system

Abstract

Provide a system. A means for learners to input their learning style, abilities, interests, and rate of progress; A means for learners to independently add problems they are weak at, A means for analyzing submitted problems to the server and identifying similar problems and their explanations; means for the server to generate a customized learning plan based on the learner's learning profile and the questions entered; means for enabling the terminal to enable the learner to work on the learning plan provided by the server; A means to assess learners' answers and provide real-time feedback Including system.

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] In recent years, the demand for online education has rapidly increased, but conventional systems have difficulty providing a personalized learning experience that matches each learner's learning style, ability, and progress speed. Furthermore, mechanisms for automatically providing content that allows learners to focus on areas they are weak at or problems they were unable to solve are inadequate. This prevents learners from progressing effectively and efficiently, resulting in inconsistent learning outcomes. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following system: a system including a means for a learner to input their own learning style, ability, interests, and progress speed, a means for the learner to input questions that they are weak at, a means for a server to analyze the input questions and identify similar questions and their explanations, a means for the server to generate a customized study plan based on the learner's learning profile and the input questions, a means for a terminal to enable the learner to work on the study plan provided by the server, and a means for evaluating the learner's answers and providing feedback in real time. This makes it possible to provide a personalized learning experience that is suited to each learner and improve learning efficiency and results.

[0006] A "learning style" is a particular method or approach that a learner uses to most effectively understand and retain information.

[0007] "Competence" is the total amount of knowledge and skills a learner has to solve a particular learning task or problem.

[0008] "Interest" refers to the curiosity or interest a learner has in a particular area or topic.

[0009] "Progression rate" refers to the pace at which a learner comprehends and masters the learning content.

[0010] "Difficult problems" refer to problems that a learner has not been able to solve in the past or that they find particularly difficult.

[0011] "Server" refers to the central system that processes and stores learning data and learning plans and provides information to terminals.

[0012] A "learning profile" refers to the totality of information entered by a learner, such as their learning style, abilities, interests, and learning goals.

[0013] "Study Plan" refers to a personalized study plan generated by the server based on the learner's profile and the questions entered.

[0014] "Terminal" refers to the device (e.g., computer, tablet, smartphone, etc.) used by a user to access learning content provided by the server.

[0015] "Real-time feedback" refers to feedback that immediately provides a learner with information about whether their answer is correct or incorrect and what areas need improvement after they have answered a question. [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 relates to a system that provides a learner with a personalized, short-term learning experience by generating an individualized learning plan that takes into account the learner's learning style, abilities, interests, and pace of progress.

[0038] The system embodiment operates in the following general manner.

[0039] User Registration and Login

[0040] This is the step where a user first accesses the system and creates an account. The user enters the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[0041] Setting up your learning profile

[0042] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[0043] Providing learning content

[0044] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[0045] Generate a personalized study plan

[0046] The server generates a customized learning plan based on the learner's learning profile and the entered problem data. This plan includes problems that the learner should focus on and explanations for them. The generated learning plan is then sent to the device.

[0047] Start a study session

[0048] The user starts a study session using a terminal. The terminal displays questions and explanations provided by the server to the user. The user progresses at their own pace and enters their answers.

[0049] Real-time feedback

[0050] Each time a user answers a question, the device sends the answer to the server, which evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to try.

[0051] Recording progress

[0052] After a study session is completed, the server records the user's progress, which is used to optimize future study plans. For example, if a user has made progress in a particular area of ​​calculus, the next study plan will offer more advanced problems.

[0053] Specific examples

[0054] For example, consider a user named Tanaka. After registering with the system and logging in, Tanaka enters his learning profile, including such things as "prefers visual learning," "is bad at math," and "interested in programming." Then, Tanaka submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0055] Tanaka begins a study session based on a personalized study plan provided by the server. After solving each problem, he submits his answer to the server and receives real-time feedback. As he studies, the server records his progress and provides him with the most appropriate problems for his next study session.

[0056] This allows Tanaka to study effectively at his own pace. In this way, the system of the present invention provides the learner with a personalized and efficient learning experience.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[0060] Step 2:

[0061] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[0062] Step 3:

[0063] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[0064] Step 4:

[0065] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[0066] Step 5:

[0067] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[0068] Step 6:

[0069] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[0070] Step 7:

[0071] The server analyzes the received problem data and searches the database for similar problems and explanations.

[0072] Step 8:

[0073] The server generates a customized learning plan based on the learning profile and analysis results.

[0074] Step 9:

[0075] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[0076] Step 10:

[0077] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[0078] Step 11:

[0079] The server receives the user's answers, evaluates them, generates evaluation results and feedback, and sends them to the device.

[0080] Step 12:

[0081] The device then displays the feedback it receives to the user, including whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[0082] Step 13:

[0083] After the learning session is completed, the server records the user's progress data in a database.

[0084] Step 14:

[0085] The server analyzes the progress data and optimizes the next study plan. A new study plan is generated and used for the next study session.

[0086] Step 15:

[0087] By repeating the above steps, the user can effectively progress through their studies at their own pace.

[0088] Example 1

[0089] 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."

[0090] Conventional learning systems have difficulty providing personalized learning plans that take into account each learner's learning style and progress speed. They also lack the functionality to provide real-time feedback, record learner progress, and reflect it in the next learning plan. This has led to the problem of learners being unable to study effectively at their own pace.

[0091] 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.

[0092] In this invention, the server includes: a means for a learner to input their own learning style, ability, interests, and progress rate; a means for the learner to input questions that they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing feedback in real time; a means for recording the learner's progress data after the study session ends and optimizing the next study plan; a means for the learner to input account information on a login screen to access the system; and a means for inputting learning style and interests after setting up a study profile. This makes it possible to create a personalized study plan according to the individual circumstances of the learner, provide feedback in real time, and optimize learning.

[0093] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[0094] "Ability" indicates a learner's level of knowledge, skills, and understanding, and refers to the level of academic ability and skill in a particular field.

[0095] "Interests" refers to areas or topics that a learner is particularly interested in.

[0096] "Progression rate" refers to the speed at which a learner comprehends and progresses through the learning content.

[0097] An "input problem" is a problem that a learner inputs to the system that they find difficult to solve.

[0098] "Server" refers to the computer system that handles the main processing and data management of the learning system.

[0099] "Devices" refers to devices such as computers, tablets, and smartphones used by learners.

[0100] "Learning Profile" refers to a collection of data including a learner's learning style, interests, abilities, and rate of progress.

[0101] A "customized learning plan" refers to a learning plan or set of tasks that are specifically created to meet the individual needs and circumstances of a learner.

[0102] "Real-time feedback" refers to providing instant assessment and comments to learners as soon as they submit their answers.

[0103] "Progress data" refers to data that shows how much progress a learner has made in their studies and what results they have achieved.

[0104] "Account information" refers to the authentication information a learner uses to access the system, and typically includes a name, email address, password, etc.

[0105] "Login screen" refers to the screen where a user enters authentication information to access the system.

[0106] A "learning session" refers to a series of learning activities that a learner performs using the system within a set period of time.

[0107] This invention is a system that provides learners with a personalized, short-term learning experience, generating an individual learning plan based on the learner's learning style, ability, interests, and progress rate. The system is operated through a device such as a web browser or mobile app, with the main processing performed by a server.

[0108] Hardware and software used

[0109] The system uses the following hardware and software:

[0110] Terminal: The device operated by the user (PC, tablet, smartphone, etc.)

[0111] Server: A remote computer that processes and stores training data.

[0112] Database: A storage system for storing learning profiles, progress data, and question data.

[0113] Web application / Mobile application: Provides an interface for users to access their learning plans

[0114] System overview and specific operation

[0115] User Registration and Login

[0116] When a user first accesses the system, they create an account. After entering the required information, such as their name, email address, and password, the device sends this information to the server, which encrypts it and stores it in a database. The user then accesses the system using the login screen.

[0117] Setting up your learning profile

[0118] Immediately after logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they enter information such as "I prefer visual learning," "I'm not good at math," or "I'm interested in programming." This information is sent from the device to the server, which then creates a learning profile and stores it in a database.

[0119] Providing learning content

[0120] The user enters a problem they were unable to solve into the system. This problem data is sent from the device to the server, which analyzes it and searches the database for similar problems and their explanations. Links and files for the corresponding learning content are then sent to the user's device.

[0121] Generate a personalized study plan

[0122] The server generates a customized study plan based on the learning profile and the entered problem data. The study plan includes the problems to focus on and their explanations. The study plan is then sent to the device.

[0123] Start a study session

[0124] The user starts a study session using a terminal, which displays questions and explanations provided by the server, and the user progresses at their own pace and enters their answers.

[0125] Real-time feedback

[0126] Each time a user answers a question, the answer is sent from the device to the server. The server evaluates the answer and generates real-time feedback, including whether the answer was correct or incorrect, a detailed explanation, and the next step to try. The feedback is sent to the device and displayed to the user.

[0127] Recording progress

[0128] After a study session is completed, the device sends the session results data to the server, which records the progress data it receives and uses it to optimize future study plans. For example, if a user makes progress in a particular area of ​​calculus, it can be reflected in the next study plan.

[0129] Specific examples

[0130] For example, consider a user named A. After registering with the system and logging in, A enters his learning profile, such as "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, A submits a high school-level calculus problem to the system. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0131] Person A begins a study session based on a personalized study plan provided by the server. As he solves each problem, he sends his answers to the server and receives real-time feedback. As he continues to study, the server records Person A's progress and provides him with the most appropriate problems for his next study session.

[0132] Prompt Sentence Examples

[0133] "Generate a personalized learning plan for Person A, who prefers visual learning, struggles with math, and is interested in programming."

[0134] In this way, the system of the present invention provides a personalized learning experience that is tailored to the individual circumstances of each learner.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: User Registration and Login

[0137] 1.1 A user accesses the system and opens the account registration page. They enter personal information such as their name, email address, and password. The device collects the input information.

[0138] 1.2 The device sends the collected personal information to the server, which receives the data and performs encryption processing.

[0139] 1.3 The server stores the encrypted personal information in the database. The output is a notification that the data has been saved to the database.

[0140] 1.4 A user enters an email address and password on a login screen. The input is the user's authentication information.

[0141] 1.5 The terminal sends the authentication information to the server. The server checks the authentication information against the database information, and if authentication is successful, the user is logged in. The output is a notification of successful login.

[0142] Step 2: Set up your learning profile

[0143] 2.1 After logging in, the user opens the learning profile setting page. Inputs include learning style, interests, abilities, learning goals, etc. The device collects the input information.

[0144] 2.2 The device sends the collected learning profile information to the server. The server receives the data and stores it in the profile database. The output is a notification that the profile has been set up.

[0145] 2.3 As a specific action, the user inputs "I prefer visual learning," "I'm not good at math," and "I'm interested in programming."

[0146] Step 3: Provide learning content

[0147] 3.1 The user inputs the problem they were unable to solve into the system as text or images. The input is the problem data. The terminal collects the problem data.

[0148] 3.2 The terminal sends the collected problem data to the server, which receives the data and performs problem analysis processing.

[0149] 3.3 The server searches the database for similar problems and their explanations. The output is data on similar problems and their explanations.

[0150] 3.4 The terminal displays the similar questions and explanations received from the server to the user.

[0151] Step 4: Generate a personalized study plan

[0152] 4.1 The server generates a customized learning plan based on the learning profile and the input question data. The input is the learning profile and the question data.

[0153] 4.2 Include the focus questions and explanations in the server-generated study plan. The output is a customized study plan.

[0154] 4.3 The device displays the study plan received from the server to the user, and the user begins studying according to the plan.

[0155] Step 5: Start your study session

[0156] 5.1 The user starts a learning session using a terminal. The input is the start command.

[0157] 5.2 The terminal displays the questions and explanations provided by the server to the user. The output is the displayed learning content.

[0158] 5.3 The user works on the problem and inputs the answer. The input is the answer data.

[0159] Step 6: Real-time feedback

[0160] 6.1 When the user answers a question, the terminal sends the answer to the server. The input is the answer data.

[0161] 6.2 The server evaluates the answer data and generates feedback in real time. Data processing involves determining whether the answer is correct or incorrect and generating an explanation. The output is the generated feedback data.

[0162] 6.3 The device displays the feedback received from the server to the user. Specifically, the user immediately checks the feedback and proceeds to the next task.

[0163] Step 7: Record your progress

[0164] 7.1 After the learning session is completed, the device sends the session result data to the server. The input is the learning progress data.

[0165] 7.2 The server records the received progress data and stores it in a database. The output is the stored progress data.

[0166] 7.3 Progress data is used to optimize your next study plan, specifically providing more challenging and user-appropriate questions in your next study session.

[0167] (Application example 1)

[0168] 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."

[0169] Conventional learning systems have faced challenges in effectively providing learners with optimal learning experiences tailored to their individual learning styles and interests. In particular, there was a lack of technology to provide short, personalized learning experiences using smartphones and smart devices. Furthermore, there was also a lack of systems that could dynamically update learning plans based on learners' progress and provide appropriate feedback in real time. This resulted in reduced learning efficiency and prevented learners from effectively progressing through their studies at their own pace.

[0170] 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.

[0171] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, and progress rate; a means for the learner to input questions they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing real-time feedback; a means installed on a smartphone, smart glasses, head-mounted display, or robot to allow the learner to receive a personalized, short-term study experience; a means for recording the learner's progress data during the study session and reflecting it in the next study plan; a means for generating questions and their explanations optimal for the learner using a generative AI model; and a means for generating a study plan based on the learner's requests using prompt sentences. This allows the learner to receive an optimal learning experience tailored to their learning style and interests. Furthermore, the learner's progress data can be recorded while receiving real-time feedback and reflected in the next study plan, improving learning efficiency.

[0172] "Learning style" refers to the unique learning methods and procedures that each learner has, and refers to different senses and approaches such as visual, auditory, and experiential.

[0173] "Ability" refers to the overall level of knowledge and skills that a learner possesses, including proficiency in a particular field.

[0174] "Interest" refers to the interest a learner has in a particular field or topic, and is an important factor that influences motivation to learn.

[0175] "Progression speed" is an indicator of how quickly a learner can understand and progress through the learning content.

[0176] A "problem" is a task or question that a learner must solve, and is an important element for assessing learning progress.

[0177] "Explanation" refers to additional information or explanation about the question and its answer, provided to help learners deepen their understanding.

[0178] A "learning plan" is a plan of learning content and progression created based on a learner's individual needs and customized for efficient learning.

[0179] A "terminal" is a device or equipment on which a learner works on their learning plan, including a smartphone, smart glasses, a head-mounted display, or a robot.

[0180] "Feedback" refers to evaluations and comments provided by the server in real time in response to answers submitted by learners.

[0181] "Progress Data" means data that records a learner's progress during a learning session and is used to optimize subsequent learning plans.

[0182] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate questions and explanations that are optimal for learners.

[0183] A "prompt" is text data that is input into a generative AI model and is used to generate a learning plan based on the learner's requirements.

[0184] The present invention relates to a system that provides a user with a personalized, short-term learning experience by using an application installed on a device such as a smartphone, smart glasses, a head-mounted display, or a robot.

[0185] First, a user accesses the system and creates an account. The user enters the required information, such as their name, email address, and password, and sends it from their device to the server. The server encrypts and stores this information, and the user then accesses the system through the login screen.

[0186] Next, the user inputs their learning style, abilities, interests, and learning goals. For example, if a user prefers visual learning, is bad at math, and is interested in programming, they enter that information into the device. This information is sent to the server, which then creates and stores a learning profile.

[0187] When a user begins studying, they input questions that they find difficult or have been unable to solve into the system. These question data are sent from the device to a server, which then analyzes the input questions. Natural language processing technology such as SpaCy is used for the analysis. The server then searches a database for similar questions and their explanations, and generates a customized study plan based on that information.

[0188] The generated study plan is sent to the device, and the user begins a study session through the device. The user answers the questions and the answers are sent to the server. The server evaluates the answers and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to be attempted.

[0189] After a study session is completed, the server records the user's progress data, which is used to optimize the next study plan. For example, if a user shows progress on a particular task, the next study plan will offer more advanced problems.

[0190] Furthermore, the system uses a generative AI model to generate questions and explanations that are optimal for each learner, based on the user's learning profile and progress data, enabling more efficient learning.

[0191] Example prompts to input to a generative AI model:

[0192] User name: Tanaka-san

[0193] Uses: Visual learners, poor at math, interested in programming

[0194] Contents: Calculus at the second year of high school level

[0195] Problem: Solution to \(\int x^2 dx\)

[0196] Based on these prompts, the generative AI model provides a personalized learning plan for the user, enabling effective learning in a short amount of time.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] A user accesses the system and creates an account. They enter the required information, such as their name, email address, and password, and send it from their terminal to the server. The server encrypts this information (using AES encryption) and stores it in a database. The input is user information (name, email address, and password), and the output is the encrypted user information.

[0200] Step 2:

[0201] A user accesses the system through a login screen. The server authenticates the login information and grants the user access rights. The input is the login information (email address, password) and the output is the authentication result (success or failure). If authentication is successful, the user proceeds to the next step.

[0202] Step 3:

[0203] The user inputs their learning style, ability, interests, and learning goals. This information is sent from the terminal to the server, which then generates a learning profile and stores it in a database. The input is the learning profile information (learning style, ability, interests, learning goals), and the output is the generated learning profile.

[0204] Step 4:

[0205] Users submit problems they have difficulty with or have been unable to solve to the system. The problem data is sent from the device to the server, which then analyzes the problem using natural language processing technology (SpaCy). The server then searches a database for similar problems and their explanations, and generates a customized study plan. The input is the problem data, and the output is a customized study plan.

[0206] Step 5:

[0207] The generated study plan is sent to the device, and the user starts a study session through the device. The user answers questions, and the answers are sent from the device to the server. The input is the user's answer, and the output is the state in which the answer has been sent to the server.

[0208] Step 6:

[0209] The server evaluates the user's answers and provides real-time feedback, including whether the answer is correct or incorrect, an explanation, and what to do next. The input is the user's answer and the output is the feedback. The server evaluates the answers using an evaluation algorithm.

[0210] Step 7:

[0211] After the study session ends, the server records the user's progress data. This data is used to optimize the next study plan. The input is the study session data (progress data) and the output is the recorded progress data. The server saves the progress data in a database and dynamically updates the next study plan.

[0212] Step 8:

[0213] The server uses a generative AI model to generate questions and explanations that are optimal for the learner. The generative AI model inputs learning profile and progress data and outputs optimal questions and explanations. The input is learning profile and progress data, and the output is generated questions and explanations.

[0214] Step 9:

[0215] The server uses the prompt to generate a learning plan based on the learner's requirements. The prompt includes the user's requirements (learning style, interests, learning content, etc.) and is input to the generative AI model. The input is the prompt, and the output is the learning plan.

[0216] Example prompts to input to a generative AI model:

[0217] User name: Yamada-san

[0218] Uses: Visual learners, poor at math, interested in programming

[0219] What you'll learn: University-level linear algebra

[0220] Problem: How to find the inverse of a matrix

[0221] Based on this prompt, the server uses a generative AI model to provide Yamada with an optimal learning plan, allowing the user to study effectively in a short amount of time.

[0222] 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.

[0223] The present invention combines an emotion engine with a system that provides learners with a personalized, short-term learning experience, which generates an individualized learning plan based on the learner's learning style, ability, interests, pace, and emotions.

[0224] An embodiment of the system operates as follows.

[0225] User Registration and Login

[0226] When a user first accesses the system, they create an account. They enter the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[0227] Setting up your learning profile

[0228] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[0229] Providing learning content

[0230] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[0231] Generate a personalized study plan

[0232] The server generates a customized study plan based on the study profile and the entered question data, and the study plan is sent to the terminal, which displays the contents to the user.

[0233] Introducing the Emotion Engine

[0234] During a study session, the device uses the user's facial expressions, tone of voice, and input patterns to acquire emotional data from the emotion engine. The server receives this emotional data and reflects it in the study plan. For example, if the user is feeling stressed, it can provide them with questions with a lower level of difficulty.

[0235] Start a study session

[0236] The user starts a learning session using the device, works on the provided questions, and inputs their answers. The device uses an emotion engine to measure the user's emotions in real time and transmits them to the server.

[0237] Real-time feedback

[0238] Each time a user answers a question, the device sends the answer to the server. The server evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to take. Furthermore, the server uses data from the emotion engine to provide appropriate motivation to the user.

[0239] Recording progress

[0240] After the learning session ends, the server records the user's progress and emotional data in a database, which is used to optimize the next learning plan.

[0241] Specific examples

[0242] For example, consider the case of a user named Yamada. After registering with the system and logging in, Yamada enters his learning profile, including the following: "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, Yamada submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0243] When Yamada starts a session, the device analyzes her facial expressions and tone of voice through an emotion engine. For example, if the emotion engine recognizes that Yamada is having difficulty solving a problem, the server will provide her with a slightly less difficult problem next time. If Yamada is feeling stressed, the server will provide feedback suggesting a short break to relax.

[0244] After the study session, Yamada's progress and emotional data are recorded and used to optimize his next study plan.

[0245] This allows Yamada to study effectively at his own pace and in line with his emotional state. In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[0249] Step 2:

[0250] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[0251] Step 3:

[0252] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[0253] Step 4:

[0254] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[0255] Step 5:

[0256] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[0257] Step 6:

[0258] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[0259] Step 7:

[0260] The server analyzes the received problem data and searches the database for similar problems and explanations.

[0261] Step 8:

[0262] The server generates a customized learning plan based on the learning profile and analysis results.

[0263] Step 9:

[0264] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[0265] Step 10:

[0266] The device analyzes the user's facial expressions and tone of voice using an emotion engine and sends the emotion data to the server.

[0267] Step 11:

[0268] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[0269] Step 12:

[0270] The server receives the user's answer, evaluates it, generates evaluation results and feedback, and sends them to the device, taking into account the user's emotional data.

[0271] Step 13:

[0272] The device then displays the received feedback to the user, including whether the answer was correct or incorrect, an explanation, the next step to be taken, and motivation based on emotional data.

[0273] Step 14:

[0274] After the learning session is completed, the server records the user's progress and emotional data in a database.

[0275] Step 15:

[0276] The server analyzes the progress and emotion data to optimize the next learning plan, and a new learning plan is generated and used for the next learning session.

[0277] Step 16:

[0278] By repeating the above steps, users can study effectively at their own pace and in line with their emotional state.

[0279] Example 2

[0280] 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."

[0281] Conventional learning support systems have the problem of being unable to adequately provide individual learning plans that are tailored to each learner's learning style, ability, interests, and progress speed. Furthermore, they are unable to take into account the learner's emotions during learning, which can lead to a decrease in learning effectiveness if the learner continues to study while feeling stressed or anxious. Another problem is that the lack of real-time emotional feedback makes it difficult to respond immediately.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0283] In this invention, the server includes means for the learner to input their own learning style, ability, interests, and progress speed, means for the learner to input questions that they are weak at, means for the server to analyze the input questions and identify similar questions and their explanations, means for the server to generate a customized study plan based on the learner's learning profile and the input questions, means for the terminal to acquire emotional data of the learner while studying, and means for the server to dynamically adjust the study plan based on the emotional data. This makes it possible to provide an individually optimized study plan that incorporates the learner's emotions throughout the entire learning process, and realizes emotional feedback and appropriate responses in real time.

[0284] "Learning style" refers to the method or technique in which a learner learns most effectively.

[0285] "Ability" refers to the knowledge and skills that a learner has to solve a particular task or problem.

[0286] "Interest" refers to the degree of interest or concern a learner has in a particular field or topic.

[0287] "Progression rate" refers to the pace or speed at which a learner progresses through their studies.

[0288] "Learning Profile" refers to a collection of information including a learner's learning style, abilities, interests, progress rate, and other relevant data.

[0289] A "learning plan" refers to a learning guideline or plan created based on the learner's individual needs and circumstances.

[0290] "Emotional data" refers to data about emotions obtained by analyzing a learner's facial expressions, voice, input patterns, etc.

[0291] "Devices" refer to electronic devices such as computers, smartphones, and tablets used by learners.

[0292] A "server" is a computer that is the core of a learning system and is a device that processes and stores data and provides services.

[0293] "Feedback" refers to the immediate evaluation and advice given to a learner in response to their actions or answers.

[0294] The present invention combines an emotion engine with a system that provides a personalized, short-term learning experience for learners. The system generates an individualized learning plan taking into account the learner's learning style, ability, interests, progress rate, and emotions. An embodiment of the system is configured as follows:

[0295] User Registration and Login

[0296] The user accesses the system and creates an account. The user enters the required information such as name, email address, and password, and presses the registration button. The terminal sends the entered information to the server, which encrypts and stores the data. After completing registration, the user enters their email address and password on the login screen to log in to the system.

[0297] Setting up your learning profile

[0298] After logging in, the user enters their learning style, interests, abilities, learning goals, etc. on the "Profile Settings" page. The device sends this information to the server, which then creates a learning profile and stores it in a database.

[0299] Providing learning content

[0300] Users enter questions they have difficulty with or have been unable to solve into the system on the "Learning Contents" page. The device sends the question data to the server, which analyzes the question data and searches the database for similar questions and their explanations.

[0301] Generate a personalized study plan

[0302] The server generates a customized study plan based on the study profile and the entered question data, and the generated study plan is sent to the terminal and displayed to the user.

[0303] Introducing the Emotion Engine

[0304] During a learning session, the device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and passes the data to an emotion engine. The server then dynamically adjusts the learning plan based on the emotional data, such as lowering the difficulty level if the user is feeling stressed.

[0305] Start a study session

[0306] The user begins a learning session and works through the presented problems. The device measures emotional data in real time and transmits it to the server, which then provides appropriate feedback.

[0307] Real-time feedback

[0308] Each time a user answers a question, the device sends the answer data to the server, which then evaluates the answer and immediately provides a correct answer, explanation, and the next task to tackle. It also provides advice based on the user's emotional data to boost motivation.

[0309] Recording progress

[0310] After the learning session ends, the server records the user's progress and emotional data and uses it to optimize the next learning plan.

[0311] Hardware and software used

[0312] Server: For data processing and storage, we use common web servers and database servers (e.g., Apache (registered trademark), MySQL (registered trademark)).

[0313] Device: The computer, smartphone, tablet, etc. that the user uses.

[0314] Software: Machine learning libraries such as TENSORFLOW (registered trademark) are used for sentiment analysis, and HTML and JavaScript (registered trademark) are used for the front end.

[0315] Specific examples

[0316] For example, consider a case where a learner uses the system. The learner registers with the system and enters a learning profile that includes "prefers visual learning," "is not good at math," and "is interested in programming." The learner then submits a calculus problem at the second-year high school level. The server analyzes this problem and identifies similar problems and their explanations.

[0317] When a learner starts a study session, the device uses an emotion engine to analyze the learner's facial expressions and tone of voice, and if the learner is feeling stressed, it will provide them with questions of lower difficulty and suggest appropriate breaks to relax.

[0318] Prompt Sentence Examples

[0319] 1. "Prefer visual learning," "are bad at math," and "are interested in programming" - Give us some calculus problems at the second-year high school level.

[0320] 2. Analyze the user's facial expressions and tone of voice and suggest a short break to relax if they are feeling stressed.

[0321] In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1: User Registration

[0324] The user clicks the "New Registration" button on the system's access page and enters the necessary information into the input form, such as name, email address, and password. The device sends this information to the server. The server encrypts the entered data and stores it in a database. The user's personal information is stored as input, and encrypted data is stored as output.

[0325] Step 2: Log in

[0326] After completing registration, the user enters their email address and password on the login screen and presses the "Login" button. The device sends this to the server, which then verifies it against a database. The input is the user's login information, and the output is the authentication result (success or failure). If authentication is successful, the user is redirected to the home screen.

[0327] Step 3: Set up your learning profile

[0328] Users access the "Profile Settings" page from the home screen. They input their learning style, interests, abilities, and learning goals. The device sends this information to the server, which then generates a learning profile based on the input data and stores it in a database. Learning style and interests are stored as input, and the generated learning profile is stored as output.

[0329] Step 4: Provide learning content

[0330] On the "Learning Content" page, users enter questions they have difficulty with or have been unable to solve into the system. The device then sends the question data to the server. The server then uses natural language processing technology to analyze the question data and searches the database for similar questions and their explanations. The learner's question data is the input, and similar questions and their explanations are identified as the output.

[0331] Step 5: Generate a personalized study plan

[0332] The server uses an algorithm to generate a customized study plan based on the learning profile and input question data. The generated study plan is sent to the terminal and displayed to the user. The input is the learning profile and question data, and the output is the customized study plan.

[0333] Step 6: Implementing the Emotion Engine

[0334] During a study session, the device uses a camera and microphone to capture the user's facial expressions and tone of voice in real time. The device passes this data to an emotion engine to infer the user's emotions. The inference results are sent to a server, which dynamically adjusts the difficulty of the study plan and break suggestions based on the emotion data. The input is real-time emotion data, and the output is an adjusted study plan.

[0335] Step 7: Start your study session

[0336] The user clicks the "Start Learning Session" button to start a learning session. The user works through the presented questions and enters their answers. The device sends the answer data to the server, which evaluates the answers and generates feedback. The input is the user's answer data, and the output is feedback.

[0337] Step 8: Real-time feedback

[0338] Each time a user answers a question, the device sends the answer data to the server. The server evaluates the answer and provides real-time feedback on whether the answer was correct, an explanation, and the next step to tackle. It also provides motivational advice based on emotional data. The inputs are answer data and emotional data, and the output is immediate feedback.

[0339] Step 9: Record your progress

[0340] After the study session ends, the server records the user's progress and emotion data, which are used to optimize the next study plan. The inputs are the progress and emotion data from the session, and the output is the optimized next study plan.

[0341] (Application example 2)

[0342] 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."

[0343] In modern society, optimizing individual learning experiences is an important challenge, but existing systems lack personalized feedback that takes into account the learner's emotional state. Furthermore, in areas such as electronic payment, dynamic adjustment functions are needed to help users learn effectively and master operations. Systems that address these challenges and allow learners to progress at their own pace without stress or anxiety are needed.

[0344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0345] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, progress rate, and emotional state; a means for the learner to input questions or learning content that they are weak at; a means for the server to analyze the input questions or learning content and identify similar questions or learning content and their explanations; a means for the server to generate a customized learning plan based on the learner's learning profile and the input questions or learning content; a means for a terminal to enable the learner to work on the learning plan provided by the server; a means for an emotion engine to acquire emotional data of the learner using the learner's facial expressions, tone of voice, input patterns, etc.; a means for the server to dynamically adjust the learning plan based on the emotional data; and a means for evaluating the learner's answers or responses and providing feedback in real time, thereby making it possible to individually optimize the learner's learning experience and adjust it in real time according to the learner's emotional state.

[0346] "Learning style" refers to the way a learner finds it easy to understand or learn information.

[0347] "Ability" refers to the degree of skill or knowledge a learner has in a particular field or subject.

[0348] "Interest" refers to the degree of interest or involvement a learner has in a particular field or topic.

[0349] "Progression rate" refers to how quickly a learner masters the material.

[0350] "Emotional state" refers to the psychological state such as stress, excitement, and impatience that learners feel while studying.

[0351] "Problems or learning content" refers to tasks or educational materials that a learner must complete.

[0352] "Server" refers to a computing device for processing, analyzing, and optimizing data input from learners.

[0353] A "learning profile" refers to information entered by a learner summarizing their learning style, abilities, interests, pace of progress, and emotional state.

[0354] "Customized learning plan" refers to an individual learning schedule and content that is optimized based on the learner's learning profile.

[0355] "Terminal" refers to the device through which a learner accesses and works on the learning plan material provided by the server.

[0356] An "emotion engine" refers to software or hardware that analyzes a learner's facial expressions, tone of voice, input patterns, etc. to recognize their emotional state.

[0357] "Dynamic adjustment" refers to changing the learning plan in real time in response to the learner's ongoing performance and emotional state.

[0358] "Feedback" refers to the process of providing evaluation and instructional comments on answers or responses submitted by learners.

[0359] The present invention provides a system for providing a personalized learning experience to a learner. The system incorporates an emotion engine and includes a function for dynamically adjusting a learning plan based on the learner's emotional state. Specific embodiments of the system are described below.

[0360] System Configuration

[0361] The system consists of the following major components:

[0362] 1. Server

[0363] 2. Terminal

[0364] 3. Emotion Engine

[0365] server

[0366] The server is a computing device that processes, analyzes, and optimizes data input from learners. The server has the following functions:

[0367] There is a database function that allows learners to input their learning style, ability, interests, progress rate, and emotional state.

[0368] The server receives the question data and learning content entered by the learner and analyzes them. The analysis results include a means for identifying questions and explanations similar to the learner's answers.

[0369] Based on the learner's learning profile and input question data, the server generates a customized study plan that is optimized based on the learner's learning style, pace, and emotional state.

[0370] Terminal

[0371] The terminal is a device that displays the customized learning plan provided by the server and allows the learner to work on it. This terminal is mainly a smartphone, but other devices can also be considered.

[0372] The device's emotion engine uses the learner's facial expressions, tone of voice, input patterns, etc. to obtain the learner's emotional data.

[0373] Additionally, the device receives feedback from the server and provides it to the learner in real time, including information on whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[0374] Based on the emotion engine data, the device sends information to the server to dynamically adjust the learning plan.

[0375] Emotion Engine

[0376] The emotion engine is software that analyzes data such as the learner's facial expressions, tone of voice, and input patterns to recognize their emotional state. Specifically, the EmotionAnalyzer and LearningPlanGenerator are used.

[0377] The emotion engine analyzes the learner's emotional state in real time during the learning session and sends the data to the server, which uses the data to adjust the learning plan appropriately.

[0378] For example, if a learner is feeling stressed, the server can provide them with questions of lower difficulty, or if they are highly motivated, it can present them with questions of higher difficulty.

[0379] Specific examples

[0380] For example, a learner may enter a learning profile stating that they "prefer visual learning," "are not good at math," and "are interested in programming." The learner then submits a high school calculus problem to the system. The server analyzes this problem and identifies similar problems and their explanations. During the learning session, the device analyzes the learner's facial expressions and tone of voice through an emotion engine. If the learner is struggling, the server can provide a less difficult problem next time. Also, if the learner is feeling stressed, feedback is provided suggesting a short break to relax.

[0381] Example input to a generative AI model

[0382] The next challenge to tackle is the security of electronic payments. The emotion engine analyzes your emotions and provides appropriate motivation. If you feel that "learning how to pay is difficult," please suggest what feedback would be appropriate for the next step.

[0383] Thus, the present invention provides a system that can individually optimize a learner's learning experience and adjust it in real time according to their emotional state.

[0384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0385] Step 1:

[0386] A user accesses the system and creates an account.

[0387] How it works: The user enters the required information, such as their name, email address, and password, and the device sends it to the server, which then encrypts and stores the information and creates a user account.

[0388] Input: Name, Email Address, Password

[0389] Output: User account created and saved

[0390] Step 2:

[0391] The user accesses the system through the login screen.

[0392] How it works: The user logs in using their registered email address and password, and the device sends this authentication information to the server, which verifies the information entered and returns the authentication result.

[0393] Input: Email address, Password

[0394] Output: Authentication result (success or failure)

[0395] Step 3:

[0396] New users set up their learning profile.

[0397] Specific operation: After logging in, the user inputs their learning style, ability, interests, progress rate, and emotional state, and the device sends this information to the server, which then generates a learning profile and stores it in a database.

[0398] Inputs: learning style, ability, interest, progress rate, emotional state

[0399] Output: Generate and save a learning profile

[0400] Step 4:

[0401] Users can input their own weak questions or learning content.

[0402] Specific operation: The user inputs questions that they were unable to solve or that they found difficult, and the device sends them to the server. The server receives the submitted questions and begins analyzing them.

[0403] Input: Problems you couldn't solve, problems you're not good at

[0404] Output: Receive problem data and start analysis

[0405] Step 5:

[0406] The server analyzes the submitted problem and identifies similar problems and their explanations.

[0407] Specific operation: The server analyzes the received problem data and searches the database for similar problems. The search results include similar problems with explanations.

[0408] Input: Problem data

[0409] Output: Similar problems and their explanations

[0410] Step 6:

[0411] The server generates a customized learning plan based on the learner's learning profile and input question data.

[0412] Specific operation: The server combines the learning profile and analysis results to generate an individually optimized learning plan and send it to the device.

[0413] Input: Learning profile, analysis results of problem data

[0414] Output: Generate a customized learning plan

[0415] Step 7:

[0416] The terminal allows the learner to work through a learning plan provided by the server.

[0417] Specific operations: The device displays the received learning plan and provides an interface for the user to work on it. The user answers the assignments and enters the results.

[0418] Input: Study Plan

[0419] Output: Display of the learning plan and provision of an interface

[0420] Step 8:

[0421] The emotion engine obtains the learner's emotional data using the learner's facial expressions, tone of voice, input patterns, etc.

[0422] How it works: The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data to identify their emotional state, which is then sent to a server.

[0423] Input: facial expression data, tone of voice, input pattern

[0424] Output: Emotion data

[0425] Step 9:

[0426] The server dynamically adjusts the learning plan based on the emotional data.

[0427] Specific operation: The server analyzes the received emotional data and adjusts the difficulty and content of the study plan. For example, if the user is feeling stressed, it will provide questions with lower difficulty.

[0428] Input: Emotion data

[0429] Output: A dynamically adjusted learning plan

[0430] Step 10:

[0431] Evaluate learners' answers or responses and provide feedback in real time.

[0432] How it works: The device sends the user's answers to the server, which evaluates them and provides real-time feedback, such as whether the answer was correct, additional explanations, and the next assignment.

[0433] Input: User's answer or response

[0434] Output: Real-time feedback

[0435] Step 11:

[0436] Stores learner progress and sentiment data and optimizes the next learning plan based on that.

[0437] What it does: After a learning session ends, the server stores progress and emotion data in a database, which is used to optimize the next learning plan.

[0438] Input: Progress data, emotion data

[0439] Output: Save data and optimize the next learning plan

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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."

[0456] The present invention relates to a system that provides a learner with a personalized, short-term learning experience by generating an individualized learning plan that takes into account the learner's learning style, abilities, interests, and pace of progress.

[0457] The system embodiment operates in the following general manner.

[0458] User Registration and Login

[0459] This is the step where a user first accesses the system and creates an account. The user enters the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[0460] Setting up your learning profile

[0461] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[0462] Providing learning content

[0463] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[0464] Generate a personalized study plan

[0465] The server generates a customized learning plan based on the learner's learning profile and the entered problem data. This plan includes problems that the learner should focus on and explanations for them. The generated learning plan is then sent to the device.

[0466] Start a study session

[0467] The user starts a study session using a terminal. The terminal displays questions and explanations provided by the server to the user. The user progresses at their own pace and enters their answers.

[0468] Real-time feedback

[0469] Each time a user answers a question, the device sends the answer to the server, which evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to try.

[0470] Recording progress

[0471] After a study session is completed, the server records the user's progress, which is used to optimize future study plans. For example, if a user has made progress in a particular area of ​​calculus, the next study plan will offer more advanced problems.

[0472] Specific examples

[0473] For example, consider a user named Tanaka. After registering with the system and logging in, Tanaka enters his learning profile, including such things as "prefers visual learning," "is bad at math," and "interested in programming." Then, Tanaka submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0474] Tanaka begins a study session based on a personalized study plan provided by the server. After solving each problem, he submits his answer to the server and receives real-time feedback. As he studies, the server records his progress and provides him with the most appropriate problems for his next study session.

[0475] This allows Tanaka to study effectively at his own pace. In this way, the system of the present invention provides the learner with a personalized and efficient learning experience.

[0476] The processing flow will be explained below.

[0477] Step 1:

[0478] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[0479] Step 2:

[0480] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[0481] Step 3:

[0482] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[0483] Step 4:

[0484] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[0485] Step 5:

[0486] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[0487] Step 6:

[0488] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[0489] Step 7:

[0490] The server analyzes the received problem data and searches the database for similar problems and explanations.

[0491] Step 8:

[0492] The server generates a customized learning plan based on the learning profile and analysis results.

[0493] Step 9:

[0494] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[0495] Step 10:

[0496] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[0497] Step 11:

[0498] The server receives the user's answers, evaluates them, generates evaluation results and feedback, and sends them to the device.

[0499] Step 12:

[0500] The device then displays the feedback it receives to the user, including whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[0501] Step 13:

[0502] After the learning session is completed, the server records the user's progress data in a database.

[0503] Step 14:

[0504] The server analyzes the progress data and optimizes the next study plan. A new study plan is generated and used for the next study session.

[0505] Step 15:

[0506] By repeating the above steps, the user can effectively progress through their studies at their own pace.

[0507] Example 1

[0508] 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."

[0509] Conventional learning systems have difficulty providing personalized learning plans that take into account each learner's learning style and progress speed. They also lack the functionality to provide real-time feedback, record learner progress, and reflect it in the next learning plan. This has led to the problem of learners being unable to study effectively at their own pace.

[0510] 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.

[0511] In this invention, the server includes: a means for a learner to input their own learning style, ability, interests, and progress rate; a means for the learner to input questions that they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing feedback in real time; a means for recording the learner's progress data after the study session ends and optimizing the next study plan; a means for the learner to input account information on a login screen to access the system; and a means for inputting learning style and interests after setting up a study profile. This makes it possible to create a personalized study plan according to the individual circumstances of the learner, provide feedback in real time, and optimize learning.

[0512] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[0513] "Ability" indicates a learner's level of knowledge, skills, and understanding, and refers to the level of academic ability and skill in a particular field.

[0514] "Interests" refers to areas or topics that a learner is particularly interested in.

[0515] "Progression rate" refers to the speed at which a learner comprehends and progresses through the learning content.

[0516] An "input problem" is a problem that a learner inputs to the system that they find difficult to solve.

[0517] "Server" refers to the computer system that handles the main processing and data management of the learning system.

[0518] "Devices" refers to devices such as computers, tablets, and smartphones used by learners.

[0519] "Learning Profile" refers to a collection of data including a learner's learning style, interests, abilities, and rate of progress.

[0520] A "customized learning plan" refers to a learning plan or set of tasks that are specifically created to meet the individual needs and circumstances of a learner.

[0521] "Real-time feedback" refers to providing instant assessment and comments to learners as soon as they submit their answers.

[0522] "Progress data" refers to data that shows how much progress a learner has made in their studies and what results they have achieved.

[0523] "Account information" refers to the authentication information a learner uses to access the system, and typically includes a name, email address, password, etc.

[0524] "Login screen" refers to the screen where a user enters authentication information to access the system.

[0525] A "learning session" refers to a series of learning activities that a learner performs using the system within a set period of time.

[0526] This invention is a system that provides learners with a personalized, short-term learning experience, generating an individual learning plan based on the learner's learning style, ability, interests, and progress rate. The system is operated through a device such as a web browser or mobile app, with the main processing performed by a server.

[0527] Hardware and software used

[0528] The system uses the following hardware and software:

[0529] Terminal: The device operated by the user (PC, tablet, smartphone, etc.)

[0530] Server: A remote computer that processes and stores training data.

[0531] Database: A storage system for storing learning profiles, progress data, and question data.

[0532] Web application / Mobile application: Provides an interface for users to access their learning plans

[0533] System overview and specific operation

[0534] User Registration and Login

[0535] When a user first accesses the system, they create an account. After entering the required information, such as their name, email address, and password, the device sends this information to the server, which encrypts it and stores it in a database. The user then accesses the system using the login screen.

[0536] Setting up your learning profile

[0537] Immediately after logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they enter information such as "I prefer visual learning," "I'm not good at math," or "I'm interested in programming." This information is sent from the device to the server, which then creates a learning profile and stores it in a database.

[0538] Providing learning content

[0539] The user enters a problem they were unable to solve into the system. This problem data is sent from the device to the server, which analyzes it and searches the database for similar problems and their explanations. Links and files for the corresponding learning content are then sent to the user's device.

[0540] Generate a personalized study plan

[0541] The server generates a customized study plan based on the learning profile and the entered problem data. The study plan includes the problems to focus on and their explanations. The study plan is then sent to the device.

[0542] Start a study session

[0543] The user starts a study session using a terminal, which displays questions and explanations provided by the server, and the user progresses at their own pace and enters their answers.

[0544] Real-time feedback

[0545] Each time a user answers a question, the answer is sent from the device to the server. The server evaluates the answer and generates real-time feedback, including whether the answer was correct or incorrect, a detailed explanation, and the next step to try. The feedback is sent to the device and displayed to the user.

[0546] Recording progress

[0547] After a study session is completed, the device sends the session results data to the server, which records the progress data it receives and uses it to optimize future study plans. For example, if a user makes progress in a particular area of ​​calculus, that progress can be reflected in their next study plan.

[0548] Specific examples

[0549] For example, consider a user named A. After registering with the system and logging in, A enters his learning profile, such as "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, A submits a high school-level calculus problem to the system. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0550] Person A begins a study session based on a personalized study plan provided by the server. As he solves each problem, he sends his answers to the server and receives real-time feedback. As he continues to study, the server records Person A's progress and provides him with the most appropriate problems for his next study session.

[0551] Prompt Sentence Examples

[0552] "Generate a personalized learning plan for Person A, who prefers visual learning, struggles with math, and is interested in programming."

[0553] In this way, the system of the present invention provides a personalized learning experience that is tailored to the individual circumstances of each learner.

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

[0555] Step 1: User Registration and Login

[0556] 1.1 A user accesses the system and opens the account registration page. They enter personal information such as their name, email address, and password. The device collects the input information.

[0557] 1.2 The device sends the collected personal information to the server, which receives the data and performs encryption processing.

[0558] 1.3 The server stores the encrypted personal information in the database. The output is a notification that the data has been saved to the database.

[0559] 1.4 A user enters an email address and password on a login screen. The input is the user's authentication information.

[0560] 1.5 The terminal sends the authentication information to the server. The server checks the authentication information against the database information, and if authentication is successful, the user is logged in. The output is a notification of successful login.

[0561] Step 2: Set up your learning profile

[0562] 2.1 After logging in, the user opens the learning profile setting page. Inputs include learning style, interests, abilities, learning goals, etc. The device collects the input information.

[0563] 2.2 The device sends the collected learning profile information to the server. The server receives the data and stores it in the profile database. The output is a notification that the profile has been set up.

[0564] 2.3 As a specific action, the user inputs "I prefer visual learning," "I'm not good at math," and "I'm interested in programming."

[0565] Step 3: Provide learning content

[0566] 3.1 The user inputs the problem they were unable to solve into the system as text or images. The input is the problem data. The terminal collects the problem data.

[0567] 3.2 The terminal sends the collected problem data to the server, which receives the data and performs problem analysis processing.

[0568] 3.3 The server searches the database for similar problems and their explanations. The output is data on similar problems and their explanations.

[0569] 3.4 The terminal displays the similar questions and explanations received from the server to the user.

[0570] Step 4: Generate a personalized study plan

[0571] 4.1 The server generates a customized learning plan based on the learning profile and the input question data. The input is the learning profile and the question data.

[0572] 4.2 Include the focus questions and explanations in the server-generated study plan. The output is a customized study plan.

[0573] 4.3 The device displays the study plan received from the server to the user, and the user begins studying according to the plan.

[0574] Step 5: Start your study session

[0575] 5.1 The user starts a learning session using a terminal. The input is the start command.

[0576] 5.2 The terminal displays the questions and explanations provided by the server to the user. The output is the displayed learning content.

[0577] 5.3 The user works on the problem and inputs the answer. The input is the answer data.

[0578] Step 6: Real-time feedback

[0579] 6.1 When the user answers a question, the terminal sends the answer to the server. The input is the answer data.

[0580] 6.2 The server evaluates the answer data and generates feedback in real time. Data processing involves determining whether the answer is correct or incorrect and generating an explanation. The output is the generated feedback data.

[0581] 6.3 The device displays the feedback received from the server to the user. Specifically, the user immediately checks the feedback and proceeds to the next task.

[0582] Step 7: Record your progress

[0583] 7.1 After the learning session is completed, the device sends the session result data to the server. The input is the learning progress data.

[0584] 7.2 The server records the received progress data and stores it in a database. The output is the stored progress data.

[0585] 7.3 Progress data is used to optimize your next study plan, specifically providing more challenging and user-appropriate questions in your next study session.

[0586] (Application example 1)

[0587] 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."

[0588] Conventional learning systems have faced challenges in effectively providing learners with optimal learning experiences tailored to their individual learning styles and interests. In particular, there was a lack of technology to provide short, personalized learning experiences using smartphones and smart devices. Furthermore, there was also a lack of systems that could dynamically update learning plans based on learners' progress and provide appropriate feedback in real time. This resulted in reduced learning efficiency and prevented learners from effectively progressing through their studies at their own pace.

[0589] 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.

[0590] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, and progress rate; a means for the learner to input questions they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing real-time feedback; a means installed on a smartphone, smart glasses, head-mounted display, or robot to allow the learner to receive a personalized, short-term study experience; a means for recording the learner's progress data during the study session and reflecting it in the next study plan; a means for generating questions and their explanations optimal for the learner using a generative AI model; and a means for generating a study plan based on the learner's requests using prompt sentences. This allows the learner to receive an optimal learning experience tailored to their learning style and interests. Furthermore, the learner's progress data can be recorded while receiving real-time feedback and reflected in the next study plan, improving learning efficiency.

[0591] "Learning style" refers to the unique learning methods and procedures that each learner has, and refers to different senses and approaches such as visual, auditory, and experiential.

[0592] "Ability" refers to the overall level of knowledge and skills that a learner possesses, including proficiency in a particular field.

[0593] "Interest" refers to the interest a learner has in a particular field or topic, and is an important factor that influences motivation to learn.

[0594] "Progression speed" is an indicator of how quickly a learner can understand and progress through the learning content.

[0595] A "problem" is a task or question that a learner must solve, and is an important element for assessing learning progress.

[0596] "Explanation" refers to additional information or explanation about the question and its answer, provided to help learners deepen their understanding.

[0597] A "learning plan" is a plan of learning content and progression created based on a learner's individual needs and customized for efficient learning.

[0598] A "terminal" is a device or equipment on which a learner works on their learning plan, including a smartphone, smart glasses, a head-mounted display, or a robot.

[0599] "Feedback" refers to evaluations and comments provided by the server in real time in response to answers submitted by learners.

[0600] "Progress Data" means data that records a learner's progress during a learning session and is used to optimize subsequent learning plans.

[0601] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate questions and explanations that are optimal for learners.

[0602] A "prompt" is text data that is input into a generative AI model and is used to generate a learning plan based on the learner's requirements.

[0603] The present invention relates to a system that provides a user with a personalized, short-term learning experience by using an application installed on a device such as a smartphone, smart glasses, a head-mounted display, or a robot.

[0604] First, a user accesses the system and creates an account. The user enters the required information, such as their name, email address, and password, and sends it from their device to the server. The server encrypts and stores this information, and the user then accesses the system through the login screen.

[0605] Next, the user inputs their learning style, abilities, interests, and learning goals. For example, if a user prefers visual learning, is bad at math, and is interested in programming, they enter that information into the device. This information is sent to the server, which then creates and stores a learning profile.

[0606] When a user begins studying, they input questions that they find difficult or have been unable to solve into the system. These question data are sent from the device to a server, which then analyzes the input questions. Natural language processing technology such as SpaCy is used for the analysis. The server then searches a database for similar questions and their explanations, and generates a customized study plan based on that information.

[0607] The generated study plan is sent to the device, and the user begins a study session through the device. The user answers the questions and the answers are sent to the server. The server evaluates the answers and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to be attempted.

[0608] After a study session is completed, the server records the user's progress data, which is used to optimize the next study plan. For example, if a user shows progress on a particular task, the next study plan will offer more advanced problems.

[0609] Furthermore, the system uses a generative AI model to generate questions and explanations that are optimal for each learner, based on the user's learning profile and progress data, enabling more efficient learning.

[0610] Example prompts to input to a generative AI model:

[0611] User name: Tanaka-san

[0612] Uses: Visual learners, poor at math, interested in programming

[0613] Contents: Calculus at the second year of high school level

[0614] Problem: Solution to \(\int x^2 dx\)

[0615] Based on these prompts, the generative AI model provides a personalized learning plan for the user, enabling effective learning in a short amount of time.

[0616] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0617] Step 1:

[0618] A user accesses the system and creates an account. They enter the required information, such as their name, email address, and password, and send it from their terminal to the server. The server encrypts this information (using AES encryption) and stores it in a database. The input is user information (name, email address, and password), and the output is the encrypted user information.

[0619] Step 2:

[0620] A user accesses the system through a login screen. The server authenticates the login information and grants the user access rights. The input is the login information (email address, password) and the output is the authentication result (success or failure). If authentication is successful, the user proceeds to the next step.

[0621] Step 3:

[0622] The user inputs their learning style, ability, interests, and learning goals. This information is sent from the terminal to the server, which then generates a learning profile and stores it in a database. The input is the learning profile information (learning style, ability, interests, learning goals), and the output is the generated learning profile.

[0623] Step 4:

[0624] Users submit problems they have difficulty with or have been unable to solve to the system. The problem data is sent from the device to the server, which then analyzes the problem using natural language processing technology (SpaCy). The server then searches a database for similar problems and their explanations, and generates a customized study plan. The input is the problem data, and the output is a customized study plan.

[0625] Step 5:

[0626] The generated study plan is sent to the device, and the user starts a study session through the device. The user answers questions, and the answers are sent from the device to the server. The input is the user's answer, and the output is the state in which the answer has been sent to the server.

[0627] Step 6:

[0628] The server evaluates the user's answers and provides real-time feedback, including whether the answer is correct or incorrect, an explanation, and what to do next. The input is the user's answer and the output is the feedback. The server evaluates the answers using an evaluation algorithm.

[0629] Step 7:

[0630] After the study session ends, the server records the user's progress data. This data is used to optimize the next study plan. The input is the study session data (progress data) and the output is the recorded progress data. The server saves the progress data in a database and dynamically updates the next study plan.

[0631] Step 8:

[0632] The server uses a generative AI model to generate questions and explanations that are optimal for the learner. The generative AI model inputs learning profile and progress data and outputs optimal questions and explanations. The input is learning profile and progress data, and the output is generated questions and explanations.

[0633] Step 9:

[0634] The server uses the prompt to generate a learning plan based on the learner's requirements. The prompt includes the user's requirements (learning style, interests, learning content, etc.) and is input to the generative AI model. The input is the prompt, and the output is the learning plan.

[0635] Example prompts to input to a generative AI model:

[0636] User name: Yamada-san

[0637] Uses: Visual learners, poor at math, interested in programming

[0638] What you'll learn: University-level linear algebra

[0639] Problem: How to find the inverse of a matrix

[0640] Based on this prompt, the server uses a generative AI model to provide Yamada with an optimal learning plan, allowing the user to study effectively in a short amount of time.

[0641] 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.

[0642] The present invention combines an emotion engine with a system that provides learners with a personalized, short-term learning experience, which generates an individualized learning plan based on the learner's learning style, ability, interests, pace, and emotions.

[0643] An embodiment of the system operates as follows.

[0644] User Registration and Login

[0645] When a user first accesses the system, they create an account. They enter the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[0646] Setting up your learning profile

[0647] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[0648] Providing learning content

[0649] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[0650] Generate a personalized study plan

[0651] The server generates a customized study plan based on the study profile and the entered question data, and the study plan is sent to the terminal, which displays the contents to the user.

[0652] Introducing the Emotion Engine

[0653] During a study session, the device uses the user's facial expressions, tone of voice, and input patterns to acquire emotional data from the emotion engine. The server receives this emotional data and reflects it in the study plan. For example, if the user is feeling stressed, it can provide them with questions with a lower level of difficulty.

[0654] Start a study session

[0655] The user starts a learning session using the device, works on the provided questions, and inputs their answers. The device uses an emotion engine to measure the user's emotions in real time and transmits them to the server.

[0656] Real-time feedback

[0657] Each time a user answers a question, the device sends the answer to the server. The server evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to take. Furthermore, the server uses data from the emotion engine to provide appropriate motivation to the user.

[0658] Recording progress

[0659] After the learning session ends, the server records the user's progress and emotional data in a database, which is used to optimize the next learning plan.

[0660] Specific examples

[0661] For example, consider the case of a user named Yamada. After registering with the system and logging in, Yamada enters his learning profile, including the following: "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, Yamada submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0662] When Yamada starts a session, the device analyzes her facial expressions and tone of voice through an emotion engine. For example, if the emotion engine recognizes that Yamada is having difficulty solving a problem, the server will provide her with a slightly less difficult problem next time. If Yamada is feeling stressed, the server will provide feedback suggesting a short break to relax.

[0663] After the study session, Yamada's progress and emotional data are recorded and used to optimize his next study plan.

[0664] This allows Yamada to study effectively at his own pace and in line with his emotional state. In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[0665] The processing flow will be explained below.

[0666] Step 1:

[0667] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[0668] Step 2:

[0669] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[0670] Step 3:

[0671] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[0672] Step 4:

[0673] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[0674] Step 5:

[0675] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[0676] Step 6:

[0677] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[0678] Step 7:

[0679] The server analyzes the received problem data and searches the database for similar problems and explanations.

[0680] Step 8:

[0681] The server generates a customized learning plan based on the learning profile and analysis results.

[0682] Step 9:

[0683] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[0684] Step 10:

[0685] The device analyzes the user's facial expressions and tone of voice using an emotion engine and sends the emotion data to the server.

[0686] Step 11:

[0687] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[0688] Step 12:

[0689] The server receives the user's answer, evaluates it, generates evaluation results and feedback, and sends them to the device, taking into account the user's emotional data.

[0690] Step 13:

[0691] The device then displays the received feedback to the user, including whether the answer was correct or incorrect, an explanation, the next step to be taken, and motivation based on emotional data.

[0692] Step 14:

[0693] After the learning session is completed, the server records the user's progress and emotional data in a database.

[0694] Step 15:

[0695] The server analyzes the progress and emotion data to optimize the next learning plan, and a new learning plan is generated and used for the next learning session.

[0696] Step 16:

[0697] By repeating the above steps, users can study effectively at their own pace and in line with their emotional state.

[0698] Example 2

[0699] 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."

[0700] Conventional learning support systems have the problem of being unable to adequately provide individual learning plans that are tailored to each learner's learning style, ability, interests, and progress speed. Furthermore, they are unable to take into account the learner's emotions during learning, which can lead to a decrease in learning effectiveness if the learner continues to study while feeling stressed or anxious. Another problem is that the lack of real-time emotional feedback makes it difficult to respond immediately.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0702] In this invention, the server includes means for the learner to input their own learning style, ability, interests, and progress speed, means for the learner to input questions that they are weak at, means for the server to analyze the input questions and identify similar questions and their explanations, means for the server to generate a customized study plan based on the learner's learning profile and the input questions, means for the terminal to acquire emotional data of the learner while studying, and means for the server to dynamically adjust the study plan based on the emotional data. This makes it possible to provide an individually optimized study plan that incorporates the learner's emotions throughout the entire learning process, and realizes emotional feedback and appropriate responses in real time.

[0703] "Learning style" refers to the method or technique in which a learner learns most effectively.

[0704] "Ability" refers to the knowledge and skills that a learner has to solve a particular task or problem.

[0705] "Interest" refers to the degree of interest or concern a learner has in a particular field or topic.

[0706] "Progression rate" refers to the pace or speed at which a learner progresses through their studies.

[0707] "Learning Profile" refers to a collection of information including a learner's learning style, abilities, interests, progress rate, and other relevant data.

[0708] A "learning plan" refers to a learning guideline or plan created based on the learner's individual needs and circumstances.

[0709] "Emotional data" refers to data about emotions obtained by analyzing a learner's facial expressions, voice, input patterns, etc.

[0710] "Devices" refer to electronic devices such as computers, smartphones, and tablets used by learners.

[0711] A "server" is a computer that is the core of a learning system and is a device that processes and stores data and provides services.

[0712] "Feedback" refers to the immediate evaluation and advice given to a learner in response to their actions or answers.

[0713] The present invention combines an emotion engine with a system that provides a personalized, short-term learning experience for learners. The system generates an individualized learning plan taking into account the learner's learning style, ability, interests, progress rate, and emotions. An embodiment of the system is configured as follows:

[0714] User Registration and Login

[0715] The user accesses the system and creates an account. The user enters the required information such as name, email address, and password, and presses the registration button. The terminal sends the entered information to the server, which encrypts and stores the data. After completing registration, the user enters their email address and password on the login screen to log in to the system.

[0716] Setting up your learning profile

[0717] After logging in, the user enters their learning style, interests, abilities, learning goals, etc. on the "Profile Settings" page. The device sends this information to the server, which then creates a learning profile and stores it in a database.

[0718] Providing learning content

[0719] Users enter questions they have difficulty with or have been unable to solve into the system on the "Learning Contents" page. The device sends the question data to the server, which analyzes the question data and searches the database for similar questions and their explanations.

[0720] Generate a personalized study plan

[0721] The server generates a customized study plan based on the study profile and the entered question data, and the generated study plan is sent to the terminal and displayed to the user.

[0722] Introducing the Emotion Engine

[0723] During a learning session, the device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and passes the data to an emotion engine. The server then dynamically adjusts the learning plan based on the emotional data, such as lowering the difficulty level if the user is feeling stressed.

[0724] Start a study session

[0725] The user begins a learning session and works through the presented problems. The device measures emotional data in real time and transmits it to the server, which then provides appropriate feedback.

[0726] Real-time feedback

[0727] Each time a user answers a question, the device sends the answer data to the server, which then evaluates the answer and immediately provides a correct answer, explanation, and the next task to tackle. It also provides advice based on the user's emotional data to boost motivation.

[0728] Recording progress

[0729] After the learning session ends, the server records the user's progress and emotional data and uses it to optimize the next learning plan.

[0730] Hardware and software used

[0731] Server: We use common web servers and database servers (e.g., Apache, MySQL) for data processing and storage.

[0732] Device: The computer, smartphone, tablet, etc. that the user uses.

[0733] Software: Machine learning libraries such as TensorFlow are used for sentiment analysis, and HTML and JavaScript are used for the front end.

[0734] Specific examples

[0735] For example, consider a case where a learner uses the system. The learner registers with the system and enters a learning profile that includes "prefers visual learning," "is not good at math," and "is interested in programming." The learner then submits a calculus problem at the second-year high school level. The server analyzes this problem and identifies similar problems and their explanations.

[0736] When a learner starts a study session, the device uses an emotion engine to analyze the learner's facial expressions and tone of voice, and if the learner is feeling stressed, it will provide them with questions of lower difficulty and suggest appropriate breaks to relax.

[0737] Prompt Sentence Examples

[0738] 1. "Prefer visual learning," "are bad at math," and "are interested in programming" - Give us some calculus problems at the second-year high school level.

[0739] 2. Analyze the user's facial expressions and tone of voice and suggest a short break to relax if they are feeling stressed.

[0740] In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[0741] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0742] Step 1: User Registration

[0743] The user clicks the "New Registration" button on the system's access page and enters the necessary information into the input form, such as name, email address, and password. The device sends this information to the server. The server encrypts the entered data and stores it in a database. The user's personal information is stored as input, and encrypted data is stored as output.

[0744] Step 2: Log in

[0745] After completing registration, the user enters their email address and password on the login screen and presses the "Login" button. The device sends this to the server, which then verifies it against a database. The input is the user's login information, and the output is the authentication result (success or failure). If authentication is successful, the user is redirected to the home screen.

[0746] Step 3: Set up your learning profile

[0747] Users access the "Profile Settings" page from the home screen. They input their learning style, interests, abilities, and learning goals. The device sends this information to the server, which then generates a learning profile based on the input data and stores it in a database. Learning style and interests are stored as input, and the generated learning profile is stored as output.

[0748] Step 4: Provide learning content

[0749] On the "Learning Content" page, users enter questions they have difficulty with or have been unable to solve into the system. The device then sends the question data to the server. The server then uses natural language processing technology to analyze the question data and searches the database for similar questions and their explanations. The learner's question data is the input, and similar questions and their explanations are identified as the output.

[0750] Step 5: Generate a personalized study plan

[0751] The server uses an algorithm to generate a customized study plan based on the learning profile and input question data. The generated study plan is sent to the terminal and displayed to the user. The input is the learning profile and question data, and the output is the customized study plan.

[0752] Step 6: Implementing the Emotion Engine

[0753] During a study session, the device uses a camera and microphone to capture the user's facial expressions and tone of voice in real time. The device passes this data to an emotion engine to infer the user's emotions. The inference results are sent to a server, which dynamically adjusts the difficulty of the study plan and break suggestions based on the emotion data. The input is real-time emotion data, and the output is an adjusted study plan.

[0754] Step 7: Start your study session

[0755] The user clicks the "Start Learning Session" button to start a learning session. The user works through the presented questions and enters their answers. The device sends the answer data to the server, which evaluates the answers and generates feedback. The input is the user's answer data, and the output is feedback.

[0756] Step 8: Real-time feedback

[0757] Each time a user answers a question, the device sends the answer data to the server. The server evaluates the answer and provides real-time feedback on whether the answer was correct, an explanation, and the next step to tackle. It also provides motivational advice based on emotional data. The inputs are answer data and emotional data, and the output is immediate feedback.

[0758] Step 9: Record your progress

[0759] After the study session ends, the server records the user's progress and emotion data, which are used to optimize the next study plan. The inputs are the progress and emotion data from the session, and the output is the optimized next study plan.

[0760] (Application example 2)

[0761] 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."

[0762] In modern society, optimizing individual learning experiences is an important challenge, but existing systems lack personalized feedback that takes into account the learner's emotional state. Furthermore, in areas such as electronic payment, dynamic adjustment functions are needed to help users learn effectively and master operations. Systems that address these challenges and allow learners to progress at their own pace without stress or anxiety are needed.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0764] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, progress rate, and emotional state; a means for the learner to input questions or learning content that they are weak at; a means for the server to analyze the input questions or learning content and identify similar questions or learning content and their explanations; a means for the server to generate a customized learning plan based on the learner's learning profile and the input questions or learning content; a means for a terminal to enable the learner to work on the learning plan provided by the server; a means for an emotion engine to acquire emotional data of the learner using the learner's facial expressions, tone of voice, input patterns, etc.; a means for the server to dynamically adjust the learning plan based on the emotional data; and a means for evaluating the learner's answers or responses and providing feedback in real time, thereby making it possible to individually optimize the learner's learning experience and adjust it in real time according to the learner's emotional state.

[0765] "Learning style" refers to the way a learner finds it easy to understand or learn information.

[0766] "Ability" refers to the degree of skill or knowledge a learner has in a particular field or subject.

[0767] "Interest" refers to the degree of interest or involvement a learner has in a particular field or topic.

[0768] "Progression rate" refers to how quickly a learner masters the material.

[0769] "Emotional state" refers to the psychological state such as stress, excitement, and impatience that learners feel while studying.

[0770] "Problems or learning content" refers to tasks or educational materials that a learner must complete.

[0771] "Server" refers to a computing device for processing, analyzing, and optimizing data input from learners.

[0772] A "learning profile" refers to information entered by a learner summarizing their learning style, abilities, interests, pace of progress, and emotional state.

[0773] "Customized learning plan" refers to an individual learning schedule and content that is optimized based on the learner's learning profile.

[0774] "Terminal" refers to the device through which a learner accesses and works on the learning plan material provided by the server.

[0775] An "emotion engine" refers to software or hardware that analyzes a learner's facial expressions, tone of voice, input patterns, etc. to recognize their emotional state.

[0776] "Dynamic adjustment" refers to changing the learning plan in real time in response to the learner's ongoing performance and emotional state.

[0777] "Feedback" refers to the process of providing evaluation and instructional comments on answers or responses submitted by learners.

[0778] The present invention provides a system for providing a personalized learning experience to a learner. The system incorporates an emotion engine and includes a function for dynamically adjusting a learning plan based on the learner's emotional state. Specific embodiments of the system are described below.

[0779] System Configuration

[0780] The system consists of the following major components:

[0781] 1. Server

[0782] 2. Terminal

[0783] 3. Emotion Engine

[0784] server

[0785] The server is a computing device that processes, analyzes, and optimizes data input from learners. The server has the following functions:

[0786] There is a database function that allows learners to input their learning style, ability, interests, progress rate, and emotional state.

[0787] The server receives the question data and learning content entered by the learner and analyzes them. The analysis results include a means for identifying questions and explanations similar to the learner's answers.

[0788] Based on the learner's learning profile and input question data, the server generates a customized study plan that is optimized based on the learner's learning style, pace, and emotional state.

[0789] Terminal

[0790] The terminal is a device that displays the customized learning plan provided by the server and allows the learner to work on it. This terminal is mainly a smartphone, but other devices can also be considered.

[0791] The device's emotion engine uses the learner's facial expressions, tone of voice, input patterns, etc. to obtain the learner's emotional data.

[0792] Additionally, the device receives feedback from the server and provides it to the learner in real time, including information on whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[0793] Based on the emotion engine data, the device sends information to the server to dynamically adjust the learning plan.

[0794] Emotion Engine

[0795] The emotion engine is software that analyzes data such as the learner's facial expressions, tone of voice, and input patterns to recognize their emotional state. Specifically, the EmotionAnalyzer and LearningPlanGenerator are used.

[0796] The emotion engine analyzes the learner's emotional state in real time during the learning session and sends the data to the server, which uses the data to adjust the learning plan appropriately.

[0797] For example, if a learner is feeling stressed, the server can provide them with questions of lower difficulty, or if they are highly motivated, it can present them with questions of higher difficulty.

[0798] Specific examples

[0799] For example, a learner may enter a learning profile stating that they "prefer visual learning," "are not good at math," and "are interested in programming." The learner then submits a high school calculus problem to the system. The server analyzes this problem and identifies similar problems and their explanations. During the learning session, the device analyzes the learner's facial expressions and tone of voice through an emotion engine. If the learner is struggling, the server can provide a less difficult problem next time. Also, if the learner is feeling stressed, feedback is provided suggesting a short break to relax.

[0800] Example input to a generative AI model

[0801] The next challenge to tackle is the security of electronic payments. The emotion engine analyzes your emotions and provides appropriate motivation. If you feel that "learning how to pay is difficult," please suggest what feedback would be appropriate for the next step.

[0802] Thus, the present invention provides a system that can individually optimize a learner's learning experience and adjust it in real time according to their emotional state.

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Step 1:

[0805] A user accesses the system and creates an account.

[0806] How it works: The user enters the required information, such as their name, email address, and password, and the device sends it to the server, which then encrypts and stores the information and creates a user account.

[0807] Input: Name, Email Address, Password

[0808] Output: User account created and saved

[0809] Step 2:

[0810] The user accesses the system through the login screen.

[0811] How it works: The user logs in using their registered email address and password, and the device sends this authentication information to the server, which verifies the information entered and returns the authentication result.

[0812] Input: Email address, Password

[0813] Output: Authentication result (success or failure)

[0814] Step 3:

[0815] New users set up their learning profile.

[0816] Specific operation: After logging in, the user inputs their learning style, ability, interests, progress rate, and emotional state, and the device sends this information to the server, which then generates a learning profile and stores it in a database.

[0817] Inputs: learning style, ability, interest, progress rate, emotional state

[0818] Output: Generate and save a learning profile

[0819] Step 4:

[0820] Users can input their own weak questions or learning content.

[0821] Specific operation: The user inputs questions that they were unable to solve or that they found difficult, and the device sends them to the server. The server receives the submitted questions and begins analyzing them.

[0822] Input: Problems you couldn't solve, problems you're not good at

[0823] Output: Receive problem data and start analysis

[0824] Step 5:

[0825] The server analyzes the submitted problem and identifies similar problems and their explanations.

[0826] Specific operation: The server analyzes the received problem data and searches the database for similar problems. The search results include similar problems with explanations.

[0827] Input: Problem data

[0828] Output: Similar problems and their explanations

[0829] Step 6:

[0830] The server generates a customized learning plan based on the learner's learning profile and input question data.

[0831] Specific operation: The server combines the learning profile and analysis results to generate an individually optimized learning plan and send it to the device.

[0832] Input: Learning profile, analysis results of problem data

[0833] Output: Generate a customized learning plan

[0834] Step 7:

[0835] The terminal allows the learner to work through a learning plan provided by the server.

[0836] Specific operations: The device displays the received learning plan and provides an interface for the user to work on it. The user answers the assignments and enters the results.

[0837] Input: Study Plan

[0838] Output: Display of the learning plan and provision of an interface

[0839] Step 8:

[0840] The emotion engine obtains the learner's emotional data using the learner's facial expressions, tone of voice, input patterns, etc.

[0841] How it works: The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data to identify their emotional state, which is then sent to a server.

[0842] Input: facial expression data, tone of voice, input pattern

[0843] Output: Emotion data

[0844] Step 9:

[0845] The server dynamically adjusts the learning plan based on the emotional data.

[0846] Specific operation: The server analyzes the received emotional data and adjusts the difficulty and content of the study plan. For example, if the user is feeling stressed, it will provide questions with lower difficulty.

[0847] Input: Emotion data

[0848] Output: A dynamically adjusted learning plan

[0849] Step 10:

[0850] Evaluate learners' answers or responses and provide feedback in real time.

[0851] How it works: The device sends the user's answers to the server, which evaluates them and provides real-time feedback, such as whether the answer was correct, additional explanations, and the next assignment.

[0852] Input: User's answer or response

[0853] Output: Real-time feedback

[0854] Step 11:

[0855] Stores learner progress and sentiment data and optimizes the next learning plan based on that.

[0856] What it does: After a learning session ends, the server stores progress and emotion data in a database, which is used to optimize the next learning plan.

[0857] Input: Progress data, emotion data

[0858] Output: Save data and optimize the next learning plan

[0859] 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.

[0860] 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.

[0861] 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.

[0862] [Third embodiment]

[0863] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0864] 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.

[0865] 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).

[0866] 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.

[0867] 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.

[0868] 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).

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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."

[0875] The present invention relates to a system that provides a learner with a personalized, short-term learning experience by generating an individualized learning plan that takes into account the learner's learning style, abilities, interests, and pace of progress.

[0876] The system embodiment operates in the following general manner.

[0877] User Registration and Login

[0878] This is the step where a user first accesses the system and creates an account. The user enters the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[0879] Setting up your learning profile

[0880] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[0881] Providing learning content

[0882] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[0883] Generate a personalized study plan

[0884] The server generates a customized learning plan based on the learner's learning profile and the entered problem data. This plan includes problems that the learner should focus on and explanations for them. The generated learning plan is then sent to the device.

[0885] Start a study session

[0886] The user starts a study session using a terminal. The terminal displays questions and explanations provided by the server to the user. The user progresses at their own pace and enters their answers.

[0887] Real-time feedback

[0888] Each time a user answers a question, the device sends the answer to the server, which evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to try.

[0889] Recording progress

[0890] After a study session is completed, the server records the user's progress, which is used to optimize future study plans. For example, if a user has made progress in a particular area of ​​calculus, the next study plan will offer more advanced problems.

[0891] Specific examples

[0892] For example, consider a user named Tanaka. After registering with the system and logging in, Tanaka enters his learning profile, including such things as "prefers visual learning," "is bad at math," and "interested in programming." Then, Tanaka submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0893] Tanaka begins a study session based on a personalized study plan provided by the server. After solving each problem, he submits his answer to the server and receives real-time feedback. As he studies, the server records his progress and provides him with the most appropriate problems for his next study session.

[0894] This allows Tanaka to study effectively at his own pace. In this way, the system of the present invention provides the learner with a personalized and efficient learning experience.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[0898] Step 2:

[0899] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[0900] Step 3:

[0901] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[0902] Step 4:

[0903] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[0904] Step 5:

[0905] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[0906] Step 6:

[0907] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[0908] Step 7:

[0909] The server analyzes the received problem data and searches the database for similar problems and explanations.

[0910] Step 8:

[0911] The server generates a customized learning plan based on the learning profile and analysis results.

[0912] Step 9:

[0913] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[0914] Step 10:

[0915] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[0916] Step 11:

[0917] The server receives the user's answers, evaluates them, generates evaluation results and feedback, and sends them to the device.

[0918] Step 12:

[0919] The device then displays the feedback it receives to the user, including whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[0920] Step 13:

[0921] After the learning session is completed, the server records the user's progress data in a database.

[0922] Step 14:

[0923] The server analyzes the progress data and optimizes the next study plan. A new study plan is generated and used for the next study session.

[0924] Step 15:

[0925] By repeating the above steps, the user can effectively progress through their studies at their own pace.

[0926] Example 1

[0927] 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."

[0928] Conventional learning systems have difficulty providing personalized learning plans that take into account each learner's learning style and progress speed. They also lack the functionality to provide real-time feedback, record learner progress, and reflect it in the next learning plan. This has led to the problem of learners being unable to study effectively at their own pace.

[0929] 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.

[0930] In this invention, the server includes: a means for a learner to input their own learning style, ability, interests, and progress rate; a means for the learner to input questions that they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing feedback in real time; a means for recording the learner's progress data after the study session ends and optimizing the next study plan; a means for the learner to input account information on a login screen to access the system; and a means for inputting learning style and interests after setting up a study profile. This makes it possible to create a personalized study plan according to the individual circumstances of the learner, provide feedback in real time, and optimize learning.

[0931] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[0932] "Ability" indicates a learner's level of knowledge, skills, and understanding, and refers to the level of academic ability and skill in a particular field.

[0933] "Interests" refers to areas or topics that a learner is particularly interested in.

[0934] "Progression rate" refers to the speed at which a learner comprehends and progresses through the learning content.

[0935] An "input problem" is a problem that a learner inputs to the system that they find difficult to solve.

[0936] "Server" refers to the computer system that handles the main processing and data management of the learning system.

[0937] "Devices" refers to devices such as computers, tablets, and smartphones used by learners.

[0938] "Learning Profile" refers to a collection of data including a learner's learning style, interests, abilities, and rate of progress.

[0939] A "customized learning plan" refers to a learning plan or set of tasks that are specifically created to meet the individual needs and circumstances of a learner.

[0940] "Real-time feedback" refers to providing instant assessment and comments to learners as soon as they submit their answers.

[0941] "Progress data" refers to data that shows how much progress a learner has made in their studies and what results they have achieved.

[0942] "Account information" refers to the authentication information a learner uses to access the system, and typically includes a name, email address, password, etc.

[0943] "Login screen" refers to the screen where a user enters authentication information to access the system.

[0944] A "learning session" refers to a series of learning activities that a learner performs using the system within a set period of time.

[0945] This invention is a system that provides learners with a personalized, short-term learning experience, generating an individual learning plan based on the learner's learning style, ability, interests, and progress rate. The system is operated through a device such as a web browser or mobile app, with the main processing performed by a server.

[0946] Hardware and software used

[0947] The system uses the following hardware and software:

[0948] Terminal: The device operated by the user (PC, tablet, smartphone, etc.)

[0949] Server: A remote computer that processes and stores training data.

[0950] Database: A storage system for storing learning profiles, progress data, and question data.

[0951] Web application / Mobile application: Provides an interface for users to access their learning plans

[0952] System overview and specific operation

[0953] User Registration and Login

[0954] When a user first accesses the system, they create an account. After entering the required information, such as their name, email address, and password, the device sends this information to the server, which encrypts it and stores it in a database. The user then accesses the system using the login screen.

[0955] Setting up your learning profile

[0956] Immediately after logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they enter information such as "I prefer visual learning," "I'm not good at math," or "I'm interested in programming." This information is sent from the device to the server, which then creates a learning profile and stores it in a database.

[0957] Providing learning content

[0958] The user enters a problem they were unable to solve into the system. This problem data is sent from the device to the server, which analyzes it and searches the database for similar problems and their explanations. Links and files for the corresponding learning content are then sent to the user's device.

[0959] Generate a personalized study plan

[0960] The server generates a customized study plan based on the learning profile and the entered problem data. The study plan includes the problems to focus on and their explanations. The study plan is then sent to the device.

[0961] Start a study session

[0962] The user starts a study session using a terminal, which displays questions and explanations provided by the server, and the user progresses at their own pace and enters their answers.

[0963] Real-time feedback

[0964] Each time a user answers a question, the answer is sent from the device to the server. The server evaluates the answer and generates real-time feedback, including whether the answer was correct or incorrect, a detailed explanation, and the next step to try. The feedback is sent to the device and displayed to the user.

[0965] Recording progress

[0966] After a study session is completed, the device sends the session results data to the server, which records the progress data it receives and uses it to optimize future study plans. For example, if a user makes progress in a particular area of ​​calculus, that progress can be reflected in their next study plan.

[0967] Specific examples

[0968] For example, consider a user named A. After registering with the system and logging in, A enters his learning profile, such as "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, A submits a high school-level calculus problem to the system. The server analyzes this calculus problem and identifies similar problems and their explanations.

[0969] Person A begins a study session based on a personalized study plan provided by the server. As he solves each problem, he sends his answers to the server and receives real-time feedback. As he continues to study, the server records Person A's progress and provides him with the most appropriate problems for his next study session.

[0970] Prompt Sentence Examples

[0971] "Generate a personalized learning plan for Person A, who prefers visual learning, struggles with math, and is interested in programming."

[0972] In this way, the system of the present invention provides a personalized learning experience that is tailored to the individual circumstances of each learner.

[0973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0974] Step 1: User Registration and Login

[0975] 1.1 A user accesses the system and opens the account registration page. They enter personal information such as their name, email address, and password. The device collects the input information.

[0976] 1.2 The device sends the collected personal information to the server, which receives the data and performs encryption processing.

[0977] 1.3 The server stores the encrypted personal information in the database. The output is a notification that the data has been saved to the database.

[0978] 1.4 A user enters an email address and password on a login screen. The input is the user's authentication information.

[0979] 1.5 The terminal sends the authentication information to the server. The server checks the authentication information against the database information, and if authentication is successful, the user is logged in. The output is a notification of successful login.

[0980] Step 2: Set up your learning profile

[0981] 2.1 After logging in, the user opens the learning profile setting page. Inputs include learning style, interests, abilities, learning goals, etc. The device collects the input information.

[0982] 2.2 The device sends the collected learning profile information to the server. The server receives the data and stores it in the profile database. The output is a notification that the profile has been set up.

[0983] 2.3 As a specific action, the user inputs "I prefer visual learning," "I'm not good at math," and "I'm interested in programming."

[0984] Step 3: Provide learning content

[0985] 3.1 The user inputs the problem they were unable to solve into the system as text or images. The input is the problem data. The terminal collects the problem data.

[0986] 3.2 The terminal sends the collected problem data to the server, which receives the data and performs problem analysis processing.

[0987] 3.3 The server searches the database for similar problems and their explanations. The output is data on similar problems and their explanations.

[0988] 3.4 The terminal displays the similar questions and explanations received from the server to the user.

[0989] Step 4: Generate a personalized study plan

[0990] 4.1 The server generates a customized learning plan based on the learning profile and the input question data. The input is the learning profile and the question data.

[0991] 4.2 Include the focus questions and explanations in the server-generated study plan. The output is a customized study plan.

[0992] 4.3 The device displays the study plan received from the server to the user, and the user begins studying according to the plan.

[0993] Step 5: Start your study session

[0994] 5.1 The user starts a learning session using a terminal. The input is the start command.

[0995] 5.2 The terminal displays the questions and explanations provided by the server to the user. The output is the displayed learning content.

[0996] 5.3 The user works on the problem and inputs the answer. The input is the answer data.

[0997] Step 6: Real-time feedback

[0998] 6.1 When the user answers a question, the terminal sends the answer to the server. The input is the answer data.

[0999] 6.2 The server evaluates the answer data and generates feedback in real time. Data processing involves determining whether the answer is correct or incorrect and generating an explanation. The output is the generated feedback data.

[1000] 6.3 The device displays the feedback received from the server to the user. Specifically, the user immediately checks the feedback and proceeds to the next task.

[1001] Step 7: Record your progress

[1002] 7.1 After the learning session is completed, the device sends the session result data to the server. The input is the learning progress data.

[1003] 7.2 The server records the received progress data and stores it in a database. The output is the stored progress data.

[1004] 7.3 Progress data is used to optimize your next study plan, specifically providing more challenging and user-appropriate questions in your next study session.

[1005] (Application example 1)

[1006] 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."

[1007] Conventional learning systems have faced challenges in effectively providing learners with optimal learning experiences tailored to their individual learning styles and interests. In particular, there was a lack of technology to provide short, personalized learning experiences using smartphones and smart devices. Furthermore, there was also a lack of systems that could dynamically update learning plans based on learners' progress and provide appropriate feedback in real time. This resulted in reduced learning efficiency and prevented learners from effectively progressing through their studies at their own pace.

[1008] 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.

[1009] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, and progress rate; a means for the learner to input questions they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing real-time feedback; a means installed on a smartphone, smart glasses, head-mounted display, or robot to allow the learner to receive a personalized, short-term study experience; a means for recording the learner's progress data during the study session and reflecting it in the next study plan; a means for generating questions and their explanations optimal for the learner using a generative AI model; and a means for generating a study plan based on the learner's requests using prompt sentences. This allows the learner to receive an optimal learning experience tailored to their learning style and interests. Furthermore, the learner's progress data can be recorded while receiving real-time feedback and reflected in the next study plan, improving learning efficiency.

[1010] "Learning style" refers to the unique learning methods and procedures that each learner has, and refers to different senses and approaches such as visual, auditory, and experiential.

[1011] "Ability" refers to the overall level of knowledge and skills that a learner possesses, including proficiency in a particular field.

[1012] "Interest" refers to the interest a learner has in a particular field or topic, and is an important factor that influences motivation to learn.

[1013] "Progression speed" is an indicator of how quickly a learner can understand and progress through the learning content.

[1014] A "problem" is a task or question that a learner must solve, and is an important element for assessing learning progress.

[1015] "Explanation" refers to additional information or explanation about the question and its answer, provided to help learners deepen their understanding.

[1016] A "learning plan" is a plan of learning content and progression created based on a learner's individual needs and customized for efficient learning.

[1017] A "terminal" is a device or equipment on which a learner works on their learning plan, including a smartphone, smart glasses, a head-mounted display, or a robot.

[1018] "Feedback" refers to evaluations and comments provided by the server in real time in response to answers submitted by learners.

[1019] "Progress Data" means data that records a learner's progress during a learning session and is used to optimize subsequent learning plans.

[1020] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate questions and explanations that are optimal for learners.

[1021] A "prompt" is text data that is input into a generative AI model and is used to generate a learning plan based on the learner's requirements.

[1022] The present invention relates to a system that provides a user with a personalized, short-term learning experience by using an application installed on a device such as a smartphone, smart glasses, a head-mounted display, or a robot.

[1023] First, a user accesses the system and creates an account. The user enters the required information, such as their name, email address, and password, and sends it from their device to the server. The server encrypts and stores this information, and the user then accesses the system through the login screen.

[1024] Next, the user inputs their learning style, abilities, interests, and learning goals. For example, if a user prefers visual learning, is bad at math, and is interested in programming, they enter that information into the device. This information is sent to the server, which then creates and stores a learning profile.

[1025] When a user begins studying, they input questions that they find difficult or have been unable to solve into the system. These question data are sent from the device to a server, which then analyzes the input questions. Natural language processing technology such as SpaCy is used for the analysis. The server then searches a database for similar questions and their explanations, and generates a customized study plan based on that information.

[1026] The generated study plan is sent to the device, and the user begins a study session through the device. The user answers the questions and the answers are sent to the server. The server evaluates the answers and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to be attempted.

[1027] After a study session is completed, the server records the user's progress data, which is used to optimize the next study plan. For example, if a user shows progress on a particular task, the next study plan will offer more advanced problems.

[1028] Furthermore, the system uses a generative AI model to generate questions and explanations that are optimal for each learner, based on the user's learning profile and progress data, enabling more efficient learning.

[1029] Example prompts to input to a generative AI model:

[1030] User name: Tanaka-san

[1031] Uses: Visual learners, poor at math, interested in programming

[1032] Contents: Calculus at the second year of high school level

[1033] Problem: Solution to \(\int x^2 dx\)

[1034] Based on these prompts, the generative AI model provides a personalized learning plan for the user, enabling effective learning in a short amount of time.

[1035] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1036] Step 1:

[1037] A user accesses the system and creates an account. They enter the required information, such as their name, email address, and password, and send it from their terminal to the server. The server encrypts this information (using AES encryption) and stores it in a database. The input is user information (name, email address, and password), and the output is the encrypted user information.

[1038] Step 2:

[1039] A user accesses the system through a login screen. The server authenticates the login information and grants the user access rights. The input is the login information (email address, password) and the output is the authentication result (success or failure). If authentication is successful, the user proceeds to the next step.

[1040] Step 3:

[1041] The user inputs their learning style, ability, interests, and learning goals. This information is sent from the terminal to the server, which then generates a learning profile and stores it in a database. The input is the learning profile information (learning style, ability, interests, learning goals), and the output is the generated learning profile.

[1042] Step 4:

[1043] Users submit problems they have difficulty with or have been unable to solve to the system. The problem data is sent from the device to the server, which then analyzes the problem using natural language processing technology (SpaCy). The server then searches a database for similar problems and their explanations, and generates a customized study plan. The input is the problem data, and the output is a customized study plan.

[1044] Step 5:

[1045] The generated study plan is sent to the device, and the user starts a study session through the device. The user answers questions, and the answers are sent from the device to the server. The input is the user's answer, and the output is the state in which the answer has been sent to the server.

[1046] Step 6:

[1047] The server evaluates the user's answers and provides real-time feedback, including whether the answer is correct or incorrect, an explanation, and what to do next. The input is the user's answer and the output is the feedback. The server evaluates the answers using an evaluation algorithm.

[1048] Step 7:

[1049] After the study session ends, the server records the user's progress data. This data is used to optimize the next study plan. The input is the study session data (progress data) and the output is the recorded progress data. The server saves the progress data in a database and dynamically updates the next study plan.

[1050] Step 8:

[1051] The server uses a generative AI model to generate questions and explanations that are optimal for the learner. The generative AI model inputs learning profile and progress data and outputs optimal questions and explanations. The input is learning profile and progress data, and the output is generated questions and explanations.

[1052] Step 9:

[1053] The server uses the prompt to generate a learning plan based on the learner's requirements. The prompt includes the user's requirements (learning style, interests, learning content, etc.) and is input to the generative AI model. The input is the prompt, and the output is the learning plan.

[1054] Example prompts to input to a generative AI model:

[1055] User name: Yamada-san

[1056] Uses: Visual learners, poor at math, interested in programming

[1057] What you'll learn: University-level linear algebra

[1058] Problem: How to find the inverse of a matrix

[1059] Based on this prompt, the server uses a generative AI model to provide Yamada with an optimal learning plan, allowing the user to study effectively in a short amount of time.

[1060] 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.

[1061] The present invention combines an emotion engine with a system that provides learners with a personalized, short-term learning experience, which generates an individualized learning plan based on the learner's learning style, ability, interests, pace, and emotions.

[1062] An embodiment of the system operates as follows.

[1063] User Registration and Login

[1064] When a user first accesses the system, they create an account. They enter the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[1065] Setting up your learning profile

[1066] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[1067] Providing learning content

[1068] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[1069] Generate a personalized study plan

[1070] The server generates a customized study plan based on the study profile and the entered question data, and the study plan is sent to the terminal, which displays the contents to the user.

[1071] Introducing the Emotion Engine

[1072] During a study session, the device uses the user's facial expressions, tone of voice, and input patterns to acquire emotional data from the emotion engine. The server receives this emotional data and reflects it in the study plan. For example, if the user is feeling stressed, it can provide them with questions with a lower level of difficulty.

[1073] Start a study session

[1074] The user starts a learning session using the device, works on the provided questions, and inputs their answers. The device uses an emotion engine to measure the user's emotions in real time and transmits them to the server.

[1075] Real-time feedback

[1076] Each time a user answers a question, the device sends the answer to the server. The server evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to take. Furthermore, the server uses data from the emotion engine to provide appropriate motivation to the user.

[1077] Recording progress

[1078] After the learning session ends, the server records the user's progress and emotional data in a database, which is used to optimize the next learning plan.

[1079] Specific examples

[1080] For example, consider the case of a user named Yamada. After registering with the system and logging in, Yamada enters his learning profile, including the following: "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, Yamada submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[1081] When Yamada starts a session, the device analyzes her facial expressions and tone of voice through an emotion engine. For example, if the emotion engine recognizes that Yamada is having difficulty solving a problem, the server will provide her with a slightly less difficult problem next time. If Yamada is feeling stressed, the server will provide feedback suggesting a short break to relax.

[1082] After the study session, Yamada's progress and emotional data are recorded and used to optimize his next study plan.

[1083] This allows Yamada to study effectively at his own pace and in line with his emotional state. In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[1087] Step 2:

[1088] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[1089] Step 3:

[1090] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[1091] Step 4:

[1092] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[1093] Step 5:

[1094] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[1095] Step 6:

[1096] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[1097] Step 7:

[1098] The server analyzes the received problem data and searches the database for similar problems and explanations.

[1099] Step 8:

[1100] The server generates a customized learning plan based on the learning profile and analysis results.

[1101] Step 9:

[1102] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[1103] Step 10:

[1104] The device analyzes the user's facial expressions and tone of voice using an emotion engine and sends the emotion data to the server.

[1105] Step 11:

[1106] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[1107] Step 12:

[1108] The server receives the user's answer, evaluates it, generates evaluation results and feedback, and sends them to the device, taking into account the user's emotional data.

[1109] Step 13:

[1110] The device then displays the received feedback to the user, including whether the answer was correct or incorrect, an explanation, the next step to be taken, and motivation based on emotional data.

[1111] Step 14:

[1112] After the learning session is completed, the server records the user's progress and emotional data in a database.

[1113] Step 15:

[1114] The server analyzes the progress and emotion data to optimize the next learning plan, and a new learning plan is generated and used for the next learning session.

[1115] Step 16:

[1116] By repeating the above steps, users can study effectively at their own pace and in line with their emotional state.

[1117] Example 2

[1118] 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."

[1119] Conventional learning support systems have the problem of being unable to adequately provide individual learning plans that are tailored to each learner's learning style, ability, interests, and progress speed. Furthermore, they are unable to take into account the learner's emotions during learning, which can lead to a decrease in learning effectiveness if the learner continues to study while feeling stressed or anxious. Another problem is that the lack of real-time emotional feedback makes it difficult to respond immediately.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1121] In this invention, the server includes means for the learner to input their own learning style, ability, interests, and progress speed, means for the learner to input questions that they are weak at, means for the server to analyze the input questions and identify similar questions and their explanations, means for the server to generate a customized study plan based on the learner's learning profile and the input questions, means for the terminal to acquire emotional data of the learner while studying, and means for the server to dynamically adjust the study plan based on the emotional data. This makes it possible to provide an individually optimized study plan that incorporates the learner's emotions throughout the entire learning process, and realizes emotional feedback and appropriate responses in real time.

[1122] "Learning style" refers to the method or technique in which a learner learns most effectively.

[1123] "Ability" refers to the knowledge and skills that a learner has to solve a particular task or problem.

[1124] "Interest" refers to the degree of interest or concern a learner has in a particular field or topic.

[1125] "Progression rate" refers to the pace or speed at which a learner progresses through their studies.

[1126] "Learning Profile" refers to a collection of information including a learner's learning style, abilities, interests, progress rate, and other relevant data.

[1127] A "learning plan" refers to a learning guideline or plan created based on the learner's individual needs and circumstances.

[1128] "Emotional data" refers to data about emotions obtained by analyzing a learner's facial expressions, voice, input patterns, etc.

[1129] "Devices" refer to electronic devices such as computers, smartphones, and tablets used by learners.

[1130] A "server" is a computer that is the core of a learning system and is a device that processes and stores data and provides services.

[1131] "Feedback" refers to the immediate evaluation and advice given to a learner in response to their actions or answers.

[1132] The present invention combines an emotion engine with a system that provides a personalized, short-term learning experience for learners. The system generates an individualized learning plan taking into account the learner's learning style, ability, interests, progress rate, and emotions. An embodiment of the system is configured as follows:

[1133] User Registration and Login

[1134] The user accesses the system and creates an account. The user enters the required information such as name, email address, and password, and presses the registration button. The terminal sends the entered information to the server, which encrypts and stores the data. After completing registration, the user enters their email address and password on the login screen to log in to the system.

[1135] Setting up your learning profile

[1136] After logging in, the user enters their learning style, interests, abilities, learning goals, etc. on the "Profile Settings" page. The device sends this information to the server, which then creates a learning profile and stores it in a database.

[1137] Providing learning content

[1138] Users enter questions they have difficulty with or have been unable to solve into the system on the "Learning Contents" page. The device sends the question data to the server, which analyzes the question data and searches the database for similar questions and their explanations.

[1139] Generate a personalized study plan

[1140] The server generates a customized study plan based on the study profile and the entered question data, and the generated study plan is sent to the terminal and displayed to the user.

[1141] Introducing the Emotion Engine

[1142] During a learning session, the device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and passes the data to an emotion engine. The server then dynamically adjusts the learning plan based on the emotional data, such as lowering the difficulty level if the user is feeling stressed.

[1143] Start a study session

[1144] The user begins a learning session and works through the presented problems. The device measures emotional data in real time and transmits it to the server, which then provides appropriate feedback.

[1145] Real-time feedback

[1146] Each time a user answers a question, the device sends the answer data to the server, which then evaluates the answer and immediately provides a correct answer, explanation, and the next task to tackle. It also provides advice based on the user's emotional data to boost motivation.

[1147] Recording progress

[1148] After the learning session ends, the server records the user's progress and emotional data and uses it to optimize the next learning plan.

[1149] Hardware and software used

[1150] Server: We use common web servers and database servers (e.g., Apache, MySQL) for data processing and storage.

[1151] Device: The computer, smartphone, tablet, etc. that the user uses.

[1152] Software: Machine learning libraries such as TensorFlow are used for sentiment analysis, and HTML and JavaScript are used for the front end.

[1153] Specific examples

[1154] For example, consider a case where a learner uses the system. The learner registers with the system and enters a learning profile that includes "prefers visual learning," "is not good at math," and "is interested in programming." The learner then submits a calculus problem at the second-year high school level. The server analyzes this problem and identifies similar problems and their explanations.

[1155] When a learner starts a study session, the device uses an emotion engine to analyze the learner's facial expressions and tone of voice, and if the learner is feeling stressed, it will provide them with questions of lower difficulty and suggest appropriate breaks to relax.

[1156] Prompt Sentence Examples

[1157] 1. "Prefer visual learning," "are bad at math," and "are interested in programming" - Give us some calculus problems at the second-year high school level.

[1158] 2. Analyze the user's facial expressions and tone of voice and suggest a short break to relax if they are feeling stressed.

[1159] In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[1160] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1161] Step 1: User Registration

[1162] The user clicks the "New Registration" button on the system's access page and enters the necessary information into the input form, such as name, email address, and password. The device sends this information to the server. The server encrypts the entered data and stores it in a database. The user's personal information is stored as input, and encrypted data is stored as output.

[1163] Step 2: Log in

[1164] After completing registration, the user enters their email address and password on the login screen and presses the "Login" button. The device sends this to the server, which then verifies it against a database. The input is the user's login information, and the output is the authentication result (success or failure). If authentication is successful, the user is redirected to the home screen.

[1165] Step 3: Set up your learning profile

[1166] Users access the "Profile Settings" page from the home screen. They input their learning style, interests, abilities, and learning goals. The device sends this information to the server, which then generates a learning profile based on the input data and stores it in a database. Learning style and interests are stored as input, and the generated learning profile is stored as output.

[1167] Step 4: Provide learning content

[1168] On the "Learning Content" page, users enter questions they have difficulty with or have been unable to solve into the system. The device then sends the question data to the server. The server then uses natural language processing technology to analyze the question data and searches the database for similar questions and their explanations. The learner's question data is the input, and similar questions and their explanations are identified as the output.

[1169] Step 5: Generate a personalized study plan

[1170] The server uses an algorithm to generate a customized study plan based on the learning profile and input question data. The generated study plan is sent to the terminal and displayed to the user. The input is the learning profile and question data, and the output is the customized study plan.

[1171] Step 6: Implementing the Emotion Engine

[1172] During a study session, the device uses a camera and microphone to capture the user's facial expressions and tone of voice in real time. The device passes this data to an emotion engine to infer the user's emotions. The inference results are sent to a server, which dynamically adjusts the difficulty of the study plan and break suggestions based on the emotion data. The input is real-time emotion data, and the output is an adjusted study plan.

[1173] Step 7: Start your study session

[1174] The user clicks the "Start Learning Session" button to start a learning session. The user works through the presented questions and enters their answers. The device sends the answer data to the server, which evaluates the answers and generates feedback. The input is the user's answer data, and the output is feedback.

[1175] Step 8: Real-time feedback

[1176] Each time a user answers a question, the device sends the answer data to the server. The server evaluates the answer and provides real-time feedback on whether the answer was correct, an explanation, and the next step to tackle. It also provides motivational advice based on emotional data. The inputs are answer data and emotional data, and the output is immediate feedback.

[1177] Step 9: Record your progress

[1178] After the study session ends, the server records the user's progress and emotion data, which are used to optimize the next study plan. The inputs are the progress and emotion data from the session, and the output is the optimized next study plan.

[1179] (Application example 2)

[1180] 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."

[1181] In modern society, optimizing individual learning experiences is an important challenge, but existing systems lack personalized feedback that takes into account the learner's emotional state. Furthermore, in areas such as electronic payment, dynamic adjustment functions are needed to help users learn effectively and master operations. Systems that address these challenges and allow learners to progress at their own pace without stress or anxiety are needed.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1183] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, progress rate, and emotional state; a means for the learner to input questions or learning content that they are weak at; a means for the server to analyze the input questions or learning content and identify similar questions or learning content and their explanations; a means for the server to generate a customized learning plan based on the learner's learning profile and the input questions or learning content; a means for a terminal to enable the learner to work on the learning plan provided by the server; a means for an emotion engine to acquire emotional data of the learner using the learner's facial expressions, tone of voice, input patterns, etc.; a means for the server to dynamically adjust the learning plan based on the emotional data; and a means for evaluating the learner's answers or responses and providing feedback in real time, thereby making it possible to individually optimize the learner's learning experience and adjust it in real time according to the learner's emotional state.

[1184] "Learning style" refers to the way a learner finds it easy to understand or learn information.

[1185] "Ability" refers to the degree of skill or knowledge a learner has in a particular field or subject.

[1186] "Interest" refers to the degree of interest or involvement a learner has in a particular field or topic.

[1187] "Progression rate" refers to how quickly a learner masters the material.

[1188] "Emotional state" refers to the psychological state such as stress, excitement, and impatience that learners feel while studying.

[1189] "Problems or learning content" refers to tasks or educational materials that a learner must complete.

[1190] "Server" refers to a computing device for processing, analyzing, and optimizing data input from learners.

[1191] A "learning profile" refers to information entered by a learner summarizing their learning style, abilities, interests, pace of progress, and emotional state.

[1192] "Customized learning plan" refers to an individual learning schedule and content that is optimized based on the learner's learning profile.

[1193] "Terminal" refers to the device through which a learner accesses and works on the learning plan material provided by the server.

[1194] An "emotion engine" refers to software or hardware that analyzes a learner's facial expressions, tone of voice, input patterns, etc. to recognize their emotional state.

[1195] "Dynamic adjustment" refers to changing the learning plan in real time in response to the learner's ongoing performance and emotional state.

[1196] "Feedback" refers to the process of providing evaluation and instructional comments on answers or responses submitted by learners.

[1197] The present invention provides a system for providing a personalized learning experience to a learner. The system incorporates an emotion engine and includes a function for dynamically adjusting a learning plan based on the learner's emotional state. Specific embodiments of the system are described below.

[1198] System Configuration

[1199] The system consists of the following major components:

[1200] 1. Server

[1201] 2. Terminal

[1202] 3. Emotion Engine

[1203] server

[1204] The server is a computing device that processes, analyzes, and optimizes data input from learners. The server has the following functions:

[1205] There is a database function that allows learners to input their learning style, ability, interests, progress rate, and emotional state.

[1206] The server receives the question data and learning content entered by the learner and analyzes them. The analysis results include a means for identifying questions and explanations similar to the learner's answers.

[1207] Based on the learner's learning profile and input question data, the server generates a customized study plan that is optimized based on the learner's learning style, pace, and emotional state.

[1208] Terminal

[1209] The terminal is a device that displays the customized learning plan provided by the server and allows the learner to work on it. This terminal is mainly a smartphone, but other devices can also be considered.

[1210] The device's emotion engine uses the learner's facial expressions, tone of voice, input patterns, etc. to obtain the learner's emotional data.

[1211] Additionally, the device receives feedback from the server and provides it to the learner in real time, including information on whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[1212] Based on the emotion engine data, the device sends information to the server to dynamically adjust the learning plan.

[1213] Emotion Engine

[1214] The emotion engine is software that analyzes data such as the learner's facial expressions, tone of voice, and input patterns to recognize their emotional state. Specifically, the EmotionAnalyzer and LearningPlanGenerator are used.

[1215] The emotion engine analyzes the learner's emotional state in real time during the learning session and sends the data to the server, which uses the data to adjust the learning plan appropriately.

[1216] For example, if a learner is feeling stressed, the server can provide them with questions of lower difficulty, or if they are highly motivated, it can present them with questions of higher difficulty.

[1217] Specific examples

[1218] For example, a learner may enter a learning profile stating that they "prefer visual learning," "are not good at math," and "are interested in programming." The learner then submits a high school calculus problem to the system. The server analyzes this problem and identifies similar problems and their explanations. During the learning session, the device analyzes the learner's facial expressions and tone of voice through an emotion engine. If the learner is struggling, the server can provide a less difficult problem next time. Also, if the learner is feeling stressed, feedback is provided suggesting a short break to relax.

[1219] Example input to a generative AI model

[1220] The next challenge to tackle is the security of electronic payments. The emotion engine analyzes your emotions and provides appropriate motivation. If you feel that "learning how to pay is difficult," please suggest what feedback would be appropriate for the next step.

[1221] Thus, the present invention provides a system that can individually optimize a learner's learning experience and adjust it in real time according to their emotional state.

[1222] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1223] Step 1:

[1224] A user accesses the system and creates an account.

[1225] How it works: The user enters the required information, such as their name, email address, and password, and the device sends it to the server, which then encrypts and stores the information and creates a user account.

[1226] Input: Name, Email Address, Password

[1227] Output: User account created and saved

[1228] Step 2:

[1229] The user accesses the system through the login screen.

[1230] How it works: The user logs in using their registered email address and password, and the device sends this authentication information to the server, which verifies the information entered and returns the authentication result.

[1231] Input: Email address, Password

[1232] Output: Authentication result (success or failure)

[1233] Step 3:

[1234] New users set up their learning profile.

[1235] Specific operation: After logging in, the user inputs their learning style, ability, interests, progress rate, and emotional state, and the device sends this information to the server, which then generates a learning profile and stores it in a database.

[1236] Inputs: learning style, ability, interest, progress rate, emotional state

[1237] Output: Generate and save a learning profile

[1238] Step 4:

[1239] Users can input their own weak questions or learning content.

[1240] Specific operation: The user inputs questions that they were unable to solve or that they found difficult, and the device sends them to the server. The server receives the submitted questions and begins analyzing them.

[1241] Input: Problems you couldn't solve, problems you're not good at

[1242] Output: Receive problem data and start analysis

[1243] Step 5:

[1244] The server analyzes the submitted problem and identifies similar problems and their explanations.

[1245] Specific operation: The server analyzes the received problem data and searches the database for similar problems. The search results include similar problems with explanations.

[1246] Input: Problem data

[1247] Output: Similar problems and their explanations

[1248] Step 6:

[1249] The server generates a customized learning plan based on the learner's learning profile and input question data.

[1250] Specific operation: The server combines the learning profile and analysis results to generate an individually optimized learning plan and send it to the device.

[1251] Input: Learning profile, analysis results of problem data

[1252] Output: Generate a customized learning plan

[1253] Step 7:

[1254] The terminal allows the learner to work through a learning plan provided by the server.

[1255] Specific operations: The device displays the received learning plan and provides an interface for the user to work on it. The user answers the assignments and enters the results.

[1256] Input: Study Plan

[1257] Output: Display of the learning plan and provision of an interface

[1258] Step 8:

[1259] The emotion engine obtains the learner's emotional data using the learner's facial expressions, tone of voice, input patterns, etc.

[1260] How it works: The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data to identify their emotional state, which is then sent to a server.

[1261] Input: facial expression data, tone of voice, input pattern

[1262] Output: Emotion data

[1263] Step 9:

[1264] The server dynamically adjusts the learning plan based on the emotional data.

[1265] Specific operation: The server analyzes the received emotional data and adjusts the difficulty and content of the study plan. For example, if the user is feeling stressed, it will provide questions with lower difficulty.

[1266] Input: Emotion data

[1267] Output: A dynamically adjusted learning plan

[1268] Step 10:

[1269] Evaluate learners' answers or responses and provide feedback in real time.

[1270] How it works: The device sends the user's answers to the server, which evaluates them and provides real-time feedback, such as whether the answer was correct, additional explanations, and the next assignment.

[1271] Input: User's answer or response

[1272] Output: Real-time feedback

[1273] Step 11:

[1274] Stores learner progress and sentiment data and optimizes the next learning plan based on that.

[1275] What it does: After a learning session ends, the server stores progress and emotion data in a database, which is used to optimize the next learning plan.

[1276] Input: Progress data, emotion data

[1277] Output: Save data and optimize the next learning plan

[1278] 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.

[1279] 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.

[1280] 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.

[1281] [Fourth embodiment]

[1282] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1283] 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.

[1284] 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).

[1285] 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.

[1286] 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.

[1287] 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).

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] 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."

[1295] The present invention relates to a system that provides a learner with a personalized, short-term learning experience by generating an individualized learning plan that takes into account the learner's learning style, abilities, interests, and pace of progress.

[1296] The system embodiment operates in the following general manner.

[1297] User Registration and Login

[1298] This is the step where a user first accesses the system and creates an account. The user enters the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[1299] Setting up your learning profile

[1300] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[1301] Providing learning content

[1302] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[1303] Generate a personalized study plan

[1304] The server generates a customized learning plan based on the learner's learning profile and the entered problem data. This plan includes problems that the learner should focus on and explanations for them. The generated learning plan is then sent to the device.

[1305] Start a study session

[1306] The user starts a study session using a terminal. The terminal displays questions and explanations provided by the server to the user. The user progresses at their own pace and enters their answers.

[1307] Real-time feedback

[1308] Each time a user answers a question, the device sends the answer to the server, which evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to try.

[1309] Recording progress

[1310] After a study session is completed, the server records the user's progress, which is used to optimize future study plans. For example, if a user has made progress in a particular area of ​​calculus, the next study plan will offer more advanced problems.

[1311] Specific examples

[1312] For example, consider a user named Tanaka. After registering with the system and logging in, Tanaka enters his learning profile, including such things as "prefers visual learning," "is bad at math," and "interested in programming." Then, Tanaka submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[1313] Tanaka begins a study session based on a personalized study plan provided by the server. After solving each problem, he submits his answer to the server and receives real-time feedback. As he studies, the server records his progress and provides him with the most appropriate problems for his next study session.

[1314] This allows Tanaka to study effectively at his own pace. In this way, the system of the present invention provides the learner with a personalized and efficient learning experience.

[1315] The processing flow will be explained below.

[1316] Step 1:

[1317] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[1318] Step 2:

[1319] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[1320] Step 3:

[1321] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[1322] Step 4:

[1323] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[1324] Step 5:

[1325] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[1326] Step 6:

[1327] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[1328] Step 7:

[1329] The server analyzes the received problem data and searches the database for similar problems and explanations.

[1330] Step 8:

[1331] The server generates a customized learning plan based on the learning profile and analysis results.

[1332] Step 9:

[1333] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[1334] Step 10:

[1335] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[1336] Step 11:

[1337] The server receives the user's answers, evaluates them, generates evaluation results and feedback, and sends them to the device.

[1338] Step 12:

[1339] The device then displays the feedback it receives to the user, including whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[1340] Step 13:

[1341] After the learning session is completed, the server records the user's progress data in a database.

[1342] Step 14:

[1343] The server analyzes the progress data and optimizes the next study plan. A new study plan is generated and used for the next study session.

[1344] Step 15:

[1345] By repeating the above steps, the user can effectively progress through their studies at their own pace.

[1346] Example 1

[1347] 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."

[1348] Conventional learning systems have difficulty providing personalized learning plans that take into account each learner's learning style and progress speed. They also lack the functionality to provide real-time feedback, record learner progress, and reflect it in the next learning plan. This has led to the problem of learners being unable to study effectively at their own pace.

[1349] 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.

[1350] In this invention, the server includes: a means for a learner to input their own learning style, ability, interests, and progress rate; a means for the learner to input questions that they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing feedback in real time; a means for recording the learner's progress data after the study session ends and optimizing the next study plan; a means for the learner to input account information on a login screen to access the system; and a means for inputting learning style and interests after setting up a study profile. This makes it possible to create a personalized study plan according to the individual circumstances of the learner, provide feedback in real time, and optimize learning.

[1351] "Learning style" refers to the method or approach that a learner uses to learn most effectively.

[1352] "Ability" indicates a learner's level of knowledge, skills, and understanding, and refers to the level of academic ability and skill in a particular field.

[1353] "Interests" refers to areas or topics that a learner is particularly interested in.

[1354] "Progression rate" refers to the speed at which a learner comprehends and progresses through the learning content.

[1355] An "input problem" is a problem that a learner inputs to the system that they find difficult to solve.

[1356] "Server" refers to the computer system that handles the main processing and data management of the learning system.

[1357] "Devices" refers to devices such as computers, tablets, and smartphones used by learners.

[1358] "Learning Profile" refers to a collection of data including a learner's learning style, interests, abilities, and rate of progress.

[1359] A "customized learning plan" refers to a learning plan or set of tasks that are specifically created to meet the individual needs and circumstances of a learner.

[1360] "Real-time feedback" refers to providing instant assessment and comments to learners as soon as they submit their answers.

[1361] "Progress data" refers to data that shows how much progress a learner has made in their studies and what results they have achieved.

[1362] "Account information" refers to the authentication information a learner uses to access the system, and typically includes a name, email address, password, etc.

[1363] "Login screen" refers to the screen where a user enters authentication information to access the system.

[1364] A "learning session" refers to a series of learning activities that a learner performs using the system within a set period of time.

[1365] This invention is a system that provides learners with a personalized, short-term learning experience, generating an individual learning plan based on the learner's learning style, ability, interests, and progress rate. The system is operated through a device such as a web browser or mobile app, with the main processing performed by a server.

[1366] Hardware and software used

[1367] The system uses the following hardware and software:

[1368] Terminal: The device operated by the user (PC, tablet, smartphone, etc.)

[1369] Server: A remote computer that processes and stores training data.

[1370] Database: A storage system for storing learning profiles, progress data, and question data.

[1371] Web application / Mobile application: Provides an interface for users to access their learning plans

[1372] System overview and specific operation

[1373] User Registration and Login

[1374] When a user first accesses the system, they create an account. After entering the required information, such as their name, email address, and password, the device sends this information to the server, which encrypts it and stores it in a database. The user then accesses the system using the login screen.

[1375] Setting up your learning profile

[1376] Immediately after logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they enter information such as "I prefer visual learning," "I'm not good at math," or "I'm interested in programming." This information is sent from the device to the server, which then creates a learning profile and stores it in a database.

[1377] Providing learning content

[1378] The user enters a problem they were unable to solve into the system. This problem data is sent from the device to the server, which analyzes it and searches the database for similar problems and their explanations. Links and files for the corresponding learning content are then sent to the user's device.

[1379] Generate a personalized study plan

[1380] The server generates a customized study plan based on the learning profile and the entered problem data. The study plan includes the problems to focus on and their explanations. The study plan is then sent to the device.

[1381] Start a study session

[1382] The user starts a study session using a terminal, which displays questions and explanations provided by the server, and the user progresses at their own pace and enters their answers.

[1383] Real-time feedback

[1384] Each time a user answers a question, the answer is sent from the device to the server. The server evaluates the answer and generates real-time feedback, including whether the answer was correct or incorrect, a detailed explanation, and the next step to try. The feedback is sent to the device and displayed to the user.

[1385] Recording progress

[1386] After a study session is completed, the device sends the session results data to the server, which records the progress data it receives and uses it to optimize future study plans. For example, if a user makes progress in a particular area of ​​calculus, that progress can be reflected in their next study plan.

[1387] Specific examples

[1388] For example, consider a user named A. After registering with the system and logging in, A enters his learning profile, such as "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, A submits a high school-level calculus problem to the system. The server analyzes this calculus problem and identifies similar problems and their explanations.

[1389] Person A begins a study session based on a personalized study plan provided by the server. As he solves each problem, he sends his answers to the server and receives real-time feedback. As he continues to study, the server records Person A's progress and provides him with the most appropriate problems for his next study session.

[1390] Prompt Sentence Examples

[1391] "Generate a personalized learning plan for Person A, who prefers visual learning, struggles with math, and is interested in programming."

[1392] In this way, the system of the present invention provides a personalized learning experience that is tailored to the individual circumstances of each learner.

[1393] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1394] Step 1: User Registration and Login

[1395] 1.1 A user accesses the system and opens the account registration page. They enter personal information such as their name, email address, and password. The device collects the input information.

[1396] 1.2 The device sends the collected personal information to the server, which receives the data and performs encryption processing.

[1397] 1.3 The server stores the encrypted personal information in the database. The output is a notification that the data has been saved to the database.

[1398] 1.4 A user enters an email address and password on a login screen. The input is the user's authentication information.

[1399] 1.5 The terminal sends the authentication information to the server. The server checks the authentication information against the database information, and if authentication is successful, the user is logged in. The output is a notification of successful login.

[1400] Step 2: Set up your learning profile

[1401] 2.1 After logging in, the user opens the learning profile setting page. Inputs include learning style, interests, abilities, learning goals, etc. The device collects the input information.

[1402] 2.2 The device sends the collected learning profile information to the server. The server receives the data and stores it in the profile database. The output is a notification that the profile has been set up.

[1403] 2.3 As a specific action, the user inputs "I prefer visual learning," "I'm not good at math," and "I'm interested in programming."

[1404] Step 3: Provide learning content

[1405] 3.1 The user inputs the problem they were unable to solve into the system as text or images. The input is the problem data. The terminal collects the problem data.

[1406] 3.2 The terminal sends the collected problem data to the server, which receives the data and performs problem analysis processing.

[1407] 3.3 The server searches the database for similar problems and their explanations. The output is data on similar problems and their explanations.

[1408] 3.4 The terminal displays the similar questions and explanations received from the server to the user.

[1409] Step 4: Generate a personalized study plan

[1410] 4.1 The server generates a customized learning plan based on the learning profile and the input question data. The input is the learning profile and the question data.

[1411] 4.2 Include the focus questions and explanations in the server-generated study plan. The output is a customized study plan.

[1412] 4.3 The device displays the study plan received from the server to the user, and the user begins studying according to the plan.

[1413] Step 5: Start your study session

[1414] 5.1 The user starts a learning session using a terminal. The input is the start command.

[1415] 5.2 The terminal displays the questions and explanations provided by the server to the user. The output is the displayed learning content.

[1416] 5.3 The user works on the problem and inputs the answer. The input is the answer data.

[1417] Step 6: Real-time feedback

[1418] 6.1 When the user answers a question, the terminal sends the answer to the server. The input is the answer data.

[1419] 6.2 The server evaluates the answer data and generates feedback in real time. Data processing involves determining whether the answer is correct or incorrect and generating an explanation. The output is the generated feedback data.

[1420] 6.3 The device displays the feedback received from the server to the user. Specifically, the user immediately checks the feedback and proceeds to the next task.

[1421] Step 7: Record your progress

[1422] 7.1 After the learning session is completed, the device sends the session result data to the server. The input is the learning progress data.

[1423] 7.2 The server records the received progress data and stores it in a database. The output is the stored progress data.

[1424] 7.3 Progress data is used to optimize your next study plan, specifically providing more challenging and user-appropriate questions in your next study session.

[1425] (Application example 1)

[1426] 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."

[1427] Conventional learning systems have faced challenges in effectively providing learners with optimal learning experiences tailored to their individual learning styles and interests. In particular, there was a lack of technology to provide short, personalized learning experiences using smartphones and smart devices. Furthermore, there was also a lack of systems that could dynamically update learning plans based on learners' progress and provide appropriate feedback in real time. This resulted in reduced learning efficiency and prevented learners from effectively progressing through their studies at their own pace.

[1428] 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.

[1429] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, and progress rate; a means for the learner to input questions they are weak at; a means for the server to analyze the input questions and identify similar questions and their explanations; a means for the server to generate a customized study plan based on the learner's learning profile and the input questions; a means for a terminal to enable the learner to work on the study plan provided by the server; a means for evaluating the learner's answers and providing real-time feedback; a means installed on a smartphone, smart glasses, head-mounted display, or robot to allow the learner to receive a personalized, short-term study experience; a means for recording the learner's progress data during the study session and reflecting it in the next study plan; a means for generating questions and their explanations optimal for the learner using a generative AI model; and a means for generating a study plan based on the learner's requests using prompt sentences. This allows the learner to receive an optimal learning experience tailored to their learning style and interests. Furthermore, the learner's progress data can be recorded while receiving real-time feedback and reflected in the next study plan, improving learning efficiency.

[1430] "Learning style" refers to the unique learning methods and procedures that each learner has, and refers to different senses and approaches such as visual, auditory, and experiential.

[1431] "Ability" refers to the overall level of knowledge and skills that a learner possesses, including proficiency in a particular field.

[1432] "Interest" refers to the interest a learner has in a particular field or topic, and is an important factor that influences motivation to learn.

[1433] "Progression speed" is an indicator of how quickly a learner can understand and progress through the learning content.

[1434] A "problem" is a task or question that a learner must solve, and is an important element for assessing learning progress.

[1435] "Explanation" refers to additional information or explanation about the question and its answer, provided to help learners deepen their understanding.

[1436] A "learning plan" is a plan of learning content and progression created based on a learner's individual needs and customized for efficient learning.

[1437] A "terminal" is a device or equipment on which a learner works on their learning plan, including a smartphone, smart glasses, a head-mounted display, or a robot.

[1438] "Feedback" refers to evaluations and comments provided by the server in real time in response to answers submitted by learners.

[1439] "Progress Data" means data that records a learner's progress during a learning session and is used to optimize subsequent learning plans.

[1440] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate questions and explanations that are optimal for learners.

[1441] A "prompt" is text data that is input into a generative AI model and is used to generate a learning plan based on the learner's requirements.

[1442] The present invention relates to a system that provides a user with a personalized, short-term learning experience by using an application installed on a device such as a smartphone, smart glasses, a head-mounted display, or a robot.

[1443] First, a user accesses the system and creates an account. The user enters the required information, such as their name, email address, and password, and sends it from their device to the server. The server encrypts and stores this information, and the user then accesses the system through the login screen.

[1444] Next, the user inputs their learning style, abilities, interests, and learning goals. For example, if a user prefers visual learning, is bad at math, and is interested in programming, they enter that information into the device. This information is sent to the server, which then creates and stores a learning profile.

[1445] When a user begins studying, they input questions that they find difficult or have been unable to solve into the system. These question data are sent from the device to a server, which then analyzes the input questions. Natural language processing technology such as SpaCy is used for the analysis. The server then searches a database for similar questions and their explanations, and generates a customized study plan based on that information.

[1446] The generated study plan is sent to the device, and the user begins a study session through the device. The user answers the questions and the answers are sent to the server. The server evaluates the answers and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to be attempted.

[1447] After a study session is completed, the server records the user's progress data, which is used to optimize the next study plan. For example, if a user shows progress on a particular task, the next study plan will offer more advanced problems.

[1448] Furthermore, the system uses a generative AI model to generate questions and explanations that are optimal for each learner, based on the user's learning profile and progress data, enabling more efficient learning.

[1449] Example prompts to input to a generative AI model:

[1450] User name: Tanaka-san

[1451] Uses: Visual learners, poor at math, interested in programming

[1452] Contents: Calculus at the second year of high school level

[1453] Problem: Solution to \(\int x^2 dx\)

[1454] Based on these prompts, the generative AI model provides a personalized learning plan for the user, enabling effective learning in a short amount of time.

[1455] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1456] Step 1:

[1457] A user accesses the system and creates an account. They enter the required information, such as their name, email address, and password, and send it from their terminal to the server. The server encrypts this information (using AES encryption) and stores it in a database. The input is user information (name, email address, and password), and the output is the encrypted user information.

[1458] Step 2:

[1459] A user accesses the system through a login screen. The server authenticates the login information and grants the user access rights. The input is the login information (email address, password) and the output is the authentication result (success or failure). If authentication is successful, the user proceeds to the next step.

[1460] Step 3:

[1461] The user inputs their learning style, ability, interests, and learning goals. This information is sent from the terminal to the server, which then generates a learning profile and stores it in a database. The input is the learning profile information (learning style, ability, interests, learning goals), and the output is the generated learning profile.

[1462] Step 4:

[1463] Users submit problems they have difficulty with or have been unable to solve to the system. The problem data is sent from the device to the server, which then analyzes the problem using natural language processing technology (SpaCy). The server then searches a database for similar problems and their explanations, and generates a customized study plan. The input is the problem data, and the output is a customized study plan.

[1464] Step 5:

[1465] The generated study plan is sent to the device, and the user starts a study session through the device. The user answers questions, and the answers are sent from the device to the server. The input is the user's answer, and the output is the state in which the answer has been sent to the server.

[1466] Step 6:

[1467] The server evaluates the user's answers and provides real-time feedback, including whether the answer is correct or incorrect, an explanation, and what to do next. The input is the user's answer and the output is the feedback. The server evaluates the answers using an evaluation algorithm.

[1468] Step 7:

[1469] After the study session ends, the server records the user's progress data. This data is used to optimize the next study plan. The input is the study session data (progress data) and the output is the recorded progress data. The server saves the progress data in a database and dynamically updates the next study plan.

[1470] Step 8:

[1471] The server uses a generative AI model to generate questions and explanations that are optimal for the learner. The generative AI model inputs learning profile and progress data and outputs optimal questions and explanations. The input is learning profile and progress data, and the output is generated questions and explanations.

[1472] Step 9:

[1473] The server uses the prompt to generate a learning plan based on the learner's requirements. The prompt includes the user's requirements (learning style, interests, learning content, etc.) and is input to the generative AI model. The input is the prompt, and the output is the learning plan.

[1474] Example prompts to input to a generative AI model:

[1475] User name: Yamada-san

[1476] Uses: Visual learners, poor at math, interested in programming

[1477] What you'll learn: University-level linear algebra

[1478] Problem: How to find the inverse of a matrix

[1479] Based on this prompt, the server uses a generative AI model to provide Yamada with an optimal learning plan, allowing the user to study effectively in a short amount of time.

[1480] 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.

[1481] The present invention combines an emotion engine with a system that provides learners with a personalized, short-term learning experience, which generates an individualized learning plan based on the learner's learning style, ability, interests, pace, and emotions.

[1482] An embodiment of the system operates as follows.

[1483] User Registration and Login

[1484] When a user first accesses the system, they create an account. They enter the required information, such as their name, email address, and password. This information is sent from the device to the server, where it is encrypted and stored. The user then accesses the system using the login screen.

[1485] Setting up your learning profile

[1486] After logging in, new users enter their learning style, interests, abilities, and learning goals. For example, they may prefer visual learning, be bad at math, or be interested in programming. The device sends this information to the server, which then creates and stores a learning profile.

[1487] Providing learning content

[1488] Users submit problems they have difficulty with or have been unable to solve to the system. This problem data is sent from the terminal to the server. The server analyzes the submitted problem and searches the database for similar problems and their explanations.

[1489] Generate a personalized study plan

[1490] The server generates a customized study plan based on the study profile and the entered question data, and the study plan is sent to the terminal, which displays the contents to the user.

[1491] Introducing the Emotion Engine

[1492] During a study session, the device uses the user's facial expressions, tone of voice, and input patterns to acquire emotional data from the emotion engine. The server receives this emotional data and reflects it in the study plan. For example, if the user is feeling stressed, it can provide them with questions with a lower level of difficulty.

[1493] Start a study session

[1494] The user starts a learning session using the device, works on the provided questions, and inputs their answers. The device uses an emotion engine to measure the user's emotions in real time and transmits them to the server.

[1495] Real-time feedback

[1496] Each time a user answers a question, the device sends the answer to the server. The server evaluates the answer and provides real-time feedback, including whether the answer was correct or incorrect, an explanation, and the next step to take. Furthermore, the server uses data from the emotion engine to provide appropriate motivation to the user.

[1497] Recording progress

[1498] After the learning session ends, the server records the user's progress and emotional data in a database, which is used to optimize the next learning plan.

[1499] Specific examples

[1500] For example, consider the case of a user named Yamada. After registering with the system and logging in, Yamada enters his learning profile, including the following: "I prefer visual learning," "I'm not good at math," and "I'm interested in programming." After that, Yamada submits a calculus problem at the second-year high school level. The server analyzes this calculus problem and identifies similar problems and their explanations.

[1501] When Yamada starts a session, the device analyzes her facial expressions and tone of voice through an emotion engine. For example, if the emotion engine recognizes that Yamada is having difficulty solving a problem, the server will provide her with a slightly less difficult problem next time. If Yamada is feeling stressed, the server will provide feedback suggesting a short break to relax.

[1502] After the study session, Yamada's progress and emotional data are recorded and used to optimize his next study plan.

[1503] This allows Yamada to study effectively at his own pace and in line with his emotional state. In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[1504] The processing flow will be explained below.

[1505] Step 1:

[1506] To create an account, a user enters their name, email address, and password, and the device sends this information to the server.

[1507] Step 2:

[1508] The server receives the entered information, encrypts it, and stores it in the database. If the storage is successful, the user receives a confirmation email.

[1509] Step 3:

[1510] The user enters their email address and password on the login screen and sends them to the server. The server authenticates the user information and allows them to log in.

[1511] Step 4:

[1512] After logging in, the user inputs their learning style, interests, abilities, and learning goals, which are then sent from the terminal to the server.

[1513] Step 5:

[1514] The server records a learning profile, such as learning style, interests, abilities, and learning goals, in a database.

[1515] Step 6:

[1516] The user inputs questions that they are difficult to solve or that they were unable to solve, and sends the questions to the server via the terminal.

[1517] Step 7:

[1518] The server analyzes the received problem data and searches the database for similar problems and explanations.

[1519] Step 8:

[1520] The server generates a customized learning plan based on the learning profile and analysis results.

[1521] Step 9:

[1522] The server sends the generated learning plan to the terminal, which then displays the received learning plan to the user.

[1523] Step 10:

[1524] The device analyzes the user's facial expressions and tone of voice using an emotion engine and sends the emotion data to the server.

[1525] Step 11:

[1526] A user initiates a learning session using a device and works through the provided questions. The user enters the answers, which the device sends to the server.

[1527] Step 12:

[1528] The server receives the user's answer, evaluates it, generates evaluation results and feedback, and sends them to the device, taking into account the user's emotional data.

[1529] Step 13:

[1530] The device then displays the received feedback to the user, including whether the answer was correct or incorrect, an explanation, the next step to be taken, and motivation based on emotional data.

[1531] Step 14:

[1532] After the learning session is completed, the server records the user's progress and emotional data in a database.

[1533] Step 15:

[1534] The server analyzes the progress and emotion data to optimize the next learning plan, and a new learning plan is generated and used for the next learning session.

[1535] Step 16:

[1536] By repeating the above steps, users can study effectively at their own pace and in line with their emotional state.

[1537] Example 2

[1538] 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."

[1539] Conventional learning support systems have the problem of being unable to adequately provide individual learning plans that are tailored to each learner's learning style, ability, interests, and progress speed. Furthermore, they are unable to take into account the learner's emotions during learning, which can lead to a decrease in learning effectiveness if the learner continues to study while feeling stressed or anxious. Another problem is that the lack of real-time emotional feedback makes it difficult to respond immediately.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1541] In this invention, the server includes means for the learner to input their own learning style, ability, interests, and progress speed, means for the learner to input questions that they are weak at, means for the server to analyze the input questions and identify similar questions and their explanations, means for the server to generate a customized study plan based on the learner's learning profile and the input questions, means for the terminal to acquire emotional data of the learner while studying, and means for the server to dynamically adjust the study plan based on the emotional data. This makes it possible to provide an individually optimized study plan that incorporates the learner's emotions throughout the entire learning process, and realizes emotional feedback and appropriate responses in real time.

[1542] "Learning style" refers to the method or technique in which a learner learns most effectively.

[1543] "Ability" refers to the knowledge and skills that a learner has to solve a particular task or problem.

[1544] "Interest" refers to the degree of interest or concern a learner has in a particular field or topic.

[1545] "Progression rate" refers to the pace or speed at which a learner progresses through their studies.

[1546] "Learning Profile" refers to a collection of information including a learner's learning style, abilities, interests, progress rate, and other relevant data.

[1547] A "learning plan" refers to a learning guideline or plan created based on the learner's individual needs and circumstances.

[1548] "Emotional data" refers to data about emotions obtained by analyzing a learner's facial expressions, voice, input patterns, etc.

[1549] "Devices" refer to electronic devices such as computers, smartphones, and tablets used by learners.

[1550] A "server" is a computer that is the core of a learning system and is a device that processes and stores data and provides services.

[1551] "Feedback" refers to the immediate evaluation and advice given to a learner in response to their actions or answers.

[1552] The present invention combines an emotion engine with a system that provides a personalized, short-term learning experience for learners. The system generates an individualized learning plan taking into account the learner's learning style, ability, interests, progress rate, and emotions. An embodiment of the system is configured as follows:

[1553] User Registration and Login

[1554] The user accesses the system and creates an account. The user enters the required information such as name, email address, and password, and presses the registration button. The terminal sends the entered information to the server, which encrypts and stores the data. After completing registration, the user enters their email address and password on the login screen to log in to the system.

[1555] Setting up your learning profile

[1556] After logging in, the user enters their learning style, interests, abilities, learning goals, etc. on the "Profile Settings" page. The device sends this information to the server, which then creates a learning profile and stores it in a database.

[1557] Providing learning content

[1558] Users enter questions they have difficulty with or have been unable to solve into the system on the "Learning Contents" page. The device sends the question data to the server, which analyzes the question data and searches the database for similar questions and their explanations.

[1559] Generate a personalized study plan

[1560] The server generates a customized study plan based on the study profile and the entered question data, and the generated study plan is sent to the terminal and displayed to the user.

[1561] Introducing the Emotion Engine

[1562] During a learning session, the device uses a camera and microphone to analyze the user's facial expressions and tone of voice, and passes the data to an emotion engine. The server then dynamically adjusts the learning plan based on the emotional data, such as lowering the difficulty level if the user is feeling stressed.

[1563] Start a study session

[1564] The user begins a learning session and works through the presented problems. The device measures emotional data in real time and transmits it to the server, which then provides appropriate feedback.

[1565] Real-time feedback

[1566] Each time a user answers a question, the device sends the answer data to the server, which then evaluates the answer and immediately provides a correct answer, explanation, and the next task to tackle. It also provides advice based on the user's emotional data to boost motivation.

[1567] Recording progress

[1568] After the learning session ends, the server records the user's progress and emotional data and uses it to optimize the next learning plan.

[1569] Hardware and software used

[1570] Server: We use common web servers and database servers (e.g., Apache, MySQL) for data processing and storage.

[1571] Device: The computer, smartphone, tablet, etc. that the user uses.

[1572] Software: Machine learning libraries such as TensorFlow are used for sentiment analysis, and HTML and JavaScript are used for the front end.

[1573] Specific examples

[1574] For example, consider a case where a learner uses the system. The learner registers with the system and enters a learning profile that includes "prefers visual learning," "is not good at math," and "is interested in programming." The learner then submits a calculus problem at the second-year high school level. The server analyzes this problem and identifies similar problems and their explanations.

[1575] When a learner starts a study session, the device uses an emotion engine to analyze the learner's facial expressions and tone of voice, and if the learner is feeling stressed, it will provide them with questions of lower difficulty and suggest appropriate breaks to relax.

[1576] Prompt Sentence Examples

[1577] 1. "Prefer visual learning," "are bad at math," and "are interested in programming" - Give us some calculus problems at the second-year high school level.

[1578] 2. Analyze the user's facial expressions and tone of voice and suggest a short break to relax if they are feeling stressed.

[1579] In this way, the system of the present invention provides learners with a personalized and efficient learning experience using an emotional engine.

[1580] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1581] Step 1: User Registration

[1582] The user clicks the "New Registration" button on the system's access page and enters the necessary information into the input form, such as name, email address, and password. The device sends this information to the server. The server encrypts the entered data and stores it in a database. The user's personal information is stored as input, and encrypted data is stored as output.

[1583] Step 2: Log in

[1584] After completing registration, the user enters their email address and password on the login screen and presses the "Login" button. The device sends this to the server, which then verifies it against a database. The input is the user's login information, and the output is the authentication result (success or failure). If authentication is successful, the user is redirected to the home screen.

[1585] Step 3: Set up your learning profile

[1586] Users access the "Profile Settings" page from the home screen. They input their learning style, interests, abilities, and learning goals. The device sends this information to the server, which then generates a learning profile based on the input data and stores it in a database. Learning style and interests are stored as input, and the generated learning profile is stored as output.

[1587] Step 4: Provide learning content

[1588] On the "Learning Content" page, users enter questions they have difficulty with or have been unable to solve into the system. The device then sends the question data to the server. The server then uses natural language processing technology to analyze the question data and searches the database for similar questions and their explanations. The learner's question data is the input, and similar questions and their explanations are identified as the output.

[1589] Step 5: Generate a personalized study plan

[1590] The server uses an algorithm to generate a customized study plan based on the learning profile and input question data. The generated study plan is sent to the terminal and displayed to the user. The input is the learning profile and question data, and the output is the customized study plan.

[1591] Step 6: Implementing the Emotion Engine

[1592] During a study session, the device uses a camera and microphone to capture the user's facial expressions and tone of voice in real time. The device passes this data to an emotion engine to infer the user's emotions. The inference results are sent to a server, which dynamically adjusts the difficulty of the study plan and break suggestions based on the emotion data. The input is real-time emotion data, and the output is an adjusted study plan.

[1593] Step 7: Start your study session

[1594] The user clicks the "Start Learning Session" button to start a learning session. The user works through the presented questions and enters their answers. The device sends the answer data to the server, which evaluates the answers and generates feedback. The input is the user's answer data, and the output is feedback.

[1595] Step 8: Real-time feedback

[1596] Each time a user answers a question, the device sends the answer data to the server. The server evaluates the answer and provides real-time feedback on whether the answer was correct, an explanation, and the next step to tackle. It also provides motivational advice based on emotional data. The inputs are answer data and emotional data, and the output is immediate feedback.

[1597] Step 9: Record your progress

[1598] After the study session ends, the server records the user's progress and emotion data, which are used to optimize the next study plan. The inputs are the progress and emotion data from the session, and the output is the optimized next study plan.

[1599] (Application example 2)

[1600] 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."

[1601] In modern society, optimizing individual learning experiences is an important challenge, but existing systems lack personalized feedback that takes into account the learner's emotional state. Furthermore, in areas such as electronic payment, dynamic adjustment functions are needed to help users learn effectively and master operations. Systems that address these challenges and allow learners to progress at their own pace without stress or anxiety are needed.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1603] In this invention, the server includes: a means for a learner to input their learning style, ability, interests, progress rate, and emotional state; a means for the learner to input questions or learning content that they are weak at; a means for the server to analyze the input questions or learning content and identify similar questions or learning content and their explanations; a means for the server to generate a customized learning plan based on the learner's learning profile and the input questions or learning content; a means for a terminal to enable the learner to work on the learning plan provided by the server; a means for an emotion engine to acquire emotional data of the learner using the learner's facial expressions, tone of voice, input patterns, etc.; a means for the server to dynamically adjust the learning plan based on the emotional data; and a means for evaluating the learner's answers or responses and providing feedback in real time, thereby making it possible to individually optimize the learner's learning experience and adjust it in real time according to the learner's emotional state.

[1604] "Learning style" refers to the way a learner finds it easy to understand or learn information.

[1605] "Ability" refers to the degree of skill or knowledge a learner has in a particular field or subject.

[1606] "Interest" refers to the degree of interest or involvement a learner has in a particular field or topic.

[1607] "Progression rate" refers to how quickly a learner masters the material.

[1608] "Emotional state" refers to the psychological state such as stress, excitement, and impatience that learners feel while studying.

[1609] "Problems or learning content" refers to tasks or educational materials that a learner must complete.

[1610] "Server" refers to a computing device for processing, analyzing, and optimizing data input from learners.

[1611] A "learning profile" refers to information entered by a learner summarizing their learning style, abilities, interests, pace of progress, and emotional state.

[1612] "Customized learning plan" refers to an individual learning schedule and content that is optimized based on the learner's learning profile.

[1613] "Terminal" refers to the device through which a learner accesses and works on the learning plan material provided by the server.

[1614] An "emotion engine" refers to software or hardware that analyzes a learner's facial expressions, tone of voice, input patterns, etc. to recognize their emotional state.

[1615] "Dynamic adjustment" refers to changing the learning plan in real time in response to the learner's ongoing performance and emotional state.

[1616] "Feedback" refers to the process of providing evaluation and instructional comments on answers or responses submitted by learners.

[1617] The present invention provides a system for providing a personalized learning experience to a learner. The system incorporates an emotion engine and includes a function for dynamically adjusting a learning plan based on the learner's emotional state. Specific embodiments of the system are described below.

[1618] System Configuration

[1619] The system consists of the following major components:

[1620] 1. Server

[1621] 2. Terminal

[1622] 3. Emotion Engine

[1623] server

[1624] The server is a computing device that processes, analyzes, and optimizes data input from learners. The server has the following functions:

[1625] There is a database function that allows learners to input their learning style, ability, interests, progress rate, and emotional state.

[1626] The server receives the question data and learning content entered by the learner and analyzes them. The analysis results include a means for identifying questions and explanations similar to the learner's answers.

[1627] Based on the learner's learning profile and input question data, the server generates a customized study plan that is optimized based on the learner's learning style, pace, and emotional state.

[1628] Terminal

[1629] The terminal is a device that displays the customized learning plan provided by the server and allows the learner to work on it. This terminal is mainly a smartphone, but other devices can also be considered.

[1630] The device's emotion engine uses the learner's facial expressions, tone of voice, input patterns, etc. to obtain the learner's emotional data.

[1631] Additionally, the device receives feedback from the server and provides it to the learner in real time, including information on whether the answer was correct or incorrect, an explanation, and the next step to be taken.

[1632] Based on the emotion engine data, the device sends information to the server to dynamically adjust the learning plan.

[1633] Emotion Engine

[1634] The emotion engine is software that analyzes data such as the learner's facial expressions, tone of voice, and input patterns to recognize their emotional state. Specifically, the EmotionAnalyzer and LearningPlanGenerator are used.

[1635] The emotion engine analyzes the learner's emotional state in real time during the learning session and sends the data to the server, which uses the data to adjust the learning plan appropriately.

[1636] For example, if a learner is feeling stressed, the server can provide them with questions of lower difficulty, or if they are highly motivated, it can present them with questions of higher difficulty.

[1637] Specific examples

[1638] For example, a learner may enter a learning profile stating that they "prefer visual learning," "are not good at math," and "are interested in programming." The learner then submits a high school calculus problem to the system. The server analyzes this problem and identifies similar problems and their explanations. During the learning session, the device analyzes the learner's facial expressions and tone of voice through an emotion engine. If the learner is struggling, the server can provide a less difficult problem next time. Also, if the learner is feeling stressed, feedback is provided suggesting a short break to relax.

[1639] Example input to a generative AI model

[1640] The next challenge to tackle is the security of electronic payments. The emotion engine analyzes your emotions and provides appropriate motivation. If you feel that "learning how to pay is difficult," please suggest what feedback would be appropriate for the next step.

[1641] Thus, the present invention provides a system that can individually optimize a learner's learning experience and adjust it in real time according to their emotional state.

[1642] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1643] Step 1:

[1644] A user accesses the system and creates an account.

[1645] How it works: The user enters the required information, such as their name, email address, and password, and the device sends it to the server, which then encrypts and stores the information and creates a user account.

[1646] Input: Name, Email Address, Password

[1647] Output: User account created and saved

[1648] Step 2:

[1649] The user accesses the system through the login screen.

[1650] How it works: The user logs in using their registered email address and password, and the device sends this authentication information to the server, which verifies the information entered and returns the authentication result.

[1651] Input: Email address, Password

[1652] Output: Authentication result (success or failure)

[1653] Step 3:

[1654] New users set up their learning profile.

[1655] Specific operation: After logging in, the user inputs their learning style, ability, interests, progress rate, and emotional state, and the device sends this information to the server, which then generates a learning profile and stores it in a database.

[1656] Inputs: learning style, ability, interest, progress rate, emotional state

[1657] Output: Generate and save a learning profile

[1658] Step 4:

[1659] Users can input their own weak questions or learning content.

[1660] Specific operation: The user inputs questions that they were unable to solve or that they found difficult, and the device sends them to the server. The server receives the submitted questions and begins analyzing them.

[1661] Input: Problems you couldn't solve, problems you're not good at

[1662] Output: Receive problem data and start analysis

[1663] Step 5:

[1664] The server analyzes the submitted problem and identifies similar problems and their explanations.

[1665] Specific operation: The server analyzes the received problem data and searches the database for similar problems. The search results include similar problems with explanations.

[1666] Input: Problem data

[1667] Output: Similar problems and their explanations

[1668] Step 6:

[1669] The server generates a customized learning plan based on the learner's learning profile and input question data.

[1670] Specific operation: The server combines the learning profile and analysis results to generate an individually optimized learning plan and send it to the device.

[1671] Input: Learning profile, analysis results of problem data

[1672] Output: Generate a customized learning plan

[1673] Step 7:

[1674] The terminal allows the learner to work through a learning plan provided by the server.

[1675] Specific operations: The device displays the received learning plan and provides an interface for the user to work on it. The user answers the assignments and enters the results.

[1676] Input: Study Plan

[1677] Output: Display of the learning plan and provision of an interface

[1678] Step 8:

[1679] The emotion engine obtains the learner's emotional data using the learner's facial expressions, tone of voice, input patterns, etc.

[1680] How it works: The device uses a camera and microphone to monitor the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data to identify their emotional state, which is then sent to a server.

[1681] Input: facial expression data, tone of voice, input pattern

[1682] Output: Emotion data

[1683] Step 9:

[1684] The server dynamically adjusts the learning plan based on the emotional data.

[1685] Specific operation: The server analyzes the received emotional data and adjusts the difficulty and content of the study plan. For example, if the user is feeling stressed, it will provide questions with lower difficulty.

[1686] Input: Emotion data

[1687] Output: A dynamically adjusted learning plan

[1688] Step 10:

[1689] Evaluate learners' answers or responses and provide feedback in real time.

[1690] How it works: The device sends the user's answers to the server, which evaluates them and provides real-time feedback, such as whether the answer was correct, additional explanations, and the next assignment.

[1691] Input: User's answer or response

[1692] Output: Real-time feedback

[1693] Step 11:

[1694] Stores learner progress and sentiment data and optimizes the next learning plan based on that.

[1695] What it does: After a learning session ends, the server stores progress and emotion data in a database, which is used to optimize the next learning plan.

[1696] Input: Progress data, emotion data

[1697] Output: Save data and optimize the next learning plan

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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).

[1705] 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.

[1706] 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."

[1707] 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.

[1708] 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).

[1709] 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.

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] 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.

[1717] 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.

[1718] 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.

[1719] The following is further disclosed regarding the above embodiment.

[1720] (Claim 1)

[1721] A means for learners to input their learning style, abilities, interests, and rate of progress;

[1722] A means for learners to independently add problems they are weak at,

[1723] A means for analyzing submitted problems to the server and identifying similar problems and their explanations;

[1724] means for the server to generate a customized learning plan based on the learner's learning profile and the questions entered;

[1725] means for enabling the terminal to enable the learner to work on the learning plan provided by the server;

[1726] A means to assess learners' answers and provide real-time feedback

[1727] Including system.

[1728] (Claim 2)

[1729] 10. The system of claim 1, wherein the customized learning plan is dynamically updated based on the learner's progress data.

[1730] (Claim 3)

[1731] A means to store learner progress and learning data and optimize the next learning plan based on that data.

[1732] The system of claim 1 further comprising:

[1733] "Example 1"

[1734] (Claim 1)

[1735] A means for learners to input their learning style, abilities, interests, and rate of progress;

[1736] A means for learners to independently add problems they are weak at,

[1737] A means for analyzing submitted problems to the server and identifying similar problems and their explanations;

[1738] means for the server to generate a customized learning plan based on the learner's learning profile and the questions entered;

[1739] means for enabling the terminal to enable the learner to work on the learning plan provided by the server;

[1740] A means of assessing learners' answers and providing real-time feedback;

[1741] A means to record learner progress data after a learning session and optimize the next learning plan;

[1742] A means for learners to enter their account information on the login screen and access the system;

[1743] After setting up your learning profile, you can enter your learning style and interests.

[1744] Including system.

[1745] (Claim 2)

[1746] 10. The system of claim 1, wherein the customized learning plan is dynamically updated based on the learner's progress data.

[1747] (Claim 3)

[1748] A means to store learner progress and learning data and optimize the next learning plan based on that data.

[1749] The system of claim 1 further comprising:

[1750] "Application Example 1"

[1751] (Claim 1)

[1752] A means for learners to input their learning style, abilities, interests, and rate of progress;

[1753] A means for learners to independently add problems they are weak at,

[1754] A means for analyzing submitted problems to the server and identifying similar problems and their explanations;

[1755] means for the server to generate a customized learning plan based on the learner's learning profile and the questions entered;

[1756] means for enabling the terminal to enable the learner to work on the learning plan provided by the server;

[1757] A means of assessing learners' answers and providing real-time feedback;

[1758] A device that can be installed on a smartphone, smart glasses, head-mounted display, or robot and that allows learners to receive a personalized, short-term learning experience;

[1759] A means to record learner progress data during a learning session and use it to plan future learning.

[1760] A means to generate optimal questions and explanations for learners using a generative AI model;

[1761] A method for generating a learning plan based on the learner's request using prompt sentences

[1762] Including system.

[1763] (Claim 2)

[1764] 10. The system of claim 1, wherein the customized learning plan is dynamically updated based on the learner's progress data.

[1765] (Claim 3)

[1766] A means to store learner progress and learning data and optimize the next learning plan based on that data.

[1767] The system of claim 1 further comprising:

[1768] "Example 2: Combining Emotion Engines"

[1769] (Claim 1)

[1770] A means for learners to input their learning style, abilities, interests, and rate of progress;

[1771] A means for learners to independently add problems they are weak at,

[1772] A means for analyzing submitted problems to the server and identifying similar problems and their explanations;

[1773] means for the server to generate a customized learning plan based on the learner's learning profile and the questions entered;

[1774] means for enabling the terminal to enable the learner to work on the learning plan provided by the server;

[1775] A means of assessing learners' answers and providing real-time feedback;

[1776] A means for the device to acquire emotional data of the learner while the learner is learning;

[1777] The server needs a way to dynamically adjust the learning plan based on emotional data.

[1778] Including system.

[1779] (Claim 2)

[1780] 10. The system of claim 1, wherein the customized learning plan is dynamically updated based on the learner's progress data.

[1781] (Claim 3)

[1782] A means to store learner progress and learning data and optimize the next learning plan based on that data.

[1783] The system of claim 1 further comprising:

[1784] (Claim 4)

[1785] Develop a method to assess learners' stress levels and suggest appropriate breaks based on real-time emotional data.

[1786] The system of claim 1 further comprising:

[1787] "Application example 2 when combining emotion engines"

[1788] (Claim 1)

[1789] a means for learners to input their learning style, abilities, interests, pace of progress, and emotional state;

[1790] A means for learners to input problems or learning content that they are weak at themselves;

[1791] A means for analyzing the questions or learning content entered into the server and identifying similar questions or learning content and their explanations;

[1792] means for the server to generate a customized learning plan based on the learner's learning profile and input questions or learning content;

[1793] means for enabling the terminal to enable the learner to work on the learning plan provided by the server;

[1794] A means for the emotion engine to acquire learner's emotion data using the learner's facial expressions, tone of voice, input patterns, etc.;

[1795] means for the server to dynamically adjust the learning plan based on the emotion data;

[1796] A means to evaluate learners' answers or responses and provide feedback in real time

[1797] Including system.

[1798] (Claim 2)

[1799] 10. The system of claim 1, wherein the customized learning plan is dynamically updated based on the learner's progress and sentiment data.

[1800] (Claim 3)

[1801] A means to store learner progress and sentiment data and optimize the next learning plan based on that data.

[1802] The system of claim 1 further comprising: [Explanation of symbols]

[1803] 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 learners to input their learning style, abilities, interests, and rate of progress; A means for learners to independently add problems they are weak at, A means for analyzing submitted problems to the server and identifying similar problems and their explanations; means for the server to generate a customized learning plan based on the learner's learning profile and the questions entered; means for enabling the terminal to enable the learner to work on the learning plan provided by the server; A means to assess learners' answers and provide real-time feedback Including system.

2. 10. The system of claim 1, wherein the customized learning plan is dynamically updated based on the learner's progress data.

3. A means to store learner progress and learning data and optimize the next learning plan based on that data. The system of claim 1 further comprising:

Images