at an appropriate level based on the grade and learning history data, and a means for customizing the test and learning plan using a generative AI model, thereby enabling the generation and presentation of optimal tests and learning plans that meet the individual needs of each learner.

[1202] "Grade" is data that indicates the stage of the educational curriculum to which a learner belongs.

[1203] "Learning history data" refers to data that indicates what a learner has learned so far, their grades, level of understanding, and their learning progress.

[1204] The "test generation means" is a means having the function of creating a test at an appropriate level for a learner based on the input grade and learning history data.

[1205] A "generative AI model" is a technology that uses artificial intelligence to generate and customize optimal test questions and study plans based on a learner's specific learning data.

[1206] The "test presentation means" is a means having a function for displaying the generated test to the learner.

[1207] The "answer collection means" is a means having a function for collecting answers entered by learners to test questions.

[1208] The "means for assessing comprehension" is a means that has the function of assessing the learner's level of comprehension by determining whether the answer is correct or incorrect and the time required to answer based on the answers collected from the learner.

[1209] The "learning plan generation means" is a means having the function of creating an optimal learning plan for each learner based on the results of the comprehension assessment.

[1210] The "study plan presentation means" is a means having a function for displaying the generated study plan to the learner.

[1211] This invention is a system that provides an optimized learning experience for each learner. The system's main hardware components are a user terminal and a server, and it uses a generative AI model to generate test questions and study plans based on the learner's grade and learning history data.

[1212] 1. User Device

[1213] A user terminal is a device used by a learner (for example, a computer, tablet, or smartphone). The user terminal provides an interface for the learner to input their grade and learning history data, view the generated test, and enter their answers. Specifically, the learner inputs information such as "sixth grade of elementary school" and "I'm not good at fractions in math" through the terminal, and the terminal sends this information to the server.

[1214] 2. Server

[1215] The server is a central component that processes data. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device. Specifically, the server generates "mathematics, Japanese, science, and social studies tests for sixth graders," and creates test questions that include, for example, the math question "What is 3 x 4?" The server then sends the generated tests to the device.

[1216] Specific examples of processing

[1217] For example, a learner might use a prompt such as, "I'm in sixth grade and I'm particularly bad at fraction problems. Based on this information, please generate fraction-related test questions." The user enters this information into their device, which then sends it to the server. The server uses a generative AI model to generate a test that includes fraction questions based on this information. For example, it creates a question like, "What is 3 / 5 + 2 / 3?" The generated test is sent to the user's device, and the user answers it.

[1218] The user's device collects the entered answers and sends them back to the server. The server analyzes the answers, determines whether they were correct and analyzes the time it took to answer, and then generates an individual study plan. For example, if there are few correct answers, a study plan including "basic drills for fraction calculations" is generated. The generated study plan is sent to the user's device, and the user proceeds with their studies based on it.

[1219] This makes it possible to provide optimal learning support tailored to each individual learner's level of understanding.The system aims to efficiently overcome learners' weaknesses and promote overall learning progress.

[1220] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1221] Step 1:

[1222] The user inputs grade and learning history data through the terminal.

[1223] Specific operation: The user enters the information "sixth grade of elementary school" and "I'm not good at fractions in math" into the input form on the device.

[1224] Input: Grade information, learning history data.

[1225] Output: Entered grade information and learning history data.

[1226] Step 2:

[1227] The terminal transmits the input data to the server.

[1228] Specific operation: The device sends information such as "sixth grade of elementary school" and "not good at fractions" to the server.

[1229] Input: Grade information, learning history data.

[1230] Output: Grade information and learning history data sent to the server.

[1231] Step 3:

[1232] The server generates appropriate tests based on the received grade and learning history data, using a generative AI model to identify questions.

[1233] Specific operation: The server generates "basic fraction problems for sixth graders," for example, "What is 3 / 5 + 2 / 3?"

[1234] Input: Grade information and learning history data received by the server.

[1235] Output: The generated test questions.

[1236] Step 4:

[1237] The server sends the generated test to the device.

[1238] Specific operation: The server sends the generated fraction problem to the terminal.

[1239] Input: The generated test questions.

[1240] Output: The test question sent to the device.

[1241] Step 5:

[1242] The terminal displays the received test to the user, who then inputs answers to each test question.

[1243] Specific operation: The user enters the answer "15 / 31" in response to the question "What is 3 / 5 + 2 / 3?" displayed on the terminal.

[1244] Input: Test questions displayed on the terminal, user answers.

[1245] Output: The user's answer typed into the terminal.

[1246] Step 6:

[1247] The terminal sends the answer entered by the user to the server.

[1248] Specific operation: The device sends the answer "15 / 31" to the server.

[1249] Input: The user's answer as entered into the terminal.

[1250] Output: The user's answer sent to the server.

[1251] Step 7:

[1252] The server analyzes the received answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[1253] Specific operation: The server confirms that the correct answer to "3 / 5 + 2 / 3" is "15 / 31", determines that the answer is correct, and records the time it took to answer.

[1254] Input: The user's answer sent to the server.

[1255] Output: Learner's comprehension assessment results (correct / incorrect, answer time).

[1256] Step 8:

[1257] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment, and customizes the plan using a generative AI model.

[1258] Specific operation: If the basic fraction questions are all answered correctly, the server creates a plan including applied fraction questions. If there are many incorrect answers, the server creates a plan including "basic fraction calculation drills" and "videos explaining the concept of fractions."

[1259] Input: Learner comprehension assessment results.

[1260] Output: The generated learning plan.

[1261] Step 9:

[1262] The server transmits the generated study plan to the terminal.

[1263] Specific operation: The server sends a learning plan including "Basic drills for fraction calculations" and "Videos explaining the concept of fractions" to the device.

[1264] Input: The generated learning plan.

[1265] Output: The study plan sent to the device.

[1266] Step 10:

[1267] The terminal displays the received learning plan to the user.

[1268] Specific operation: The device displays to the user "Basic fraction calculation drills" and "Video explaining the concept of fractions."

[1269] Input: The study plan sent to your device.

[1270] Output: The learning plan displayed to the user.

[1271] Step 11:

[1272] The user proceeds with their studies based on the presented study plan.

[1273] Specific operations: The user solves the displayed "Basic fraction calculation drills" and watches the "Video explaining the concept of fractions."

[1274] Input: The learning plan displayed to the user.

[1275] Output: User's learning progress.

[1276] (Application example 1)

[1277] 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."

[1278] Conventional learning support systems have the problem of being difficult to individually optimize, fully reflecting each learner's learning stage and weaknesses. This has prevented them from providing efficient and effective learning support. It has also been difficult to perform a detailed analysis of a learner's level of understanding and provide an appropriate learning plan based on that analysis.

[1279] 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.

[1280] In this invention, the server includes means for inputting the learner's educational stage and learning history data, means for generating assessment questions of appropriate difficulty based on the said educational stage and learning history data, means for collecting the learner's answers, analyzing the collected answers and evaluating the learner's level of understanding, means for generating and presenting an optimal learning plan for the learner, means for managing progress, and means for providing individual educational materials. This makes it possible to provide an individual learning plan optimized for the characteristics of each learner, thereby realizing efficient and effective learning support.

[1281] "Learner" refers to an individual in a position to receive education.

[1282] "Educational stage" refers to the grade or learning progression to which a learner is currently assigned.

[1283] "Learning history data" refers to information about a learner's past learning activities and grades.

[1284] "Assessment questions" refer to test questions created to measure a learner's level of understanding and ability.

[1285] "Answer collection means" refers to a function that collects answers provided by learners to assessment questions.

[1286] "Means for assessing comprehension" refers to a function that determines the level of comprehension based on the answers provided by the learner.

[1287] A "learning plan" refers to a plan created to efficiently advance future learning based on the learner's level of understanding and challenges.

[1288] "Progress management means" refers to the function of monitoring and managing a learner's learning situation and progress.

[1289] "Educational materials" refers to teaching materials and reference materials provided to support learners' learning activities.

[1290] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to process data and generate new information or suggestions.

[1291] A "prompt" refers to text that contains instructions or questions to be input into a generative AI model.

[1292] This invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's educational level and learning history data, generates assessment questions of appropriate difficulty based on that data, and generates an individual learning plan by collecting and analyzing the learner's answers. Finally, the system presents the generated learning plan to the learner and manages their progress, thereby providing efficient and effective learning support.

[1293] System Overview

[1294] The system consists of the following main components:

[1295] 1. User Device

[1296] 2. Server

[1297] 1. User Device

[1298] User terminal: A device used by learners (computer, tablet, smartphone, etc.). User terminals provide an interface for learners to input educational stage and learning history data, display generated assessment questions, and enter answers. They also have functions for presenting learning plans and managing progress.

[1299] Examples:

[1300] The learner enters information such as "second year junior high school student" and "not good at mathematical equations" through the user terminal.

[1301] The user terminal transmits the information to the server.

[1302] 2. Server

[1303] Server: The server is a central data processing component. It generates assessment questions based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[1304] Examples:

[1305] The server generates "assessment questions about mathematical equations for second-year junior high school students" and sends them to the user's terminal.

[1306] A learner answers the question "Solve the x in 2x + 3 = 7" with "x = 2."

[1307] Program processing explanation

[1308] User device:

[1309] The user terminal collects the learner's educational stage and learning history data through the input interface and transmits the data to the server. After the learner answers the assessment questions, the answer data is also transmitted to the server.

[1310] server:

[1311] The server generates appropriate assessment questions based on the received data. This is done using a generative AI model. It also analyzes the learner's answers and evaluates their level of understanding. The analysis results are reflected in the learner's learning plan via prompts. The following are example prompts:

[1312] Prompt Sentence Examples

[1313] Based on the data entered, such as "second-year junior high school student" and "not good at mathematical equations," the server generates the following assessment questions.

[1314] - "Solve the x in 2x + 3 = 7"

[1315] Examples:

[1316] The server generates "assessment questions about mathematical equations for second-year junior high school students."

[1317] The user device presents the learner with the problem "Solve the x in 2x + 3 = 7."

[1318] The learner answers "x = 2", and the server receives and evaluates the answer.

[1319] The server also generates an individualized learning plan based on the results of the comprehension assessment and transmits it to the user terminal. The learning plan may include, for example, "drills to strengthen the basics of equations" and "videos explaining equations."

[1320] This allows learners to receive individually optimized learning plans, enabling them to manage their own progress and study effectively.

[1321] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1322] Step 1:

[1323] The user inputs educational stage and learning history data into the user terminal. Examples of input data include "second year junior high school student" and "poor at mathematical equations." This input data is sent to the server by the user terminal. Because the input data includes specific areas of weakness and past grades, the server treats it as basic data for generating individual assessment questions.

[1324] Step 2:

[1325] The server generates appropriate assessment questions based on the received data. Using the generative AI model, it generates assessment questions for the prompts "junior high school second year student" and "not good at math equations." For example, a question like "solve the x in 2x + 3 = 7" is generated. This generated assessment question is sent from the server to the user's device.

[1326] Step 3:

[1327] The user terminal presents the generated assessment questions to the learner. The assessment questions are displayed on the screen of the user terminal, and an interface is provided that allows the learner to answer them.

[1328] Step 4:

[1329] The user inputs an answer to the presented evaluation question. For example, to the question "Solve x in 2x + 3 = 7," the user answers "x = 2." This answer is sent to the server by the user terminal.

[1330] Step 5:

[1331] The server analyzes the received answers. Using the comprehension assessment means, it analyzes whether the answers are correct and the time it took to answer them. For example, if the answer "x = 2" is correct, it is recorded as the correct answer, and the time it took to answer is also recorded. The results of this analysis are used to assess the learner's comprehension.

[1332] Step 6:

[1333] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment. Using a generative AI model, the learning plan is generated based on the prompt. For example, a plan including "Equation Basics Reinforcement Drills" and "Equation Explanation Videos" may be generated. This plan is then sent from the server to the user's device.

[1334] Step 7:

[1335] The user device presents the generated learning plan to the learner. The user device has the function of displaying specific learning activities and reference materials, allowing the learner to check their own progress as they study.

[1336] Step 8:

[1337] The user progresses through their studies based on the presented learning plan. The user's device records the learner's learning activities and sends progress information to the server, allowing the server to continuously monitor the learner's situation and update the learning plan as necessary.

[1338] Through the above processing steps, the learner receives an optimal learning plan based on his / her educational stage and learning history, and can proceed with his / her studies efficiently and effectively.

[1339] 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.

[1340] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, it aims to improve learning efficiency by combining it with an emotion engine that recognizes the user's emotions.

[1341] System Overview

[1342] The system consists of the following main components:

[1343] 1. User Device

[1344] 2. Server

[1345] 3. Emotion Engine

[1346] 1. User Device

[1347] User terminal: A device used by a learner (e.g., computer, tablet, smartphone, etc.). It provides an interface for learners to input grade and learning history data, view generated tests, and enter answers.

[1348] Examples:

[1349] The user uses the terminal to input information such as "sixth grade elementary school student" and "not good at fractions," and this information is sent to the server via the terminal.

[1350] 2. Server

[1351] Server: A central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes comprehension, and creates a learning plan based on the results and provides it to the user.

[1352] Examples:

[1353] The server generates a test for sixth graders in mathematics, Japanese, science, and social studies, and sends it to the device. For example, the test includes a math question such as "What is 3 x 4?"

[1354] 3. Emotion Engine

[1355] Emotion engine: This component uses a camera and microphone to recognize the learner's emotions. The emotion engine analyzes the learner's facial expressions and tone of voice and collects this data to understand their emotional state, such as whether they are feeling stressed or struggling to understand.

[1356] Examples:

[1357] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[1358] Program processing

[1359] The program processing will be explained in detail below in natural language.

[1360] Test Generation

[1361] Server: Generates optimal tests based on grade and learning history data, including multiple question formats.

[1362] For example, based on the input data of "sixth grade elementary school students," create a test that includes the basic math question "What is 3 x 4?"

[1363] Test presentation and answer collection

[1364] Terminal: Presents the generated test to the learner and allows them to enter their answers.

[1365] User: Enters answers to the test questions provided.

[1366] For example, answer "3 x 4 is 12."

[1367] Terminal: Collects the learner's answers and sends the data to the server.

[1368] Data collected along with the answers also includes time data.

[1369] Comprehension analysis and sentiment data collection

[1370] Server: Scores the received answers, analyzes whether they are correct or not, and analyzes the answering time. It also analyzes the emotion data collected from the emotion engine.

[1371] For example, the system associates correct and incorrect answers to the reading of the answer "kan" (Chinese character) with emotional data such as "anxiety" and "concentration" while answering.

[1372] Create a personalized learning plan

[1373] Server: Generates an optimal learning plan for the learner based on both comprehension and emotion data.

[1374] For example, if comprehension is low and the emotion engine identifies "anxiety," the plan might include a break to relax or instructions to start with an easy problem.

[1375] Presentation of study plan

[1376] Terminal: Presents the generated learning plan to the learner.

[1377] User: Proceed with your studies based on the provided learning plan.

[1378] For example, solve the added kanji practice problems and use the provided resources to advance your learning.

[1379] Specific examples

[1380] For example, if a sixth-grade student inputs that they have difficulty with fraction problems, the server uses that information to generate a test that includes basic fraction questions. After the student answers, the emotion engine recognizes states of "anxiety" or "lack of concentration" and reflects these in the analysis. The resulting study plan includes materials to gradually teach the basics of fractions and a stress management plan that includes short breaks.

[1381] In this way, the present invention provides an individually optimized learning experience that also takes into account the emotional state, and becomes a system that more effectively solves conventional problems.

[1382] The following describes the processing flow.

[1383] Processing steps of the program

[1384] The following details the processing steps of the system including the emotion engine.

[1385] Step 1:

[1386] User: Inputs the grade level and learning history data into the terminal.

[1387] For example, inputs information such as "sixth grade in elementary school" and "weak in fractions".

[1388] Step 2:

[1389] Terminal: Transmits the input grade level and learning history data to the server.

[1390] The terminal uses the transmission function to send this information to the server.

[1391] Step 3:

[1392] Server: Based on the received grade level and learning history data, generates an appropriate test using a test generation algorithm.

[1393] Creates test questions such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢" for sixth graders in elementary school.

[1394] Step 4:

[1395] Server: Transmits the generated test to the terminal.

[1396] The created test can be sent to a device so that learners can access it.

[1397] Step 5:

[1398] Terminal: Displays received tests to the user.

[1399] The terminal displays questions on the screen and allows the user to answer them.

[1400] Step 6:

[1401] User: Enters answers to the test questions provided.

[1402] For example, enter "3 x 4 is 12".

[1403] Step 7:

[1404] Terminal: Collects the learner's answers and sends the data to the server.

[1405] The collected answers are packetized and sent to the server.

[1406] Step 8:

[1407] Server: Scores the received answers and analyzes the correctness and time required to answer.

[1408] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[1409] Step 9:

[1410] Emotion Engine: Uses a camera and microphone to analyze the user's facial expressions and tone of voice during testing.

[1411] For example, if a person's facial expression is stern and their voice is low-pitched, this will be perceived as "anxiety."

[1412] Step 10:

[1413] Server: Based on the emotional data obtained from the emotion engine, the learner's emotional state is recorded and used to assess comprehension.

[1414] The results are compared with the scoring results, and for example, the times when the learner felt stressed are recorded.

[1415] Step 11:

[1416] Server: Generates personalized learning plans based on comprehension and emotion data.

[1417] For example, if the emotional data indicates "anxiety," the study plan could include breaks to relax and simple stress-reducing exercises.

[1418] Step 12:

[1419] Server: Sends the generated learning plan to the device.

[1420] Send the created plan to the device so that learners can access it.

[1421] Step 13:

[1422] Device: Display the received learning plan to the user.

[1423] The device displays the learning plan on the screen and guides the user through the next steps.

[1424] Step 14:

[1425] User: Proceed with your studies based on the provided learning plan.

[1426] For example, the system can solve additional kanji practice problems, utilize presented resources to advance learning, and even implement instructions based on emotion data, such as taking breaks.

[1427] Through this series of steps, the system not only provides learners with an efficient and effective learning experience, but also provides them with a personalized learning plan that takes their emotional state into account.

[1428] Example 2

[1429] 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."

[1430] In today's education system, it is difficult to provide an individually optimized learning experience by analyzing each learner's level of understanding and emotional state in detail. In particular, there is still a lack of technology that can recognize a learner's emotional state in real time and adjust the learning plan accordingly. As a result, learners may feel stressed or be left with a low level of understanding.

[1431] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the learner's grade and learning history data; means for generating a test at an appropriate level based on the grade and learning history data; means for presenting the generated test to the learner and collecting the learner's answers; means for evaluating the learner's level of understanding by analyzing the collected answers and answer times; means for recognizing the learner's emotional data and analyzing the emotional data; means for generating an optimal learning plan based on the evaluation results and the emotional data analysis results; and means for presenting the generated learning plan to the learner. This enables an individually optimized learning experience that simultaneously takes into account the learner's level of understanding and emotional state.

[1432] A "learner" is an individual receiving education, specifically someone who uses the system to carry out learning activities.

[1433] "Grade" refers to the stage at which a learner progresses in the educational curriculum.

[1434] "Learning history data" refers to data that records the learning content, grades, and progress of a learner to date.

[1435] A "server" is a part of a computer system that performs central data processing, and is a device that accepts requests from multiple clients and generates appropriate responses.

[1436] A "test" is a set of questions or tasks given to a learner to assess their understanding or knowledge.

[1437] The "test generation means" is a means having the function of automatically creating a test at an appropriate level based on the learner's grade and learning history data.

[1438] "Answer" is the answer or solution provided by the learner to the test.

[1439] The "answer collection means" is a means that has the function of collecting answers given by learners to tests and recording them as data.

[1440] "Response time" is the time taken to respond to one question.

[1441] The "means for assessing comprehension" is a means that has the function of analyzing the collected answers and answer times to assess the learner's level of comprehension.

[1442] "Emotional data" is data that indicates the emotional state of the learner during learning, extracted from the learner's facial expressions, tone of voice, etc.

[1443] "Emotion data recognition means" refers to a means that has the function of recognizing the learner's emotional state through a camera or microphone, and collecting and analyzing that data.

[1444] An "optimal learning plan" is a set of learning strategies and tasks that are most effective for a learner, created based on the learner's comprehension and emotional data.

[1445] The "study plan generation means" is a means having the function of automatically creating an optimal study plan based on the evaluation results and the emotion data analysis results.

[1446] The "study plan presentation means" is a means having the function of presenting the generated study plan to the learner.

[1447] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, the system aims to improve learning efficiency by combining an emotion engine that recognizes the learner's emotions.

[1448] The system consists of the following main components:

[1449] 1. User Device

[1450] 2. Server

[1451] 3. Emotion Engine

[1452] 1. User Device

[1453] A user terminal is a device used by a learner (e.g., a computer, tablet, smartphone, etc.). The terminal provides an interface for inputting the learner's grade and learning history data, and has the functionality to display generated tests and input answers.

[1454] Examples:

[1455] The user uses a terminal to input information such as "sixth grade elementary school student" and "I'm not good at fractions." This information is then sent to the server via the terminal.

[1456] 2. Server

[1457] The server is a central data processing component. It generates tests based on learner data and sends them to the user's device. It also collects answers and analyzes comprehension, and creates and provides a learning plan based on the results.

[1458] Examples:

[1459] The server generates a "sixth grade test" and sends it to the device. For example, the math question includes "What is 3 x 4?"

[1460] 3. Emotion Engine

[1461] The emotion engine is a component that uses a camera and microphone to recognize the learner's emotions. By analyzing the learner's facial expressions and tone of voice and collecting this data, the emotion engine can grasp the learner's emotional state, such as whether they are feeling stressed or struggling to understand.

[1462] Examples:

[1463] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[1464] Program processing

[1465] Next, the processing of the program is explained in detail in natural language.

[1466] Entering User Data

[1467] The terminal provides an interface for the learner to input their grade and learning history data. The learner inputs their grade and weak subjects on the terminal.

[1468] Sending data

[1469] The terminal transmits the learner's input data to the server.

[1470] Test Generation

[1471] The server generates appropriate tests based on the learner's data. The tests include multiple question formats to measure the learner's level of understanding.

[1472] Examples:

[1473] The server selects math problems centered around fractions for sixth grade students who feel they are "not good at fractions," and generates tests that include questions such as "3 / 4 + 1 / 2 = ?"

[1474] Test Delivery

[1475] The server transmits the generated test to the user terminal.

[1476] Presenting the test and collecting answers

[1477] The terminal presents the received test to the learner, who then answers each question on the terminal.

[1478] Submit your answer

[1479] The terminal transmits the learner's answer data to the server.

[1480] Answer analysis

[1481] The server scores the received answers, analyzes the accuracy and time of the answers, and also analyzes the emotion data collected by the emotion engine.

[1482] Examples:

[1483] The server scores the received answer "5 / 4," determines whether it is correct, and analyzes the time it took to answer. At the same time, it recognizes from the emotional data that the learner felt "anxiety" while answering.

[1484] Generate a personalized learning plan

[1485] The server generates an optimal learning plan for the learner based on the answer results and the emotion data analysis results.

[1486] Examples:

[1487] The server generates a study plan that includes videos explaining basic fraction problems for those with low comprehension, links to explanatory articles, and breaks for relaxation.

[1488] Delivery and presentation of learning plans

[1489] The server transmits the generated study plan to the user terminal, and the terminal presents it to the learner.

[1490] Prompt Sentence Examples

[1491] Use the following as a specific prompt:

[1492] "Please generate a test for a sixth-grader who has difficulty with fractions. Also, please suggest an appropriate learning plan based on the emotional data of the answers."

[1493] These prompts allow the generative AI model to generate tests and corresponding personalized study plans focused on specific grade levels and subjects.

[1494] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1495] Step 1:

[1496] The user uses the device to input data such as grade level and learning history. The input information is "sixth grade" and "not good at fractions." The device prepares this data in JSON format and prepares to send it to the server.

[1497] Step 2:

[1498] The terminal sends the grade and learning history data entered by the user to the server. Specifically, when the send button is clicked, the terminal includes the information in the body of an HTTP request and posts it to the server.

[1499] Input: Grade and learning history data (e.g., "6th grade" or "I'm not good at fractions")

[1500] Output: Data sent to the server (JSON format)

[1501] Step 3:

[1502] The server generates appropriate tests based on the received learner data. The server retrieves questions from a database and selects questions according to grade and subject.

[1503] Examples:

[1504] The server generates tests for sixth graders who have difficulty with fractions, including questions such as "3 / 4 + 1 / 2 = ?"

[1505] Input: Grade and learning history data

[1506] Data processing: Extraction of relevant problems from the database

[1507] Output: Generated test data

[1508] Step 4:

[1509] The server sends the generated test to the user's device. The test data is formatted in JSON format and sent as an HTTP response.

[1510] Input: Generated test data

[1511] Output: Test transmission to user device (JSON format)

[1512] Step 5:

[1513] The device presents the received test to the learner. The device has the function of analyzing the test data and displaying the questions on the user interface.

[1514] Input: Test data received from the server

[1515] Output: Display of test questions

[1516] Step 6:

[1517] The user enters answers to the test displayed on the terminal, for example, "3 / 4 + 1 / 2 = 5 / 4."

[1518] Input: Test questions and user answers

[1519] Output: Learner's answer data

[1520] Step 7:

[1521] The terminal sends the answer data entered by the learner to the server, including the answer content and the time it took to answer.

[1522] Input: Learner's answer data

[1523] Output: Send the learner's answer data to the server

[1524] Step 8:

[1525] The server analyzes the received answer data and answer time. It judges whether the answer was correct or incorrect, and evaluates the accuracy of the answer and the time required. It also adds emotional data collected by the emotion engine to the analysis.

[1526] Examples:

[1527] The server scores the answer "5 / 4" and determines whether it is correct. It also recognizes from the emotional data that the learner felt "anxiety" while answering.

[1528] Input: Answer data and emotion data

[1529] Data processing: Correct / incorrect judgment and emotional data analysis

[1530] Output: Analysis results (comprehension assessment and emotional state)

[1531] Step 9:

[1532] The server generates an optimal learning plan for each learner based on the answers and the results of the emotion data analysis. If the learner's level of understanding is deemed low, the server creates a plan that includes supplementary materials, basic questions, and breaks for relaxation.

[1533] Input: Analysis results (comprehension assessment and emotional state)

[1534] Data processing: generating learning plans

[1535] Output: Generated lesson plan

[1536] Step 10:

[1537] The server sends the generated learning plan to the user's device. The learning plan is also formatted in JSON format and sent to the user's device as an HTTP response.

[1538] Input: Generated lesson plan

[1539] Output: Study plan sent to user device (JSON format)

[1540] Step 11:

[1541] The terminal presents the received learning plan to the learner. The terminal analyzes the learning plan and displays it on the user interface. The learner then proceeds with the next learning activity based on the presented plan.

[1542] Input: Study plan received from the server

[1543] Output: Display of learning plan

[1544] The above are the specific processing steps and flow of the entire system.

[1545] (Application example 2)

[1546] 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."

[1547] Conventional learning and factory training systems have had difficulty providing optimal plans that fully consider the learning ability and operational proficiency of individual learners and operators. Furthermore, they were unable to adjust training or learning to take into account the user's emotional state. This resulted in problems such as reduced learning and operational efficiency and increased stress and anxiety.

[1548] 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 generating an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, means for presenting the generated training protocol and collecting operation data, means for collecting emotional data during operation and adjusting the training plan, means for analyzing the level of understanding and operation data and generating an appropriate learning plan, and means for presenting the generated learning plan. This makes it possible to provide optimal learning plans and training plans for individual learners and operators, thereby improving learning efficiency and operation efficiency.

[1549] "Learner" refers to students and educational subjects who are the main participants in learning activities.

[1550] "Grade" is an indicator of the progress of the educational curriculum to which a learner belongs.

[1551] "Learning history data" refers to data that records the learning activities that a learner has undertaken and the results of those activities.

[1552] A "test" is an exam or question set used to assess a learner's understanding or knowledge.

[1553] A "training protocol" is a plan that outlines the procedures and content of the training that operators and learners should carry out.

[1554] "Proficiency" is an indicator of the operator's or learner's level of proficiency in skills and knowledge.

[1555] "Operation history data" refers to data that records the operation activities that an operator has performed up to now and the results of those activities.

[1556] "Emotional data" is data used to collect and analyze the emotional state of learners and operators.

[1557] A "training plan" is a specific learning or training plan established based on the proficiency and emotional state of an operator or learner.

[1558] "Operation data" refers to data that records the details of operations performed by an operator.

[1559] "Analysis results" are the results of analysis conducted based on collected data.

[1560] A "server" is a computer system that centrally processes data and performs various calculations and data generation.

[1561] The present invention is a system that generates an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, and provides an individually customized training plan based on the results. Furthermore, it aims to recognize the emotions of the learner or operator using an emotion engine and adjust the training plan accordingly.

[1562] System Overview

[1563] The system consists of the following main components:

[1564] 1. User Device

[1565] 2. Server

[1566] 3. Emotion Engine

[1567] 1. User Device

[1568] User terminal: A device (e.g., tablet, smartphone) used by an operator or learner. The user terminal provides an interface for inputting proficiency and operation history data, displaying generated training protocols, and collecting operation and emotion data.

[1569] 2. Server

[1570] Server: A central data processing component. It generates training protocols based on the proficiency and operation history data of operators and learners, and sends them to the user's device. It also collects operation and emotion data, and adjusts and generates training plans based on this data, which it then provides to the user.

[1571] 3. Emotion Engine

[1572] Emotion Engine: A component with the ability to recognize the emotions of operators and learners using a camera and microphone. The emotion engine analyzes facial expressions and tone of voice to understand the emotional state of operators and learners.

[1573] Program processing

[1574] Training protocol generation

[1575] The server generates an optimal training protocol based on the operator's or learner's proficiency and past operation history data, which includes multiple operation procedures and training items.

[1576] For example, based on information such as "beginner level" or "difficulty with basic robot operations," a training protocol can be created that progresses from basic arm operations.

[1577] Training presentation and operation data collection

[1578] The user terminal presents the generated training protocol to the operator and collects operation data and emotion data.

[1579] The operator performs the operation according to the protocol, and emotional data (e.g., facial expressions and tone of voice collected by a camera or microphone) is also collected.

[1580] Analysis of operational and sentiment data

[1581] The server scores and analyzes the received operation data, and simultaneously analyzes the emotion data collected from the emotion engine.

[1582] For example, if a particular operation is frequently subject to operational errors and "anxiety" is recognized in the emotional data, that operation will be included as a focus in the next training session.

[1583] Generate personalized training plans

[1584] The server generates optimal training plans for operators and learners based on operation data and emotion data.

[1585] For example, if the emotional data indicates "anxiety," it will create a training plan with increased step-by-step guidance and short breaks to relax.

[1586] Training plan presentation

[1587] The user terminal presents the generated training plan to the operator, allowing them to continue learning and training.

[1588] The operator continues to operate based on the presented training plan, and the results and emotional data are sent back to the server.

[1589] Hardware and software used

[1590] The hardware used includes a smartphone or tablet (user device) with a camera and microphone (for the emotion engine). The software uses a training program generation algorithm for the server and emotion recognition AI (e.g., Microsoft Azure Emotion API).

[1591] Examples and prompts

[1592] For example, here is a specific example of new operator A learning the basics of operating a factory robot:

[1593] Person A uses the control terminal to input "new employee level" and "not good at operating the arm."

[1594] The server generates a training protocol including basic arm operations and sends it to the terminal.

[1595] As Mr. A continues his training, his emotion recognition data is evaluated as "anxiety."

[1596] Based on the analysis, the server will provide more tips for the next training session and add breaks to improve concentration.

[1597] Example prompts to input to the generative AI model

[1598] Enter the new operator's skill level and past operating history.

[1599] Create a specific training protocol based on information such as "rookie level" or "poor arm operation", and generate an individual training plan taking into account emotional data during operation.

[1600] In this way, the present invention provides a personalized, optimized training experience that also takes into account emotional state, improving the operator's learning and operating efficiency.

[1601] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1602] Step 1:

[1603] The user uses a user terminal to input their own proficiency level and past operation history data. The input data includes information such as "rookie level" or "poor at arm operation." All of this data is sent to the server.

[1604] Input: User's skill level and operation history data

[1605] Output: Skill level and operation history data sent to the server

[1606] Step 2:

[1607] The server generates an optimal training protocol based on the received proficiency and operation history data. Using a generative AI model, it creates a specific training protocol while conducting comparative analysis with past training data.

[1608] Input: Skill level and operation history data

[1609] Output: The generated training protocol

[1610] Step 3:

[1611] The server transmits the generated training protocol to the user terminal, which presents the training protocol to the operator.

[1612] Input: Generated training protocol

[1613] Output: The training protocol sent to the user's device.

[1614] Step 4:

[1615] The user performs operations according to the training protocol displayed on the user device. During the operations, the user device collects emotional data (facial expressions, tone of voice, etc.) using a camera and microphone.

[1616] Input: User actions and emotion data

[1617] Output: Collected operation data and emotion data

[1618] Step 5:

[1619] The user terminal transmits the collected operation data and emotion data to the server, which can then perform detailed evaluation and analysis.

[1620] Input: Collected operation data and emotion data

[1621] Output: Operation data and emotion data sent to the server

[1622] Step 6:

[1623] The server analyzes the received operation data, judges the success or failure and analyzes the operation efficiency, and simultaneously analyzes the emotion data to grasp the user's emotional state during operation.

[1624] Input: Operation data and emotion data

[1625] Output: Success / failure judgment, analysis of operational efficiency, and analysis of emotional state

[1626] Step 7:

[1627] The server generates an individual training plan based on the evaluation of the user's operation data and the analysis of the user's emotional data. It adds special guidance for difficult operations and incorporates rest periods for relaxation.

[1628] Input: Success / failure judgment, analysis results of operational efficiency, analysis results of emotional state

[1629] Output: Generated personalized training plan

[1630] Step 8:

[1631] The server transmits the generated individual training plan to the user terminal, which presents the individual training plan to the user.

[1632] Input: Generated personalized training plan

[1633] Output: Individual training plan sent to the user's device

[1634] Step 9:

[1635] The user continues learning or operating in accordance with the individual training plan provided, thereby maximizing the training effect.

[1636] Input: Generated personalized training plan

[1637] Output: User interaction and continued learning

[1638] 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.

[1639] 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.

[1640] 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.

[1641] [Fourth embodiment]

[1642] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1643] 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.

[1644] 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).

[1645] 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.

[1646] 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.

[1647] 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).

[1648] 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. 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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."

[1655] The present invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's grade and learning history data, generates tests at an appropriate level based on the data, and analyzes the test results to create an individual learning plan, thereby providing efficient and effective learning support.

[1656] System Overview

[1657] The system consists of the following main components:

[1658] 1. User Device

[1659] 2. Server

[1660] 1. User Device

[1661] User Device: This is the device used by the learner (e.g., computer, tablet, smartphone, etc.). The user device provides the interface for the learner to enter grade and learning history data, view generated tests, and enter answers.

[1662] Examples:

[1663] The user inputs information such as "sixth grade elementary school student" and "I'm not good at fractions in math" through the terminal.

[1664] The terminal transmits the information to the server.

[1665] 2. Server

[1666] Server: The server is a central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[1667] Examples:

[1668] The server generates "6th grade math, Japanese, science, and social studies tests," including, for example, the math question "What is 3 x 4?"

[1669] The server sends the generated test to the device.

[1670] Program processing steps

[1671] The processing of the program will be explained in natural language below.

[1672] Test Generation

[1673] Server: Obtains the learner's grade and learning history data and generates the most appropriate test based on that.

[1674] For example, if the grade level is "sixth grade," standard test questions for sixth graders will be given.

[1675] Test presentation and answer collection

[1676] Terminal: Displays the test sent from the server to the learner.

[1677] User: Enter the answer to each test question.

[1678] For example, when asked "What is 3 x 4?", the answer is "12."

[1679] Terminal: Collects learners' answers and sends them to the server.

[1680] The terminal sends the answer to the server.

[1681] Comprehension analysis

[1682] Server: Analyzes the submitted answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[1683] For example, if the answer to the question about how to read "kan" is "kan," it is judged to be correct, and if it is "ou," it is judged to be incorrect. The time required for each answer is also recorded.

[1684] Create a personalized learning plan

[1685] Server: Generates the optimal learning plan for the learner based on the results of comprehension analysis.

[1686] For example, if a student's understanding of Japanese kanji is lacking, create a plan that includes additional practice questions and reference materials.

[1687] Server: Sends the generated learning plan to the user's device.

[1688] The server transmits the plan to the terminal and displays it to the learner.

[1689] Presentation of study plan

[1690] Device: Display the sent learning plan to the learner.

[1691] User: Proceed with your learning based on the plan provided.

[1692] Specific examples

[1693] For example, if a sixth-grade student has difficulty with fraction problems, the server will generate "basic fraction problems for sixth-grade students" based on that information. It will analyze the answers and, if there are many incorrect answers, provide an individualized learning plan that includes "basic fraction calculation drills" and "videos explaining the concept of fractions." If the student lacks understanding of how to read kanji, it will add "kanji writing" and "reading practice problems" to the learning plan.

[1694] This allows learners to efficiently overcome their weaknesses and improve their overall understanding. In this way, the present invention provides a learning experience optimized for each learner, effectively resolving conventional problems.

[1695] The following describes the process flow.

[1696] Program processing steps

[1697] Step 1:

[1698] User: Input academic year and learning history data into the terminal.

[1699] For example, input information such as "Sixth grade of primary school" and "Weak in fractions".

[1700] Step 2:

[1701] Terminal: Send the input academic year and learning history data to the server.

[1702] The terminal uses the transmission function to send this information to the server.

[1703] Step 3:

[1704] Server: Based on the received academic year and learning history data, use a test generation algorithm to generate appropriate tests.

[1705] Create test questions such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢" for sixth graders of primary school.

[1706] Step 4:

[1707] Server: Send the generated test to the terminal.

[1708] By sending the created test to the terminal, make it accessible to learners.

[1709] Step 5:

[1710] Terminal: Display the received test to the user.

[1711] The terminal displays questions on the screen and allows the user to answer them.

[1712] Step 6:

[1713] User: Enters answers to the test questions provided.

[1714] For example, enter "3 x 4 is 12".

[1715] Step 7:

[1716] Terminal: Collects the learner's answers and sends the data to the server.

[1717] The collected answers are packetized and sent to the server.

[1718] Step 8:

[1719] Server: Scores the received answers and analyzes the correctness and time required to answer.

[1720] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[1721] Step 9:

[1722] Server: Identify areas of high error and delay and assess learner comprehension.

[1723] For example, if a student gives the wrong answer "ou" to the question about how to read "kanji" many times, this is judged to be a lack of understanding.

[1724] Step 10:

[1725] Server: Generates an individual learning plan based on the results of comprehension analysis.

[1726] For example, if a child lacks understanding of kanji, a plan can be created that recommends the use of "kanji reading drills" or "kanji learning apps."

[1727] Step 11:

[1728] Server: Sends the generated learning plan to the device.

[1729] Send the created plan to the device so that learners can access it.

[1730] Step 12:

[1731] Device: Display the received learning plan to the user.

[1732] The device displays the learning plan on the screen and guides the user through the next steps.

[1733] Step 13:

[1734] User: Proceed with your studies based on the provided learning plan.

[1735] For example, students can study by solving the added kanji problems and using the provided learning resources.

[1736] Through this series of steps, the system provides learners with an efficient and effective learning experience.

[1737] Example 1

[1738] 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."

[1739] Conventional learning support systems have the problem of being unable to easily generate and present test questions and learning plans that correspond to each learner's level of understanding and learning history. This makes it difficult to provide effective learning guidance that is tailored to each learner's weaknesses and learning pace. Furthermore, the inability to provide appropriate feedback quickly makes it difficult to maintain learners' motivation.

[1740] 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.

[1741] In this invention, the server includes a means for inputting the learner's grade and learning history data, a means for generating a test at an appropriate level based on the grade and learning history data, and a means for customizing the test and learning plan using a generative AI model, thereby enabling the generation and presentation of optimal tests and learning plans that meet the individual needs of each learner.

[1742] "Grade" is data that indicates the stage of the educational curriculum to which a learner belongs.

[1743] "Learning history data" refers to data that indicates what a learner has learned so far, their grades, level of understanding, and their learning progress.

[1744] The "test generation means" is a means having the function of creating a test at an appropriate level for a learner based on the input grade and learning history data.

[1745] A "generative AI model" is a technology that uses artificial intelligence to generate and customize optimal test questions and study plans based on a learner's specific learning data.

[1746] The "test presentation means" is a means having a function for displaying the generated test to the learner.

[1747] The "answer collection means" is a means having a function for collecting answers entered by learners to test questions.

[1748] The "means for assessing comprehension" is a means that has the function of assessing the learner's level of comprehension by determining whether the answer is correct or incorrect and the time required to answer based on the answers collected from the learner.

[1749] The "learning plan generation means" is a means having the function of creating an optimal learning plan for each learner based on the results of the comprehension assessment.

[1750] The "study plan presentation means" is a means having a function for displaying the generated study plan to the learner.

[1751] This invention is a system that provides an optimized learning experience for each learner. The system's main hardware components are a user terminal and a server, and it uses a generative AI model to generate test questions and study plans based on the learner's grade and learning history data.

[1752] 1. User Device

[1753] A user terminal is a device used by a learner (for example, a computer, tablet, or smartphone). The user terminal provides an interface for the learner to input their grade and learning history data, view the generated test, and enter their answers. Specifically, the learner inputs information such as "sixth grade of elementary school" and "I'm not good at fractions in math" through the terminal, and the terminal sends this information to the server.

[1754] 2. Server

[1755] The server is a central component that processes data. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device. Specifically, the server generates "mathematics, Japanese, science, and social studies tests for sixth graders," and creates test questions that include, for example, the math question "What is 3 x 4?" The server then sends the generated tests to the device.

[1756] Specific examples of processing

[1757] For example, a learner might use a prompt such as, "I'm in sixth grade and I'm particularly bad at fraction problems. Based on this information, please generate fraction-related test questions." The user enters this information into their device, which then sends it to the server. The server uses a generative AI model to generate a test that includes fraction questions based on this information. For example, it creates a question like, "What is 3 / 5 + 2 / 3?" The generated test is sent to the user's device, and the user answers it.

[1758] The user's device collects the entered answers and sends them back to the server. The server analyzes the answers, determines whether they were correct and analyzes the time it took to answer, and then generates an individual study plan. For example, if there are few correct answers, a study plan including "basic drills for fraction calculations" is generated. The generated study plan is sent to the user's device, and the user proceeds with their studies based on it.

[1759] This makes it possible to provide optimal learning support tailored to each individual learner's level of understanding.The system aims to efficiently overcome learners' weaknesses and promote overall learning progress.

[1760] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1761] Step 1:

[1762] The user inputs grade and learning history data through the terminal.

[1763] Specific operation: The user enters the information "sixth grade of elementary school" and "I'm not good at fractions in math" into the input form on the device.

[1764] Input: Grade information, learning history data.

[1765] Output: Entered grade information and learning history data.

[1766] Step 2:

[1767] The terminal transmits the input data to the server.

[1768] Specific operation: The device sends information such as "sixth grade of elementary school" and "not good at fractions" to the server.

[1769] Input: Grade information, learning history data.

[1770] Output: Grade information and learning history data sent to the server.

[1771] Step 3:

[1772] The server generates appropriate tests based on the received grade and learning history data, using a generative AI model to identify questions.

[1773] Specific operation: The server generates "basic fraction problems for sixth graders," for example, "What is 3 / 5 + 2 / 3?"

[1774] Input: Grade information and learning history data received by the server.

[1775] Output: The generated test questions.

[1776] Step 4:

[1777] The server sends the generated test to the device.

[1778] Specific operation: The server sends the generated fraction problem to the terminal.

[1779] Input: The generated test questions.

[1780] Output: The test question sent to the device.

[1781] Step 5:

[1782] The terminal displays the received test to the user, who then inputs answers to each test question.

[1783] Specific operation: The user enters the answer "15 / 31" in response to the question "What is 3 / 5 + 2 / 3?" displayed on the terminal.

[1784] Input: Test questions displayed on the terminal, user answers.

[1785] Output: The user's answer typed into the terminal.

[1786] Step 6:

[1787] The terminal sends the answer entered by the user to the server.

[1788] Specific operation: The device sends the answer "15 / 31" to the server.

[1789] Input: The user's answer as entered into the terminal.

[1790] Output: The user's answer sent to the server.

[1791] Step 7:

[1792] The server analyzes the received answers and evaluates the learner's level of understanding based on whether the answers were correct or incorrect and the time it took to answer.

[1793] Specific operation: The server confirms that the correct answer to "3 / 5 + 2 / 3" is "15 / 31", determines that the answer is correct, and records the time it took to answer.

[1794] Input: The user's answer sent to the server.

[1795] Output: Learner's comprehension assessment results (correct / incorrect, answer time).

[1796] Step 8:

[1797] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment, and customizes the plan using a generative AI model.

[1798] Specific operation: If the basic fraction questions are all answered correctly, the server creates a plan including applied fraction questions. If there are many incorrect answers, the server creates a plan including "basic fraction calculation drills" and "videos explaining the concept of fractions."

[1799] Input: Learner comprehension assessment results.

[1800] Output: The generated learning plan.

[1801] Step 9:

[1802] The server transmits the generated study plan to the terminal.

[1803] Specific operation: The server sends a learning plan including "Basic drills for fraction calculations" and "Videos explaining the concept of fractions" to the device.

[1804] Input: The generated learning plan.

[1805] Output: The study plan sent to the device.

[1806] Step 10:

[1807] The terminal displays the received learning plan to the user.

[1808] Specific operation: The device displays to the user "Basic fraction calculation drills" and "Video explaining the concept of fractions."

[1809] Input: The study plan sent to your device.

[1810] Output: The learning plan displayed to the user.

[1811] Step 11:

[1812] The user proceeds with their studies based on the presented study plan.

[1813] Specific operations: The user solves the displayed "Basic fraction calculation drills" and watches the "Video explaining the concept of fractions."

[1814] Input: The learning plan displayed to the user.

[1815] Output: User's learning progress.

[1816] (Application example 1)

[1817] 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."

[1818] Conventional learning support systems have the problem of being difficult to individually optimize, fully reflecting each learner's learning stage and weaknesses. This has prevented them from providing efficient and effective learning support. It has also been difficult to perform a detailed analysis of a learner's level of understanding and provide an appropriate learning plan based on that analysis.

[1819] 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.

[1820] In this invention, the server includes means for inputting the learner's educational stage and learning history data, means for generating assessment questions of appropriate difficulty based on the said educational stage and learning history data, means for collecting the learner's answers, analyzing the collected answers and evaluating the learner's level of understanding, means for generating and presenting an optimal learning plan for the learner, means for managing progress, and means for providing individual educational materials. This makes it possible to provide an individual learning plan optimized for the characteristics of each learner, thereby realizing efficient and effective learning support.

[1821] "Learner" refers to an individual in a position to receive education.

[1822] "Educational stage" refers to the grade or learning progression to which a learner is currently assigned.

[1823] "Learning history data" refers to information about a learner's past learning activities and grades.

[1824] "Assessment questions" refer to test questions created to measure a learner's level of understanding and ability.

[1825] "Answer collection means" refers to a function that collects answers provided by learners to assessment questions.

[1826] "Means for assessing comprehension" refers to a function that determines the level of comprehension based on the answers provided by the learner.

[1827] A "learning plan" refers to a plan created to efficiently advance future learning based on the learner's level of understanding and challenges.

[1828] "Progress management means" refers to the function of monitoring and managing a learner's learning situation and progress.

[1829] "Educational materials" refers to teaching materials and reference materials provided to support learners' learning activities.

[1830] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to process data and generate new information or suggestions.

[1831] A "prompt" refers to text that contains instructions or questions to be input into a generative AI model.

[1832] This invention is a system that provides an optimized learning experience for each individual learner. This system inputs the learner's educational level and learning history data, generates assessment questions of appropriate difficulty based on that data, and generates an individual learning plan by collecting and analyzing the learner's answers. Finally, the system presents the generated learning plan to the learner and manages their progress, thereby providing efficient and effective learning support.

[1833] System Overview

[1834] The system consists of the following main components:

[1835] 1. User Device

[1836] 2. Server

[1837] 1. User Device

[1838] User terminal: A device used by learners (computer, tablet, smartphone, etc.). User terminals provide an interface for learners to input educational stage and learning history data, display generated assessment questions, and enter answers. They also have functions for presenting learning plans and managing progress.

[1839] Examples:

[1840] The learner enters information such as "second year junior high school student" and "not good at mathematical equations" through the user terminal.

[1841] The user terminal transmits the information to the server.

[1842] 2. Server

[1843] Server: The server is a central data processing component. It generates assessment questions based on learner information and sends them to the user's device. It also collects answers and analyzes the learner's level of understanding, creates an optimal learning plan based on the results, and provides the plan to the user's device.

[1844] Examples:

[1845] The server generates "assessment questions about mathematical equations for second-year junior high school students" and sends them to the user's terminal.

[1846] A learner answers the question "Solve the x in 2x + 3 = 7" with "x = 2."

[1847] Program processing explanation

[1848] User device:

[1849] The user terminal collects the learner's educational stage and learning history data through the input interface and transmits the data to the server. After the learner answers the assessment questions, the answer data is also transmitted to the server.

[1850] server:

[1851] The server generates appropriate assessment questions based on the received data. This is done using a generative AI model. It also analyzes the learner's answers and evaluates their level of understanding. The analysis results are reflected in the learner's learning plan via prompts. The following are example prompts:

[1852] Prompt Sentence Examples

[1853] Based on the data entered, such as "second-year junior high school student" and "not good at mathematical equations," the server generates the following assessment questions.

[1854] - "Solve the x in 2x + 3 = 7"

[1855] Examples:

[1856] The server generates "assessment questions about mathematical equations for second-year junior high school students."

[1857] The user device presents the learner with the problem "Solve the x in 2x + 3 = 7."

[1858] The learner answers "x = 2", and the server receives and evaluates the answer.

[1859] The server also generates an individualized learning plan based on the results of the comprehension assessment and transmits it to the user terminal. The learning plan may include, for example, "drills to strengthen the basics of equations" and "videos explaining equations."

[1860] This allows learners to receive individually optimized learning plans, enabling them to manage their own progress and study effectively.

[1861] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1862] Step 1:

[1863] The user inputs educational stage and learning history data into the user terminal. Examples of input data include "second year junior high school student" and "poor at mathematical equations." This input data is sent to the server by the user terminal. Because the input data includes specific areas of weakness and past grades, the server treats it as basic data for generating individual assessment questions.

[1864] Step 2:

[1865] The server generates appropriate assessment questions based on the received data. Using the generative AI model, it generates assessment questions for the prompts "junior high school second year student" and "not good at math equations." For example, a question like "solve the x in 2x + 3 = 7" is generated. This generated assessment question is sent from the server to the user's device.

[1866] Step 3:

[1867] The user terminal presents the generated assessment questions to the learner. The assessment questions are displayed on the screen of the user terminal, and an interface is provided that allows the learner to answer them.

[1868] Step 4:

[1869] The user inputs an answer to the presented evaluation question. For example, to the question "Solve x in 2x + 3 = 7," the user answers "x = 2." This answer is sent to the server by the user terminal.

[1870] Step 5:

[1871] The server analyzes the received answers. Using the comprehension assessment means, it analyzes whether the answers are correct and the time it took to answer them. For example, if the answer "x = 2" is correct, it is recorded as the correct answer, and the time it took to answer is also recorded. The results of this analysis are used to assess the learner's comprehension.

[1872] Step 6:

[1873] The server generates an optimal learning plan for each learner based on the results of the comprehension assessment. Using a generative AI model, the learning plan is generated based on the prompt. For example, a plan including "Equation Basics Reinforcement Drills" and "Equation Explanation Videos" may be generated. This plan is then sent from the server to the user's device.

[1874] Step 7:

[1875] The user device presents the generated learning plan to the learner. The user device has the function of displaying specific learning activities and reference materials, allowing the learner to check their own progress as they study.

[1876] Step 8:

[1877] The user progresses through their studies based on the presented learning plan. The user's device records the learner's learning activities and sends progress information to the server, allowing the server to continuously monitor the learner's situation and update the learning plan as necessary.

[1878] Through the above processing steps, the learner receives an optimal learning plan based on his / her educational stage and learning history, and can proceed with his / her studies efficiently and effectively.

[1879] 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.

[1880] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, it aims to improve learning efficiency by combining it with an emotion engine that recognizes the user's emotions.

[1881] System Overview

[1882] The system consists of the following main components:

[1883] 1. User Device

[1884] 2. Server

[1885] 3. Emotion Engine

[1886] 1. User Device

[1887] User terminal: A device used by a learner (e.g., computer, tablet, smartphone, etc.). It provides an interface for learners to input grade and learning history data, view generated tests, and enter answers.

[1888] Examples:

[1889] The user uses the terminal to input information such as "sixth grade elementary school student" and "not good at fractions," and this information is sent to the server via the terminal.

[1890] 2. Server

[1891] Server: A central data processing component. It generates tests based on learner information and sends them to the user's device. It also collects answers and analyzes comprehension, and creates a learning plan based on the results and provides it to the user.

[1892] Examples:

[1893] The server generates a test for sixth graders in mathematics, Japanese, science, and social studies, and sends it to the device. For example, the test includes a math question such as "What is 3 x 4?"

[1894] 3. Emotion Engine

[1895] Emotion engine: This component uses a camera and microphone to recognize the learner's emotions. The emotion engine analyzes the learner's facial expressions and tone of voice and collects this data to understand their emotional state, such as whether they are feeling stressed or struggling to understand.

[1896] Examples:

[1897] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[1898] Program processing

[1899] The program processing will be explained in detail below in natural language.

[1900] Test Generation

[1901] Server: Generates optimal tests based on grade and learning history data, including multiple question formats.

[1902] For example, based on the input data of "sixth grade elementary school students," create a test that includes the basic math question "What is 3 x 4?"

[1903] Test presentation and answer collection

[1904] Terminal: Presents the generated test to the learner and allows them to enter their answers.

[1905] User: Enters answers to the test questions provided.

[1906] For example, answer "3 x 4 is 12."

[1907] Terminal: Collects the learner's answers and sends the data to the server.

[1908] Data collected along with the answers also includes time data.

[1909] Comprehension analysis and sentiment data collection

[1910] Server: Scores the received answers, analyzes whether they are correct or not, and analyzes the answering time. It also analyzes the emotion data collected from the emotion engine.

[1911] For example, the system associates correct and incorrect answers to the reading of the answer "kan" (Chinese character) with emotional data such as "anxiety" and "concentration" while answering.

[1912] Create a personalized learning plan

[1913] Server: Generates an optimal learning plan for the learner based on both comprehension and emotion data.

[1914] For example, if comprehension is low and the emotion engine identifies "anxiety," the plan might include a break to relax or instructions to start with an easy problem.

[1915] Presentation of study plan

[1916] Terminal: Presents the generated learning plan to the learner.

[1917] User: Proceed with your studies based on the provided learning plan.

[1918] For example, solve the added kanji practice problems and use the provided resources to advance your learning.

[1919] Specific examples

[1920] For example, if a sixth-grade student inputs that they have difficulty with fraction problems, the server uses that information to generate a test that includes basic fraction questions. After the student answers, the emotion engine recognizes states of "anxiety" or "lack of concentration" and reflects these in the analysis. The resulting study plan includes materials to gradually teach the basics of fractions and a stress management plan that includes short breaks.

[1921] In this way, the present invention provides a personalized learning experience that also takes emotional state into account, making it a more effective system for solving traditional problems.

[1922] The processing flow will be explained below.

[1923] Program processing steps

[1924] The processing steps of a system including an emotion engine are described in detail below.

[1925] Step 1:

[1926] User: Enter grade and learning history data into the terminal.

[1927] For example, enter information such as "Sixth grader in elementary school" and "Weak in fractions".

[1928] Step 2:

[1929] Terminal: Send the entered grade and learning history data to the server.

[1930] The terminal uses the transmission function to send this information to the server.

[1931] Step 3:

[1932] Server: Based on the received grade and learning history data, use a test generation algorithm to generate an appropriate test.

[1933] Create test questions for sixth graders in elementary school, such as "What is 3×4?" and "Write the reading of the following Chinese character: 漢".

[1934] Step 4:

[1935] Server: Send the generated test to the terminal.

[1936] By sending the created test to the terminal, make it accessible to the learner.

[1937] Step 5:

[1938] Terminal: Display the received test to the user.

[1939] The terminal displays the questions on the screen so that the user can answer them.

[1940] Step 6:

[1941] User: Enter an answer to the presented test question.

[1942] For example, enter "3×4 is 12".

[1943] Step 7:

[1944] Terminal: Collects the learner's answers and sends the data to the server.

[1945] The collected answers are packetized and sent to the server.

[1946] Step 8:

[1947] Server: Scores the received answers and analyzes the correctness and time required to answer.

[1948] For example, if the answer is "3 x 4 is 12," this is recorded as a correct answer and the time taken to answer is measured.

[1949] Step 9:

[1950] Emotion Engine: Uses a camera and microphone to analyze the user's facial expressions and tone of voice during testing.

[1951] For example, if a person's facial expression is stern and their voice is low-pitched, this will be perceived as "anxiety."

[1952] Step 10:

[1953] Server: Based on the emotional data obtained from the emotion engine, the learner's emotional state is recorded and used to assess comprehension.

[1954] The results are compared with the scoring results, and for example, the times when the learner felt stressed are recorded.

[1955] Step 11:

[1956] Server: Generates personalized learning plans based on comprehension and emotion data.

[1957] For example, if the emotional data indicates "anxiety," the study plan could include breaks to relax and simple stress-reducing exercises.

[1958] Step 12:

[1959] Server: Sends the generated learning plan to the device.

[1960] Send the created plan to the device so that learners can access it.

[1961] Step 13:

[1962] Device: Display the received learning plan to the user.

[1963] The device displays the learning plan on the screen and guides the user through the next steps.

[1964] Step 14:

[1965] User: Proceed with your studies based on the provided learning plan.

[1966] For example, the system can solve additional kanji practice problems, utilize presented resources to advance learning, and even implement instructions based on emotion data, such as taking breaks.

[1967] Through this series of steps, the system not only provides learners with an efficient and effective learning experience, but also provides them with a personalized learning plan that takes their emotional state into account.

[1968] Example 2

[1969] 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."

[1970] In today's education system, it is difficult to provide an individually optimized learning experience by analyzing each learner's level of understanding and emotional state in detail. In particular, there is still a lack of technology that can recognize a learner's emotional state in real time and adjust the learning plan accordingly. As a result, learners may feel stressed or be left with a low level of understanding.

[1971] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the learner's grade and learning history data; means for generating a test at an appropriate level based on the grade and learning history data; means for presenting the generated test to the learner and collecting the learner's answers; means for evaluating the learner's level of understanding by analyzing the collected answers and answer times; means for recognizing the learner's emotional data and analyzing the emotional data; means for generating an optimal learning plan based on the evaluation results and the emotional data analysis results; and means for presenting the generated learning plan to the learner. This enables an individually optimized learning experience that simultaneously takes into account the learner's level of understanding and emotional state.

[1972] A "learner" is an individual receiving education, specifically someone who uses the system to carry out learning activities.

[1973] "Grade" refers to the stage at which a learner progresses in the educational curriculum.

[1974] "Learning history data" refers to data that records the learning content, grades, and progress of a learner to date.

[1975] A "server" is a part of a computer system that performs central data processing, and is a device that accepts requests from multiple clients and generates appropriate responses.

[1976] A "test" is a set of questions or tasks given to a learner to assess their understanding or knowledge.

[1977] The "test generation means" is a means having the function of automatically creating a test at an appropriate level based on the learner's grade and learning history data.

[1978] "Answer" is the answer or solution provided by the learner to the test.

[1979] The "answer collection means" is a means that has the function of collecting answers given by learners to tests and recording them as data.

[1980] "Response time" is the time taken to respond to one question.

[1981] The "means for assessing comprehension" is a means that has the function of analyzing the collected answers and answer times to assess the learner's level of comprehension.

[1982] "Emotional data" is data that indicates the emotional state of the learner during learning, extracted from the learner's facial expressions, tone of voice, etc.

[1983] "Emotion data recognition means" refers to a means that has the function of recognizing the learner's emotional state through a camera or microphone, and collecting and analyzing that data.

[1984] An "optimal learning plan" is a set of learning strategies and tasks that are most effective for a learner, created based on the learner's comprehension and emotional data.

[1985] The "study plan generation means" is a means having the function of automatically creating an optimal study plan based on the evaluation results and the emotion data analysis results.

[1986] The "study plan presentation means" is a means having the function of presenting the generated study plan to the learner.

[1987] This system generates appropriate tests based on the learner's grade and learning history data, analyzes the answers, and provides individualized learning plans. Furthermore, the system aims to improve learning efficiency by combining an emotion engine that recognizes the learner's emotions.

[1988] The system consists of the following main components:

[1989] 1. User Device

[1990] 2. Server

[1991] 3. Emotion Engine

[1992] 1. User Device

[1993] A user terminal is a device used by a learner (e.g., a computer, tablet, smartphone, etc.). The terminal provides an interface for inputting the learner's grade and learning history data, and has the functionality to display generated tests and input answers.

[1994] Examples:

[1995] The user uses a terminal to input information such as "sixth grade elementary school student" and "I'm not good at fractions." This information is then sent to the server via the terminal.

[1996] 2. Server

[1997] The server is a central data processing component. It generates tests based on learner data and sends them to the user's device. It also collects answers and analyzes comprehension, and creates and provides a learning plan based on the results.

[1998] Examples:

[1999] The server generates a "sixth grade test" and sends it to the device. For example, the math question includes "What is 3 x 4?"

[2000] 3. Emotion Engine

[2001] The emotion engine is a component that uses a camera and microphone to recognize the learner's emotions. By analyzing the learner's facial expressions and tone of voice and collecting this data, the emotion engine can grasp the learner's emotional state, such as whether they are feeling stressed or struggling to understand.

[2002] Examples:

[2003] If a learner's facial expression on camera becomes stern or their voice tone becomes lower during the test, the emotion engine will recognize this state as "anxiety."

[2004] Program processing

[2005] Next, the processing of the program is explained in detail in natural language.

[2006] Entering User Data

[2007] The terminal provides an interface for the learner to input their grade and learning history data. The learner inputs their grade and weak subjects on the terminal.

[2008] Sending data

[2009] The terminal transmits the learner's input data to the server.

[2010] Test Generation

[2011] The server generates appropriate tests based on the learner's data. The tests include multiple question formats to measure the learner's level of understanding.

[2012] Examples:

[2013] The server selects math problems centered around fractions for sixth grade students who feel they are "not good at fractions," and generates tests that include questions such as "3 / 4 + 1 / 2 = ?"

[2014] Test Delivery

[2015] The server transmits the generated test to the user terminal.

[2016] Presenting the test and collecting answers

[2017] The terminal presents the received test to the learner, who then answers each question on the terminal.

[2018] Submit your answer

[2019] The terminal transmits the learner's answer data to the server.

[2020] Answer analysis

[2021] The server scores the received answers, analyzes the accuracy and time of the answers, and also analyzes the emotion data collected by the emotion engine.

[2022] Examples:

[2023] The server scores the received answer "5 / 4," determines whether it is correct, and analyzes the time it took to answer. At the same time, it recognizes from the emotional data that the learner felt "anxiety" while answering.

[2024] Generate a personalized learning plan

[2025] The server generates an optimal learning plan for the learner based on the answer results and the emotion data analysis results.

[2026] Examples:

[2027] The server generates a study plan that includes videos explaining basic fraction problems for those with low comprehension, links to explanatory articles, and breaks for relaxation.

[2028] Delivery and presentation of learning plans

[2029] The server transmits the generated study plan to the user terminal, and the terminal presents it to the learner.

[2030] Prompt Sentence Examples

[2031] Use the following as a specific prompt:

[2032] "Please generate a test for a sixth-grader who has difficulty with fractions. Also, please suggest an appropriate learning plan based on the emotional data of the answers."

[2033] These prompts allow the generative AI model to generate tests and corresponding personalized study plans focused on specific grade levels and subjects.

[2034] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2035] Step 1:

[2036] The user uses the device to input data such as grade level and learning history. The input information is "sixth grade" and "not good at fractions." The device prepares this data in JSON format and prepares to send it to the server.

[2037] Step 2:

[2038] The terminal sends the grade and learning history data entered by the user to the server. Specifically, when the send button is clicked, the terminal includes the information in the body of an HTTP request and posts it to the server.

[2039] Input: Grade and learning history data (e.g., "6th grade" or "I'm not good at fractions")

[2040] Output: Data sent to the server (JSON format)

[2041] Step 3:

[2042] The server generates appropriate tests based on the received learner data. The server retrieves questions from a database and selects questions according to grade and subject.

[2043] Examples:

[2044] The server generates tests for sixth graders who have difficulty with fractions, including questions such as "3 / 4 + 1 / 2 = ?"

[2045] Input: Grade and learning history data

[2046] Data processing: Extraction of relevant problems from the database

[2047] Output: Generated test data

[2048] Step 4:

[2049] The server sends the generated test to the user's device. The test data is formatted in JSON format and sent as an HTTP response.

[2050] Input: Generated test data

[2051] Output: Test transmission to user device (JSON format)

[2052] Step 5:

[2053] The device presents the received test to the learner. The device has the function of analyzing the test data and displaying the questions on the user interface.

[2054] Input: Test data received from the server

[2055] Output: Display of test questions

[2056] Step 6:

[2057] The user enters answers to the test displayed on the terminal, for example, "3 / 4 + 1 / 2 = 5 / 4."

[2058] Input: Test questions and user answers

[2059] Output: Learner's answer data

[2060] Step 7:

[2061] The terminal sends the answer data entered by the learner to the server, including the answer content and the time it took to answer.

[2062] Input: Learner's answer data

[2063] Output: Send the learner's answer data to the server

[2064] Step 8:

[2065] The server analyzes the received answer data and answer time. It judges whether the answer was correct or incorrect, and evaluates the accuracy of the answer and the time required. It also adds emotional data collected by the emotion engine to the analysis.

[2066] Examples:

[2067] The server scores the answer "5 / 4" and determines whether it is correct. It also recognizes from the emotional data that the learner felt "anxiety" while answering.

[2068] Input: Answer data and emotion data

[2069] Data processing: Correct / incorrect judgment and emotional data analysis

[2070] Output: Analysis results (comprehension assessment and emotional state)

[2071] Step 9:

[2072] The server generates an optimal learning plan for each learner based on the answers and the results of the emotion data analysis. If the learner's level of understanding is deemed low, the server creates a plan that includes supplementary materials, basic questions, and breaks for relaxation.

[2073] Input: Analysis results (comprehension assessment and emotional state)

[2074] Data processing: generating learning plans

[2075] Output: Generated lesson plan

[2076] Step 10:

[2077] The server sends the generated learning plan to the user's device. The learning plan is also formatted in JSON format and sent to the user's device as an HTTP response.

[2078] Input: Generated lesson plan

[2079] Output: Study plan sent to user device (JSON format)

[2080] Step 11:

[2081] The terminal presents the received learning plan to the learner. The terminal analyzes the learning plan and displays it on the user interface. The learner then proceeds with the next learning activity based on the presented plan.

[2082] Input: Study plan received from the server

[2083] Output: Display of learning plan

[2084] The above are the specific processing steps and flow of the entire system.

[2085] (Application example 2)

[2086] 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."

[2087] Conventional learning and factory training systems have had difficulty providing optimal plans that fully consider the learning ability and operational proficiency of individual learners and operators. Furthermore, they were unable to adjust training or learning to take into account the user's emotional state. This resulted in problems such as reduced learning and operational efficiency and increased stress and anxiety.

[2088] 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 generating an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, means for presenting the generated training protocol and collecting operation data, means for collecting emotional data during operation and adjusting the training plan, means for analyzing the level of understanding and operation data and generating an appropriate learning plan, and means for presenting the generated learning plan. This makes it possible to provide optimal learning plans and training plans for individual learners and operators, thereby improving learning efficiency and operation efficiency.

[2089] "Learner" refers to students and educational subjects who are the main participants in learning activities.

[2090] "Grade" is an indicator of the progress of the educational curriculum to which a learner belongs.

[2091] "Learning history data" refers to data that records the learning activities that a learner has undertaken and the results of those activities.

[2092] A "test" is an exam or question set used to assess a learner's understanding or knowledge.

[2093] A "training protocol" is a plan that outlines the procedures and content of the training that operators and learners should carry out.

[2094] "Proficiency" is an indicator of the operator's or learner's level of proficiency in skills and knowledge.

[2095] "Operation history data" refers to data that records the operation activities that an operator has performed up to now and the results of those activities.

[2096] "Emotional data" is data used to collect and analyze the emotional state of learners and operators.

[2097] A "training plan" is a specific learning or training plan established based on the proficiency and emotional state of an operator or learner.

[2098] "Operation data" refers to data that records the details of operations performed by an operator.

[2099] "Analysis results" are the results of analysis conducted based on collected data.

[2100] A "server" is a computer system that centrally processes data and performs various calculations and data generation.

[2101] The present invention is a system that generates an optimal training protocol based on the proficiency level of a learner or operator and past operation history data, and provides an individually customized training plan based on the results. Furthermore, it aims to recognize the emotions of the learner or operator using an emotion engine and adjust the training plan accordingly.

[2102] System Overview

[2103] The system consists of the following main components:

[2104] 1. User Device

[2105] 2. Server

[2106] 3. Emotion Engine

[2107] 1. User Device

[2108] User terminal: A device (e.g., tablet, smartphone) used by an operator or learner. The user terminal provides an interface for inputting proficiency and operation history data, displaying generated training protocols, and collecting operation and emotion data.

[2109] 2. Server

[2110] Server: A central data processing component. It generates training protocols based on the proficiency and operation history data of operators and learners, and sends them to the user's device. It also collects operation and emotion data, and adjusts and generates training plans based on this data, which it then provides to the user.

[2111] 3. Emotion Engine

[2112] Emotion Engine: A component with the ability to recognize the emotions of operators and learners using a camera and microphone. The emotion engine analyzes facial expressions and tone of voice to understand the emotional state of operators and learners.

[2113] Program processing

[2114] Training protocol generation

[2115] The server generates an optimal training protocol based on the operator's or learner's proficiency and past operation history data, which includes multiple operation procedures and training items.

[2116] For example, based on information such as "beginner level" or "difficulty with basic robot operations," a training protocol can be created that progresses from basic arm operations.

[2117] Training presentation and operation data collection

[2118] The user terminal presents the generated training protocol to the operator and collects operation data and emotion data.

[2119] The operator performs the operation according to the protocol, and emotional data (e.g., facial expressions and tone of voice collected by a camera or microphone) is also collected.

[2120] Analysis of operational and sentiment data

[2121] The server scores and analyzes the received operation data, and simultaneously analyzes the emotion data collected from the emotion engine.

[2122] For example, if a particular operation is frequently subject to operational errors and "anxiety" is recognized in the emotional data, that operation will be included as a focus in the next training session.

[2123] Generate personalized training plans

[2124] The server generates optimal training plans for operators and learners based on operation data and emotion data.

[2125] For example, if the emotional data indicates "anxiety," it will create a training plan with increased step-by-step guidance and short breaks to relax.

[2126] Training plan presentation

[2127] The user terminal presents the generated training plan to the operator, allowing them to continue learning and training.

[2128] The operator continues to operate based on the presented training plan, and the results and emotional data are sent back to the server.

[2129] Hardware and software used

[2130] The hardware used includes a smartphone or tablet (user device) with a camera and microphone (for the emotion engine). The software uses a training program generation algorithm for the server and emotion recognition AI (e.g., Microsoft Azure Emotion API).

[2131] Examples and prompts

[2132] For example, here is a specific example of new operator A learning the basics of operating a factory robot:

[2133] Person A uses the control terminal to input "new employee level" and "not good at operating the arm."

[2134] The server generates a training protocol including basic arm operations and sends it to the terminal.

[2135] As Mr. A continues his training, his emotion recognition data is evaluated as "anxiety."

[2136] Based on the analysis, the server will provide more tips for the next training session and add breaks to improve concentration.

[2137] Example prompts to input to the generative AI model

[2138] Enter the new operator's skill level and past operating history.

[2139] Create a specific training protocol based on information such as "rookie level" or "poor arm operation", and generate an individual training plan taking into account emotional data during operation.

[2140] In this way, the present invention provides a personalized, optimized training experience that also takes into account emotional state, improving the operator's learning and operating efficiency.

[2141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2142] Step 1:

[2143] The user uses a user terminal to input their own proficiency level and past operation history data. The input data includes information such as "rookie level" or "poor at arm operation." All of this data is sent to the server.

[2144] Input: User's skill level and operation history data

[2145] Output: Skill level and operation history data sent to the server

[2146] Step 2:

[2147] The server generates an optimal training protocol based on the received proficiency and operation history data. Using a generative AI model, it creates a specific training protocol while conducting comparative analysis with past training data.

[2148] Input: Skill level and operation history data

[2149] Output: The generated training protocol

[2150] Step 3:

[2151] The server transmits the generated training protocol to the user terminal, which presents the training protocol to the operator.

[2152] Input: Generated training protocol

[2153] Output: The training protocol sent to the user's device.

[2154] Step 4:

[2155] The user performs operations according to the training protocol displayed on the user device. During the operations, the user device collects emotional data (facial expressions, tone of voice, etc.) using a camera and microphone.

[2156] Input: User actions and emotion data

[2157] Output: Collected operation data and emotion data

[2158] Step 5:

[2159] The user terminal transmits the collected operation data and emotion data to the server, which can then perform detailed evaluation and analysis.

[2160] Input: Collected operation data and emotion data

[2161] Output: Operation data and emotion data sent to the server

[2162] Step 6:

[2163] The server analyzes the received operation data, judges the success or failure and analyzes the operation efficiency, and simultaneously analyzes the emotion data to grasp the user's emotional state during operation.

[2164] Input: Operation data and emotion data

[2165] Output: Success / failure judgment, analysis of operational efficiency, and analysis of emotional state

[2166] Step 7:

[2167] The server generates an individual training plan based on the evaluation of the user's operation data and the analysis of the user's emotional data. It adds special guidance for difficult operations and incorporates rest periods for relaxation.

[2168] Input: Success / failure judgment, analysis results of operational efficiency, analysis results of emotional state

[2169] Output: Generated personalized training plan

[2170] Step 8:

[2171] The server transmits the generated individual training plan to the user terminal, which presents the individual training plan to the user.

[2172] Input: Generated personalized training plan

[2173] Output: Individual training plan sent to the user's device

[2174] Step 9:

[2175] The user continues learning or operating in accordance with the individual training plan provided, thereby maximizing the training effect.

[2176] Input: Generated personalized training plan

[2177] Output: User interaction and continued learning

[2178] 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.

[2179] 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.

[2180] 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.

[2181] 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.

[2182] 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.

[2183] 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.

[2184] 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).

[2185] 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.

[2186] 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."

[2187] 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.

[2188] 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).

[2189] 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.

[2190] 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.

[2191] 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.

[2192] 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.

[2193] 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.

[2194] 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.

[2195] 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.

[2196] 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.

[2197] 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.

[2198] 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.

[2199] The following is further disclosed regarding the above embodiment.

[2200] (Claim 1)

[2201] a means for inputting the learner's grade and learning history data;

[2202] means for generating an appropriate level test based on said grade and learning history data;

[2203] means for presenting the generated test to a learner;

[2204] a means of collecting learner responses;

[2205] means for analyzing the collected answers and assessing the learner's level of understanding;

[2206] means for generating an optimal learning plan for the learner based on the evaluation results;

[2207] means for presenting the generated learning plan to a learner;

[2208] A system including:

[2209] (Claim 2)

[2210] 2. The system according to claim 1, wherein the test generation means generates a test including a plurality of question formats.

[2211] (Claim 3)

[2212] 2. The system according to claim 1, wherein the understanding assessment means is a means for determining whether an answer given by a learner is correct or incorrect and analyzing the answering time.

[2213] "Example 1"

[2214] (Claim 1)

[2215] a means for inputting the learner's grade and learning history data;

[2216] means for generating an appropriate level test based on said grade and learning history data;

[2217] means for presenting the generated test to a learner;

[2218] a means of collecting learner responses;

[2219] means for analyzing the collected answers and assessing the learner's level of understanding;

[2220] means for generating an optimal learning plan for the learner based on the evaluation results;

[2221] means for presenting the generated learning plan to a learner;

[2222] means for customizing said test and study plan using a generative AI model;

[2223] A system including:

[2224] (Claim 2)

[2225] 2. The system according to claim 1, wherein the test generation means generates a test including a plurality of question formats.

[2226] (Claim 3)

[2227] 2. The system according to claim 1, wherein the understanding assessment means is a means for determining whether an answer given by a learner is correct or incorrect and analyzing the answering time.

[2228] "Application Example 1"

[2229] (Claim 1)

[2230] a means for inputting the learner's educational stage and learning history data;

[2231] means for generating assessment questions of appropriate difficulty based on the educational stage and learning history data;

[2232] means for presenting the generated assessment questions to a learner;

[2233] a means of collecting learner responses;

[2234] a means for analyzing the collected answers and assessing the learner's level of understanding;

[2235] means for generating an optimal learning plan for the learner based on the evaluation results;

[2236] a means for presenting the generated learning plan to the learner and managing the progress;

[2237] means for providing individualized educational materials based on said learning plan;

[2238] A system including:

[2239] (Claim 2)

[2240] The system according to claim 1, which is a means for generating a learning plan including reinforcement materials and explanatory videos for areas where the learner is weak.

[2241] (Claim 3)

[2242] The system of claim 1, wherein the answer collection means is a means for determining whether the learner's answers are correct or incorrect and analyzing the answer time, and a means for evaluating the learner's proficiency level using prompt sentences using a generative AI model.

[2243] "Example 2: Combining Emotion Engines"

[2244] (Claim 1)

[2245] a means for inputting the learner's grade and learning history data;

[2246] means for generating an appropriate level test based on said grade and learning history data;

[2247] means for presenting the generated test to a learner and collecting the learner's answers;

[2248] a means for analyzing the collected answers and answer times to evaluate the learner's understanding;

[2249] means for recognizing and analyzing learner emotional data;

[2250] means for generating an optimal learning plan based on the evaluation results and emotion data analysis results;

[2251] means for presenting the generated learning plan to a learner;

[2252] A system including:

[2253] (Claim 2)

[2254] 2. The system according to claim 1, wherein the test generation means generates a test including a plurality of question formats.

[2255] (Claim 3)

[2256] 2. The system according to claim 1, wherein the understanding assessment means is a means for determining whether an answer given by a learner is correct or incorrect and analyzing the answering time.

[2257] (Claim 4)

[2258] 2. The system according to claim 1, wherein the emotion data recognition means is a means for evaluating the emotion of the learner by analyzing the learner's facial expression and tone of voice.

[2259] "Application example 2 when combining emotion engines"

[2260] Claims: Rewritten

[2261] (Claim 1)

[2262] a means for inputting the learner's grade and learning history data;

[2263] means for generating an appropriate level test based on said grade and learning history data;

[2264] means for presenting the generated test to a learner;

[2265] a means of collecting learner responses;

[2266] means for analyzing the collected answers and assessing the learner's level of understanding;

[2267] A means for generating an optimal training protocol based on the operator's proficiency and past operation history data;

[2268] means for presenting the generated training protocol and collecting operation data;

[2269] A means for collecting emotional data during operation and adjusting the training plan;

[2270] A means for generating an optimal training plan for the operator based on the analysis results;

[2271] means for presenting the generated training plan to an operator;

[2272] A system including:

[2273] (Claim 2)

[2274] 2. The system according to claim 1, wherein the test generation means generates a test including a plurality of question formats.

[2275] (Claim 3)

[2276] 2. The system according to claim 1, wherein the understanding assessment means is a means for determining whether the learner's answers are correct or incorrect and analyzing the answering time, and the emotion data analysis means is a means for analyzing emotion data and evaluating the effectiveness of operations. [Explanation of symbols]

[2277] 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. a means for inputting the learner's grade and learning history data; means for generating an appropriate level test based on said grade and learning history data; means for presenting the generated test to a learner; a means of collecting learner responses; means for analyzing the collected answers and assessing the learner's level of understanding; means for generating an optimal learning plan for the learner based on the evaluation results; means for presenting the generated learning plan to a learner; A system including:

2. 2. The system according to claim 1, wherein the test generating means generates a test including a plurality of question types.

3. 2. The system according to claim 1, wherein said understanding level assessment means is means for determining whether an answer given by a learner is correct or incorrect and analyzing the answering time.

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