system

Abstract

We provide the system. [Solution] A receiving means for receiving input data from learners, A plan generation means that analyzes received input data and generates a learning plan optimized for the learner, An automated note generation method that automatically generates important learning points based on a learning plan, A trend analysis method that analyzes past exam data of the target school and models the trends in the types of questions asked, A question generation method that generates predictive questions based on modeled question trends, A progress feedback system that analyzes learners' progress and provides feedback, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional learning method, it is difficult to provide learning support optimized for individual learners, and there is a problem that the learning effect is limited. In addition, due to geographical conditions and insufficient educational resources, there is also a problem that learners living in rural areas cannot enjoy the same learning environment as those in the city center. Furthermore, since it is difficult to conduct learning in accordance with the question tendencies of the target schools, there is a possibility that the countermeasures for entrance examinations may be insufficient.

Means for Solving the Problems

[0005] This invention provides personalized learning support by receiving data input from learners, analyzing the received data, and generating a learning plan optimized for each learner. It also supports efficient review by providing notes that automatically generate important learning points based on the learning plan. Furthermore, it strengthens entrance exam preparation by analyzing past exam data from target schools, modeling question trends, and generating predictive questions. This makes it possible to provide a learning environment equivalent to that in urban areas, regardless of geographical location.

[0006] "Receiving means" refers to a device or process that has the function of acquiring input data provided by learners, converting it into a format that can be processed within the system, and storing it.

[0007] A "plan generation means" is a device or process that analyzes received input data and has the function of designing and creating a learning plan tailored to each individual learner.

[0008] An "automatic note generation means" is a device or process that has the function of selecting important learning points and content to be memorized according to a generated learning plan and automatically providing them as notes.

[0009] A "trend analysis method" is a device or process that analyzes past exam data from a target school, identifies the trends in the questions asked, and models that data.

[0010] A "problem generation means" is a device or process that has the function of creating predictive problems based on trend analysis and providing them to learners.

[0011] A "progress feedback means" is a device or process that has the function of analyzing a learner's learning progress, generating feedback based on the results, and providing it to the learner. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system for offering individually optimized learning support to learners, primarily through interactions between a server, a terminal, and the user. This document describes the main elements of the system and their functions, and details its capabilities with specific examples.

[0034] Data entry and reception from learners

[0035] User: Learners use a dedicated application to input data such as study time, subjects studied, and test results into their devices. This information serves as the basis for tracking learners' progress in real time and generating individualized learning plans.

[0036] Terminal: Stores the entered data and sends it to the server using a secure communication method.

[0037] Creating study plans and notes

[0038] Server: The server processes the received data using multiple analysis algorithms to generate a learning plan optimized for the learner. This plan includes the allocation of study time for each subject and the content to be reviewed. Furthermore, based on the learning plan, it automatically generates notes summarizing important learning points and provides them to the learner. These notes function as a tool to effectively support the user's learning.

[0039] Analysis of past exam questions and provision of predicted questions

[0040] Server: Analyzes past exam data from the student's target school and models the question trends. For example, if a history exam shows a tendency for many questions to be about names and events, the server generates new predictive questions based on that trend. This allows students to prepare for entrance exams in a more practical way.

[0041] Providing progress feedback

[0042] Server: Analyzes learners' cumulative data to analyze their progress and generates feedback to adjust their next learning plan. This feedback includes identifying subjects to prioritize next and highlighting areas of weakness.

[0043] Enhancing the user experience

[0044] Terminal: Provides learners with an interactive learning experience by instantly displaying learning plans, predicted questions, and feedback provided by the server. This allows learners to independently and effectively manage their daily learning progress.

[0045] For example, if a junior high school student wants to improve their English vocabulary, they can input their learning history into the system. The server will then analyze this data and prioritize incorporating a new vocabulary list and review questions into their plan for the following day. In this way, the present invention provides a learning environment tailored to the individual needs of each learner.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] User: Users input daily learning data, such as study time, subject information, and test results, into a dedicated app. This data serves as foundational information for evaluating learning progress and effectiveness.

[0049] Step 2:

[0050] Terminal: Temporarily stores the entered data. Once saving is complete, it encrypts the data and prepares it for secure transmission to the server.

[0051] Step 3:

[0052] Terminal: Sends data to the server via the internet. During transmission, the data is sent in an encrypted format to ensure data security.

[0053] Step 4:

[0054] Server: Stores received training data in a database and prepares it for analysis. Based on the received data, it evaluates the learner's current learning progress.

[0055] Step 5:

[0056] Server: Applies multiple analysis algorithms to generate a learning plan optimized for each individual learner. The analysis takes into account past learning history, test results, and the learner's goals.

[0057] Step 6:

[0058] Server: Based on the learning plan, it automatically extracts key learning points and provides them to the user in note format. These notes are used by learners to improve the efficiency of review and confirmation.

[0059] Step 7:

[0060] Server: Analyzes past exam data from the target school and models the question trends. Based on the analysis results, it generates predictive questions for use in the next step.

[0061] Step 8:

[0062] Server: Incorporates prediction problems generated based on the model into the user's learning plan and prepares to send them from the server to the terminal.

[0063] Step 9:

[0064] Terminal: Receives data sent from the server and presents the user with new learning plans, notes, and predicted questions. The user then uses this information to proceed with their learning.

[0065] Step 10:

[0066] Server: Analyzes the user's cumulative learning data and evaluates learning progress. Based on this evaluation, it generates feedback to help improve the next learning plan.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] In the field of education, there is a demand for providing individually optimized learning support tailored to each learner. Traditional educational support systems can only provide uniform learning plans and problems, and have the problem of not being able to adequately address the individual needs of learners. In addition, the monitoring of learning progress and the provision of feedback are insufficient, making it difficult to achieve efficient learning.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes receiving means for receiving input information from learners, planning means for analyzing the received information and generating a learning plan optimized for the learner, and generating means for using generation AI technology to generate predictive problems. This enables efficient and effective learning tailored to the individual progress and needs of each learner.

[0072] A "receiving means" is a component that has the function of taking in input information from learners into the system.

[0073] A "plan generation means" is a component that has the function of creating a learning plan optimized for the learner based on the received information.

[0074] The "automatic record generation means" is a component that has the function of automatically constructing important learning items based on the learning plan.

[0075] A "trend analysis tool" is a component that has the function of analyzing past exam question information and modeling the trends in the questions asked.

[0076] A "problem generation means" is a component that has the function of creating predicted problems based on modeled question trends.

[0077] A "progress feedback mechanism" is a component that has the function of analyzing the learner's progress and providing feedback based on the results.

[0078] A "generation means" is a component that has the function of creating predictive problems and learning plans using generative AI technology.

[0079] "Display means" refers to a component that has the function of visualizing feedback information or learning plans.

[0080] The educational support system of this invention is designed to provide learners with individually optimized learning plans and to support their progress. This system primarily functions through three elements: a server, a terminal, and a user.

[0081] The server receives input information sent by learners and stores it in a database. This information includes study time, subjects studied, and test results. Based on this information, the server uses analytical algorithms and generative AI models to generate a learning plan optimized for the learner. Programming languages ​​such as Python and R are used for data analysis during this process.

[0082] The server further analyzes past question data and models question trends to generate predicted questions. This utilizes a generative AI model incorporating natural language processing (NLP) technology. This allows learners to engage in practical problem-solving exercises.

[0083] The terminal is responsible for receiving learning plans and practice questions provided by the server and displaying them to the learner. The terminal uses encryption technologies such as SSL / TLS to securely store received data. Furthermore, it provides feedback information and progress status visually through a graphical user interface, presenting it in a way that is easy for learners to understand.

[0084] Users input their learning information using a dedicated app on their device. These input operations are performed via a touchscreen or keyboard, allowing for intuitive operation. For example, if a middle school student wants to improve their English vocabulary, they input their learning history into the system, and the server analyzes that data to generate a learning plan that prioritizes new vocabulary lists and review questions.

[0085] An example of a prompt message would be sending a request to the AI ​​model to "analyze past history exam data and generate predictive questions that will be useful for the next exam." In this way, the system functions as a tool to support learners in efficient and effective learning.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] User: Learners use a dedicated application on their device to input information such as study time, subjects studied, and test results. Input is done using the touchscreen or keyboard. The entered data is temporarily stored in a database on the device.

[0089] Step 2:

[0090] Terminal: The entered data is securely transmitted to the server using the SSL / TLS protocol. At this time, the data is converted to JSON format and configured as a packet. An acknowledgment is used to confirm that the converted data has been transmitted correctly.

[0091] Step 3:

[0092] Server: Receives data and stores it in a database system. The database is structured using SQL. Next, data analysis algorithms are used to extract the basic data needed to generate a learning plan. For example, the frequency of study and performance trends for each subject are analyzed.

[0093] Step 4:

[0094] Server: Based on the extracted data, it utilizes a generative AI model to generate individually optimized learning plans. The generated learning plans include the allocation of study time and review content for each subject. The generative AI model is often implemented using Python libraries.

[0095] Step 5:

[0096] Server: Automatically generates study notes based on the study plan. These notes summarize important learning points. It also analyzes past exam data, models the question trends, and uses a generation AI model to generate predictive questions.

[0097] Step 6:

[0098] Server: Analyzes learners' past progress data and generates feedback to adjust their next learning plan. This feedback includes subjects and assignments that should be prioritized.

[0099] Step 7:

[0100] Terminal: Receives learning plans, notes, practice questions, and feedback sent from the server and displays them to the user. A GUI is used for display, providing a visual and intuitive interface. Learners use this information to plan and execute their next learning activities.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] Conventional technologies for providing personalized learning support have struggled to reflect learners' needs in real time, particularly in analyzing past exam questions and automatically generating learning plans. Furthermore, effectively providing feedback based on learners' progress remains a challenge. Additionally, there is a lack of technologies that provide learning plans and feedback visually and interactively.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes means for receiving input information from learners, means for generating a learning plan optimized for the learner by analyzing the received input information, means for automatically generating important learning points based on the learning plan, and means for analyzing past exam data from the desired educational institution and modeling the question trends. This enables interactive and effective learning support that reflects the different needs of each learner in real time.

[0106] A "learner" is an individual who aims to acquire specific knowledge or skills.

[0107] "Input information" refers to data that learners provide to the system, including information about learning progress, learning content, and outcomes.

[0108] An "optimized learning plan" is a plan for guiding efficient learning activities that are tailored to the individual circumstances and goals of each learner.

[0109] A "plan generation means" is a component that has the function of analyzing the learner's input information and automatically creating an optimized learning plan.

[0110] An "automatic note generation method" is a component for organizing important learning points based on a learning plan and providing them in note format.

[0111] A "trend analysis tool" is a component that has the function of analyzing past question data and modeling the patterns and trends of those questions.

[0112] An "educational machine" is a machine with interactive features designed to support educational activities.

[0113] The system for realizing this invention has advanced data analysis and interactive functions for educational support. It primarily consists of a server, terminals, and educational equipment working together, with each element playing its individual role while forming the overall system.

[0114] The server functions as a centralized data receiving and processing unit. It receives input information sent from learners and performs analysis. The received information is evaluated using information analysis algorithms to generate an optimized learning plan for each learner. This plan includes the allocation of learning time and review points for specific concepts and areas. Subsequently, a generative AI model analyzes past exam data from the target educational institution to model future exam trends. This process analyzes past exam patterns and generates new predictive questions.

[0115] The terminal is a device operated by the learner, providing data input and communication with the server. The application on the terminal has an interface that allows the learner to input learning time and learning content. Furthermore, the input data is encrypted before being sent to the server. The terminal has a means of displaying learning plans and feedback provided by the server, providing learners with immediate feedback and information.

[0116] Educational machines are devices designed to facilitate learning support and provide physical interaction capabilities. These machines utilize speech recognition and natural language processing technologies to deliver an interactive learning experience to learners. The machines receive learning plans and predicted questions from a server and present them to learners in an interactive manner.

[0117] For example, if an elementary school student instructs a machine to "work on today's math assignment," the machine will present the most suitable problems and review topics for the day, both audibly and visually, based on results generated from the server. An example of a prompt to the server using a generative AI model would be, "Create basic math problems for elementary school students, based on their current learning progress. Design them to be easy for them to understand." This allows learners to receive continuous and optimized instruction.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users input their study time, studied subjects, and evaluation results through a dedicated application on their device. This input data is encrypted and sent to the server. Encryption is performed to ensure the security of user data.

[0121] Step 2:

[0122] The server analyzes the received input data. The analysis algorithm identifies the learner's progress and strengths and weaknesses, and generates an optimal learning plan based on this. This plan includes recommended allocation of study time and areas to focus on.

[0123] Step 3:

[0124] The server automatically generates key learning points based on the learning plan. The generated notes are provided in a concise format, enabling users to acquire knowledge efficiently.

[0125] Step 4:

[0126] The server analyzes past exam data from the target educational institution. It models the question trends and generates predictive questions. This process uses a generative AI model and leverages prompts based on past question patterns. For example, a prompt might say, "Generate new predictive questions based on the past math question trends of your target school."

[0127] Step 5:

[0128] The server sends the generated study plan, notes on key points, and predicted questions to the user's device. This allows the user to instantly access this information and clearly define their next learning activity.

[0129] Step 6:

[0130] Users receive information via their devices and reflect it in the educational machine, allowing them to learn interactively. The educational machine uses speech recognition and natural language processing to respond to user conversations and questions, supporting their learning.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] The present invention aims to provide a more effective learning experience by combining emotion recognition functionality with a system that individually optimizes learning support for learners. This system is implemented through interaction between a server, a terminal, and the user, and has the following characteristics.

[0133] Data entry and emotion recognition

[0134] User: First, learners input basic information such as study time, subjects studied, and test results into the device. Furthermore, emotional data is collected through devices that capture the user's voice and facial expressions.

[0135] Terminal: Temporarily stores basic and emotional data. Stored data is sent to the server using a communication protocol. Encryption is applied during transmission to ensure data security.

[0136] Creating and adjusting a learning plan

[0137] Server: Analyzes received data and generates a learning plan optimized for the learner. This plan generation incorporates an algorithm that takes the learner's emotional state into account. For example, if the server determines that the learner is tired, it will suggest lighter learning content that prioritizes review.

[0138] Server: After generating a learning plan, it extracts key learning points and automatically generates notes. These notes are designed to aid learners' understanding and enable effective review.

[0139] Providing trend analysis and prediction problems

[0140] Server: Analyzes data, including past exam questions from the target school, to model question trends. It also incorporates sentiment data to provide predictive questions in the least burdensome way possible for learners.

[0141] Progress evaluation and feedback

[0142] Server: Analyzes acquired training data and sentiment data together to evaluate learning progress. Based on the evaluation results, provides feedback to the learner and incorporates it into the next learning plan.

[0143] Enhancing the user experience

[0144] Terminal: Receives data sent from the server and presents the user with a learning plan, notes, and predicted questions. This allows the user to learn in a way that suits their current mental state and maintain a high level of motivation.

[0145] As a concrete example, let's consider a high school student studying mathematics at home. If this student says "I'm tired" using voice data, or if fatigue is detected from their facial expression, the system can reduce the amount of studying for that day and revise the schedule for the next day. This dynamic adjustment aims to maximize learning effectiveness and maintain motivation.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] User: Learners use a learning app to input study time, subjects, and test results. Simultaneously, they activate a device that records their emotions using a microphone and camera. This captures audio and facial data.

[0149] Step 2:

[0150] Device: Temporarily stores acquired training data and sentiment data, encrypts it, and prepares it for transmission to the server. At this stage, simple feedback is provided within the app to verify the accuracy of the data.

[0151] Step 3:

[0152] Terminal: Encrypts the saved data and sends it to the server. During this process, data is transmitted in real time, and the user is shown the transmission status.

[0153] Step 4:

[0154] Server: Receives data, stores it in a database, and performs necessary preprocessing for analysis. In this preparation stage, incomplete data and outliers are removed.

[0155] Step 5:

[0156] Server: Analyzes training data to generate a learning plan optimized for the learner. During this process, emotional data is used to adjust the plan. For example, if the learner is experiencing high stress levels, a less stressful learning pace will be set.

[0157] Step 6:

[0158] Server: Based on the learning plan, it automatically generates notes highlighting key learning points. This includes visual presentations based on the user's interests and level of focus.

[0159] Step 7:

[0160] Server: Extracts question trends from past question data and generates predicted questions at a difficulty level appropriate to the user's mental state. In particular, it optimizes learning effectiveness by providing questions that are neither too easy nor too difficult.

[0161] Step 8:

[0162] Server: Analyzes the feedback received and makes adjustments for the next learning cycle. This feedback includes encouraging and motivating messages tailored to the current emotional state.

[0163] Step 9:

[0164] Terminal: Displays learning plans, notes, practice questions, and feedback received from the server to the user. The user can access new plans and review the next steps through the interface.

[0165] (Example 2)

[0166] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0167] Conventional learning support systems have limitations in improving learning effectiveness and maintaining motivation because they do not adequately consider the emotional state and progress of individual learners during optimization. Furthermore, the security of received data is insufficient, and protecting privacy is a challenge. Additionally, there is a need to improve the quality of individualized learning due to insufficient analysis of question trends and the provision of necessary feedback.

[0168] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0169] In this invention, the server includes an information receiving means for receiving information from learners, a planning means for analyzing the received information and creating a learning plan suitable for the learner, and an emotion identification means for identifying the learner's emotional state and reflecting it in the learning plan. This makes it possible to optimize the learning plan in real time according to the individual state of the learner and to achieve high learning effectiveness while ensuring data security.

[0170] "Information receiving means" refers to a device or software equipped with the function of receiving input information from learners.

[0171] A "plan creation means" is a device or program that has the function of creating a learning plan suitable for the learner based on the information received.

[0172] "Note-taking means" refers to a device or program that has the function of automatically generating important learning points based on a learning plan.

[0173] A "trend analysis tool" is a device or software equipped with the function of analyzing past exam data and constructing a model of question trends.

[0174] "Problem creation means" refers to a device or program that has the function of generating predicted problems based on trend analysis.

[0175] A "progress evaluation tool" is a device or program that has the function of evaluating the learner's progress and providing appropriate feedback.

[0176] An "emotion identification tool" is a device or program that has the function of identifying a learner's emotional state and reflecting it in the learning plan.

[0177] "Encryption means" refers to a device or program that has the function of performing encryption processing to ensure the security of received information.

[0178] This invention is a system for individually optimizing support for learners, and consists of three components: a server, a terminal, and a user. The system is configured as follows:

[0179] Server: The server is equipped with a receiving mechanism for taking in information. This information includes learner input data and sentiment data, and a planning mechanism is used to analyze this data. This analysis uses a neural network implemented in Python, and a generative AI model operates. The server also models question trends based on past exam data and generates predicted questions as needed. Furthermore, it includes a progress evaluation mechanism that assesses the learner's progress and provides appropriate feedback. It also incorporates a sentiment recognition mechanism that identifies the learner's emotions and reflects them in the learning plan.

[0180] Terminal: The terminal is responsible for temporarily storing information collected from the user and sending it to the server. The HTTPS protocol is used for information transmission, and AES encryption technology is applied to ensure security. The terminal has a GUI that presents learning plans, notes, and problems sent from the server to the user.

[0181] User: Users input information related to their learning through their device. For example, when a user is studying mathematics, they might say "I'm tired" to collect audio data. This speech information and facial expression data are sent to the server, and an appropriate learning plan is adjusted. For instance, if the user feels tired, the learning content is adjusted to be lighter.

[0182] The system is optimized to help learners continue learning more effectively. A concrete example of a prompt is, "How can we optimize the learning plan based on the user's emotional data?" This allows the system to dynamically provide learning support tailored to the learner's state.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] User: Learners input their study time, subjects studied, test results, and emotional data into the device. This input data includes voice and facial expression information. Specifically, the user's voice and facial expressions are recorded using a microphone and camera. This information is then converted into a digital format and stored on the device.

[0186] Step 2:

[0187] Terminal: The collected data is encrypted and prepared for transmission to the server. Specifically, AES encryption technology is used to ensure data security, and HTTPS is used as the communication protocol. Inputs include training data and sentiment data from users, and the output is an encrypted dataset.

[0188] Step 3:

[0189] Server: Analyzes received data and generates a learning plan suitable for the learner. The input is an encrypted dataset from the terminal, which is decrypted using AES encryption and analyzed by the planning mechanism. A generative AI model is used to calculate the optimal learning content, and the learning plan is provided as output. Specifically, a neural network operates to formulate a plan tailored to the learner's characteristics.

[0190] Step 4:

[0191] Server: Based on the learning plan, it extracts key points and automatically generates notes. The input is the learning plan, and the output is notes that can be used by the learner. Specifically, it uses a text generation function integrated into the plan creation mechanism, employing natural language processing (NLP) algorithms.

[0192] Step 5:

[0193] Server: Models question trends based on past exam data and creates predictive questions. The input is exam data, which is analyzed by a machine learning model. The output is a set of predictive questions tailored to the learner. Specifically, a trend analysis mechanism operates, using algorithms such as multilayer perceptrons to analyze trends.

[0194] Step 6:

[0195] Server: Based on training data and sentiment data, it evaluates learning progress and generates feedback. The input is the entire learner's data, which is analyzed using progress evaluation methods. The output is feedback to the learner generated in real time. Specifically, past learning history is queried from the server's database and progress is evaluated.

[0196] Step 7:

[0197] Terminal: Presents learning plans, notes, and problems obtained from the server to the user. The input is educational content provided by the server, which is output via a GUI. Specifically, information is presented graphically on the terminal's display, and the user uses this to progress through their learning.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0200] Traditional learning support systems provided uniform learning plans without considering learners' emotions or fatigue levels, making it difficult to maintain learner motivation and maximize learning effectiveness. Furthermore, there was a need for a support system that could provide feedback at appropriate times and adjust the learning pace for each individual learner.

[0201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0202] In this invention, the server includes an acquisition means for acquiring input data related to learning, a plan generation means for analyzing the acquired input data and emotional information to generate a learning plan optimized for the learner, and a sensing and adjustment means for sensing the learner's state and adjusting the learning pace. This makes it possible to provide an individually optimized learning experience that responds to the learner's emotional state.

[0203] "Acquisition means" refers to devices and methods used to collect input data related to learning.

[0204] A "plan generation means" is a device or method that analyzes collected data and emotional information to create a learning plan optimized for the learner.

[0205] "Automatic generation means" refers to a device or method that has the function of automatically generating important learning information based on the created learning plan.

[0206] "Analysis means" refers to devices and methods for analyzing past evaluation information and modeling learning trends.

[0207] "Information generation means" refers to devices or methods that have the function of generating predictive information based on modeled learning trends.

[0208] A "sensing and adjusting means" refers to a device or method for sensing the learner's state and appropriately adjusting the learning pace.

[0209] A "feedback provision means" refers to a device or method that has the function of evaluating the learner's progress and providing appropriate feedback.

[0210] The system for implementing this invention is a comprehensive learning support system that incorporates emotion recognition technology to individually optimize the learner's learning experience. The entire system operates primarily through user terminals, a server, and user interaction.

[0211] The user terminal is responsible for acquiring input data from learners. This data includes not only learning-related information such as study time and subjects, but also emotional information through facial expressions and voice data. For this purpose, devices such as cameras and microphones are used. This data is temporarily stored on the terminal, encrypted, and then securely transmitted to the server.

[0212] The server analyzes the received data and generates an optimal learning plan for the learner. This plan generation utilizes Python and an emotion recognition model, employing libraries such as TENSORFLOW® and OpenCV. Furthermore, it uses past training data to model learning trends through analytical methods and generate new prediction problems. For this process, the use of data processing libraries such as Pandas and NumPy is recommended.

[0213] Regarding learners' progress, the server evaluates the data and generates feedback through a feedback provision system. This feedback is transmitted to the learner via the user terminal, contributing to adjustments to the learning plan and maintenance of motivation.

[0214] For example, if a middle school student is studying for a history test at home, and the system detects fatigue through voice analysis, it will slow down the learning pace and prioritize less demanding content. This allows the learner to continue studying without undue stress.

[0215] As an example of a prompt, sending an instruction to the AI ​​model's interface such as, "Use audio data to detect learner fatigue and adjust learning appropriately," can simplify the system's operation.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The device acquires input data from the learner. This input data includes study time, subjects studied, and emotional information through facial expressions and voice data. It operates using the camera and microphone and temporarily stores the data. This data is then encrypted and sent to the server in a secure manner.

[0219] Step 2:

[0220] The server analyzes the input data and emotional information received from the terminal. Using an emotion recognition model, it analyzes emotions from facial expressions and voice using TensorFlow and OpenCV. This allows the server to understand the learner's emotional state, and the analysis results are used to generate a learning plan in the next step.

[0221] Step 3:

[0222] The server generates a learning plan optimized for the learner based on the analysis results. Using Python, it creates more appropriate learning content and progress schedules, taking into account the analyzed sentiment data. The output is the specific learning content that the learner should work on.

[0223] Step 4:

[0224] The server analyzes past evaluation data and models learning trends. It uses Pandas and NumPy to format the data and perform trend analysis. Based on the analysis results, it generates a prediction problem and outputs that problem.

[0225] Step 5:

[0226] The server provides feedback based on the learning plan and predicted questions. It evaluates the learner's progress and creates feedback that should be incorporated into the next learning plan. The output feedback includes information about the learner's progress and areas for improvement.

[0227] Step 6:

[0228] The device displays the learning plan, predicted questions, and feedback sent from the server. This allows learners to check their learning progress and prepare for the next lesson. Specifically, it uses the information acquired by the user to advance the learning process and adjust the plan as needed.

[0229] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0245] This invention provides a system for offering individually optimized learning support to learners, primarily through interactions between a server, a terminal, and the user. This document describes the main elements of the system and their functions, and details its capabilities with specific examples.

[0246] Data entry and reception from learners

[0247] User: Learners use a dedicated application to input data such as study time, subjects studied, and test results into their devices. This information serves as the basis for tracking learners' progress in real time and generating individualized learning plans.

[0248] Terminal: Stores the entered data and sends it to the server using a secure communication method.

[0249] Creating study plans and notes

[0250] Server: The server processes the received data using multiple analysis algorithms to generate a learning plan optimized for the learner. This plan includes the allocation of study time for each subject and the content to be reviewed. Furthermore, based on the learning plan, it automatically generates notes summarizing important learning points and provides them to the learner. These notes function as a tool to effectively support the user's learning.

[0251] Analysis of past exam questions and provision of predicted questions

[0252] Server: Analyzes past exam data from the student's target school and models the question trends. For example, if a history exam shows a tendency for many questions to be about names and events, the server generates new predictive questions based on that trend. This allows students to prepare for entrance exams in a more practical way.

[0253] Providing progress feedback

[0254] Server: Analyzes learners' cumulative data to analyze their progress and generates feedback to adjust their next learning plan. This feedback includes identifying subjects to prioritize next and highlighting areas of weakness.

[0255] Enhancing the user experience

[0256] Terminal: Provides learners with an interactive learning experience by instantly displaying learning plans, predicted questions, and feedback provided by the server. This allows learners to independently and effectively manage their daily learning progress.

[0257] For example, if a junior high school student wants to improve their English vocabulary, they can input their learning history into the system. The server will then analyze this data and prioritize incorporating a new vocabulary list and review questions into their plan for the following day. In this way, the present invention provides a learning environment tailored to the individual needs of each learner.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] User: Users input daily learning data, such as study time, subject information, and test results, into a dedicated app. This data serves as foundational information for evaluating learning progress and effectiveness.

[0261] Step 2:

[0262] Terminal: Temporarily stores the entered data. Once saving is complete, it encrypts the data and prepares it for secure transmission to the server.

[0263] Step 3:

[0264] Terminal: Sends data to the server via the internet. During transmission, the data is sent in an encrypted format to ensure data security.

[0265] Step 4:

[0266] Server: Stores received training data in a database and prepares it for analysis. Based on the received data, it evaluates the learner's current learning progress.

[0267] Step 5:

[0268] Server: Applies multiple analysis algorithms to generate a learning plan optimized for each individual learner. The analysis takes into account past learning history, test results, and the learner's goals.

[0269] Step 6:

[0270] Server: Based on the learning plan, it automatically extracts key learning points and provides them to the user in note format. These notes are used by learners to improve the efficiency of review and confirmation.

[0271] Step 7:

[0272] Server: Analyzes past exam data from the target school and models the question trends. Based on the analysis results, it generates predictive questions for use in the next step.

[0273] Step 8:

[0274] Server: Incorporates prediction problems generated based on the model into the user's learning plan and prepares to send them from the server to the terminal.

[0275] Step 9:

[0276] Terminal: Receives data sent from the server and presents the user with new learning plans, notes, and predicted questions. The user then uses this information to proceed with their learning.

[0277] Step 10:

[0278] Server: Analyzes the user's cumulative learning data and evaluates learning progress. Based on this evaluation, it generates feedback to help improve the next learning plan.

[0279] (Example 1)

[0280] Next, we will describe Example 1. 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."

[0281] In the field of education, there is a demand to provide individually optimized learning support tailored to each learner. Conventional education support systems can only provide standardized learning plans and problem sets, and there is an issue that they cannot fully meet the individual needs of learners. In addition, the grasp of learning progress and the provision of feedback are insufficient, making it difficult to achieve efficient learning.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0283] In this invention, the server includes a receiving means for receiving input information from a learner, a plan generation means for analyzing the received information and generating a learning plan optimized for the learner, and a generation means for using AI technology for generating prediction questions. Thereby, efficient and effective learning according to the progress and needs of each learner becomes possible.

[0284] The "receiving means" is a component having a function for taking in input information from a learner into the system.

[0285] The "plan generation means" is a component having a function for creating a learning plan optimized for a learner based on the received information.

[0286] The "automatic record generation means" is a component having a function for automatically constructing important learning items based on a learning plan.

[0287] The "tendency analysis means" is a component having a function for analyzing past problem information and modeling the tendency of question posing.

[0288] The "question generation means" is a component having a function for creating prediction questions based on the modeled tendency of question posing.

[0289] The "progress feedback means" is a component having a function for analyzing the progress of a learner and providing feedback on the result.

[0290] A "generation means" is a component that has the function of creating predictive problems and learning plans using generative AI technology.

[0291] "Display means" refers to a component that has the function of visualizing feedback information or learning plans.

[0292] The educational support system of this invention is designed to provide learners with individually optimized learning plans and to support their progress. This system primarily functions through three elements: a server, a terminal, and a user.

[0293] The server receives input information sent by learners and stores it in a database. This information includes study time, subjects studied, and test results. Based on this information, the server uses analytical algorithms and generative AI models to generate a learning plan optimized for the learner. Programming languages ​​such as Python and R are used for data analysis during this process.

[0294] The server further analyzes past question data and models question trends to generate predicted questions. This utilizes a generative AI model incorporating natural language processing (NLP) technology. This allows learners to engage in practical problem-solving exercises.

[0295] The terminal is responsible for receiving learning plans and practice questions provided by the server and displaying them to the learner. The terminal uses encryption technologies such as SSL / TLS to securely store received data. Furthermore, it provides feedback information and progress status visually through a graphical user interface, presenting it in a way that is easy for learners to understand.

[0296] Users input their learning information using a dedicated app on their device. These input operations are performed via a touchscreen or keyboard, allowing for intuitive operation. For example, if a middle school student wants to improve their English vocabulary, they input their learning history into the system, and the server analyzes that data to generate a learning plan that prioritizes new vocabulary lists and review questions.

[0297] An example of a prompt message would be sending a request to the AI ​​model to "analyze past history exam data and generate predictive questions that will be useful for the next exam." In this way, the system functions as a tool to support learners in efficient and effective learning.

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] User: Learners use a dedicated application on their device to input information such as study time, subjects studied, and test results. Input is done using the touchscreen or keyboard. The entered data is temporarily stored in a database on the device.

[0301] Step 2:

[0302] Terminal: The entered data is securely transmitted to the server using the SSL / TLS protocol. At this time, the data is converted to JSON format and configured as a packet. An acknowledgment is used to confirm that the converted data has been transmitted correctly.

[0303] Step 3:

[0304] Server: Store the received data in a database system. The database is structured using SQL. Next, use a data analysis algorithm to extract the basic data for generating a learning plan. For example, the learning frequency and performance trends of each subject are analyzed.

[0305] Step 4:

[0306] Server: Utilize the generated AI model based on the extracted data to generate an individually optimized learning plan. The generated learning plan includes the learning time allocation and review content for each subject. The generated AI model is often implemented using Python libraries.

[0307] Step 5:

[0308] Server: Automatically generate learning notes based on the learning plan. These notes summarize important learning points. Also, analyze the data of past questions, model the question - setting trends, and then use the generated AI model to generate prediction questions.

[0309] Step 6:

[0310] Server: Analyze the learner's past progress data and create feedback for adjusting the next learning plan. The feedback includes the subjects and tasks that should be prioritized for learning.

[0311] Step 7:

[0312] Terminal: Receive the learning plan, notes, prediction questions, and feedback sent from the server and display them to the user. When displaying, use a GUI to provide a visual and intuitive interface. The learner plans and executes the next learning activities based on this information.

[0313] (Application Example 1)

[0314] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0315] Conventional technologies for providing personalized learning support have struggled to reflect learners' needs in real time, particularly in analyzing past exam questions and automatically generating learning plans. Furthermore, effectively providing feedback based on learners' progress remains a challenge. Additionally, there is a lack of technologies that provide learning plans and feedback visually and interactively.

[0316] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0317] In this invention, the server includes means for receiving input information from learners, means for generating a learning plan optimized for the learner by analyzing the received input information, means for automatically generating important learning points based on the learning plan, and means for analyzing past exam data from the desired educational institution and modeling the question trends. This enables interactive and effective learning support that reflects the different needs of each learner in real time.

[0318] A "learner" is an individual who aims to acquire specific knowledge or skills.

[0319] "Input information" refers to data that learners provide to the system, including information about learning progress, learning content, and outcomes.

[0320] An "optimized learning plan" is a plan for guiding efficient learning activities that are tailored to the individual circumstances and goals of each learner.

[0321] A "plan generation means" is a component that has the function of analyzing the learner's input information and automatically creating an optimized learning plan.

[0322] An "automatic note generation method" is a component for organizing important learning points based on a learning plan and providing them in note format.

[0323] A "trend analysis tool" is a component that has the function of analyzing past question data and modeling the patterns and trends of those questions.

[0324] An "educational machine" is a machine with interactive features designed to support educational activities.

[0325] The system for realizing this invention has advanced data analysis and interactive functions for educational support. It primarily consists of a server, terminals, and educational equipment working together, with each element playing its individual role while forming the overall system.

[0326] The server functions as a centralized data receiving and processing unit. It receives input information sent from learners and performs analysis. The received information is evaluated using information analysis algorithms to generate an optimized learning plan for each learner. This plan includes the allocation of learning time and review points for specific concepts and areas. Subsequently, a generative AI model analyzes past exam data from the target educational institution to model future exam trends. This process analyzes past exam patterns and generates new predictive questions.

[0327] The terminal is a device operated by the learner, providing data input and communication with the server. The application on the terminal has an interface that allows the learner to input learning time and learning content. Furthermore, the input data is encrypted before being sent to the server. The terminal has a means of displaying learning plans and feedback provided by the server, providing learners with immediate feedback and information.

[0328] Educational machines are devices designed to facilitate learning support and provide physical interaction capabilities. These machines utilize speech recognition and natural language processing technologies to deliver an interactive learning experience to learners. The machines receive learning plans and predicted questions from a server and present them to learners in an interactive manner.

[0329] For example, if an elementary school student instructs a machine to "work on today's math assignment," the machine will present the most suitable problems and review topics for the day, both audibly and visually, based on results generated from the server. An example of a prompt to the server using a generative AI model would be, "Create basic math problems for elementary school students, based on their current learning progress. Design them to be easy for them to understand." This allows learners to receive continuous and optimized instruction.

[0330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0331] Step 1:

[0332] Users input their study time, studied subjects, and evaluation results through a dedicated application on their device. This input data is encrypted and sent to the server. Encryption is performed to ensure the security of user data.

[0333] Step 2:

[0334] The server analyzes the received input data. The analysis algorithm identifies the learner's progress and strengths and weaknesses, and generates an optimal learning plan based on this. This plan includes recommended allocation of study time and areas to focus on.

[0335] Step 3:

[0336] The server automatically generates key learning points based on the learning plan. The generated notes are provided in a concise format, enabling users to acquire knowledge efficiently.

[0337] Step 4:

[0338] The server analyzes past exam data from the target educational institution. It models the question trends and generates predictive questions. This process uses a generative AI model and leverages prompts based on past question patterns. For example, a prompt might say, "Generate new predictive questions based on the past math question trends of your target school."

[0339] Step 5:

[0340] The server sends the generated study plan, notes on key points, and predicted questions to the user's device. This allows the user to instantly access this information and clearly define their next learning activity.

[0341] Step 6:

[0342] Users receive information via their devices and reflect it in the educational machine, allowing them to learn interactively. The educational machine uses speech recognition and natural language processing to respond to user conversations and questions, supporting their learning.

[0343] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0344] The present invention aims to provide a more effective learning experience by combining emotion recognition functionality with a system that individually optimizes learning support for learners. This system is implemented through interaction between a server, a terminal, and the user, and has the following characteristics.

[0345] Data entry and emotion recognition

[0346] User: First, learners input basic information such as study time, subjects studied, and test results into the device. Furthermore, emotional data is collected through devices that capture the user's voice and facial expressions.

[0347] Terminal: Temporarily stores basic and emotional data. Stored data is sent to the server using a communication protocol. Encryption is applied during transmission to ensure data security.

[0348] Creating and adjusting a learning plan

[0349] Server: Analyzes received data and generates a learning plan optimized for the learner. This plan generation incorporates an algorithm that takes the learner's emotional state into account. For example, if the server determines that the learner is tired, it will suggest lighter learning content that prioritizes review.

[0350] Server: After generating a learning plan, it extracts key learning points and automatically generates notes. These notes are designed to aid learners' understanding and enable effective review.

[0351] Providing trend analysis and prediction problems

[0352] Server: Analyzes data, including past exam questions from the target school, to model question trends. It also incorporates sentiment data to provide predictive questions in the least burdensome way possible for learners.

[0353] Progress evaluation and feedback

[0354] Server: Analyzes acquired training data and sentiment data together to evaluate learning progress. Based on the evaluation results, provides feedback to the learner and incorporates it into the next learning plan.

[0355] Enhancing the user experience

[0356] Terminal: Receives data sent from the server and presents the user with a learning plan, notes, and predicted questions. This allows the user to learn in a way that suits their current mental state and maintain a high level of motivation.

[0357] As a concrete example, let's consider a high school student studying mathematics at home. If this student says "I'm tired" using voice data, or if fatigue is detected from their facial expression, the system can reduce the amount of studying for that day and revise the schedule for the next day. This dynamic adjustment aims to maximize learning effectiveness and maintain motivation.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] User: Learners use a learning app to input study time, subjects, and test results. Simultaneously, they activate a device that records their emotions using a microphone and camera. This captures audio and facial data.

[0361] Step 2:

[0362] Device: Temporarily stores acquired training data and sentiment data, encrypts it, and prepares it for transmission to the server. At this stage, simple feedback is provided within the app to verify the accuracy of the data.

[0363] Step 3:

[0364] Terminal: Encrypts the saved data and sends it to the server. During this process, data is transmitted in real time, and the user is shown the transmission status.

[0365] Step 4:

[0366] Server: Receives data, stores it in a database, and performs necessary preprocessing for analysis. In this preparation stage, incomplete data and outliers are removed.

[0367] Step 5:

[0368] Server: Analyzes training data to generate a learning plan optimized for the learner. During this process, emotional data is used to adjust the plan. For example, if the learner is experiencing high stress levels, a less stressful learning pace will be set.

[0369] Step 6:

[0370] Server: Based on the learning plan, it automatically generates notes highlighting key learning points. This includes visual presentations based on the user's interests and level of focus.

[0371] Step 7:

[0372] Server: Extracts question trends from past question data and generates predicted questions at a difficulty level appropriate to the user's mental state. In particular, it optimizes learning effectiveness by providing questions that are neither too easy nor too difficult.

[0373] Step 8:

[0374] Server: Analyzes the feedback received and makes adjustments for the next learning cycle. This feedback includes encouraging and motivating messages tailored to the current emotional state.

[0375] Step 9:

[0376] Terminal: Displays learning plans, notes, practice questions, and feedback received from the server to the user. The user can access new plans and review the next steps through the interface.

[0377] (Example 2)

[0378] Next, we will describe Example 2. 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".

[0379] Conventional learning support systems have limitations in improving learning effectiveness and maintaining motivation because they do not adequately consider the emotional state and progress of individual learners during optimization. Furthermore, the security of received data is insufficient, and protecting privacy is a challenge. Additionally, there is a need to improve the quality of individualized learning due to insufficient analysis of question trends and the provision of necessary feedback.

[0380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0381] In this invention, the server includes an information receiving means for receiving information from learners, a planning means for analyzing the received information and creating a learning plan suitable for the learner, and an emotion identification means for identifying the learner's emotional state and reflecting it in the learning plan. This makes it possible to optimize the learning plan in real time according to the individual state of the learner and to achieve high learning effectiveness while ensuring data security.

[0382] "Information receiving means" refers to a device or software equipped with the function of receiving input information from learners.

[0383] A "plan creation means" is a device or program that has the function of creating a learning plan suitable for the learner based on the information received.

[0384] "Note-taking means" refers to a device or program that has the function of automatically generating important learning points based on a learning plan.

[0385] A "trend analysis tool" is a device or software equipped with the function of analyzing past exam data and constructing a model of question trends.

[0386] "Problem creation means" refers to a device or program that has the function of generating predicted problems based on trend analysis.

[0387] A "progress evaluation tool" is a device or program that has the function of evaluating the learner's progress and providing appropriate feedback.

[0388] An "emotion identification tool" is a device or program that has the function of identifying a learner's emotional state and reflecting it in the learning plan.

[0389] "Encryption means" refers to a device or program that has the function of performing encryption processing to ensure the security of received information.

[0390] This invention is a system for individually optimizing support for learners, and consists of three components: a server, a terminal, and a user. The system is configured as follows:

[0391] Server: The server is equipped with a receiving mechanism for taking in information. This information includes learner input data and sentiment data, and a planning mechanism is used to analyze this data. This analysis uses a neural network implemented in Python, and a generative AI model operates. The server also models question trends based on past exam data and generates predicted questions as needed. Furthermore, it includes a progress evaluation mechanism that assesses the learner's progress and provides appropriate feedback. It also incorporates a sentiment recognition mechanism that identifies the learner's emotions and reflects them in the learning plan.

[0392] Terminal: The terminal is responsible for temporarily storing information collected from the user and sending it to the server. The HTTPS protocol is used for information transmission, and AES encryption technology is applied to ensure security. The terminal has a GUI that presents learning plans, notes, and problems sent from the server to the user.

[0393] User: Users input information related to their learning through their device. For example, when a user is studying mathematics, they might say "I'm tired" to collect audio data. This speech information and facial expression data are sent to the server, and an appropriate learning plan is adjusted. For instance, if the user feels tired, the learning content is adjusted to be lighter.

[0394] The system is optimized to help learners continue learning more effectively. A concrete example of a prompt is, "How can we optimize the learning plan based on the user's emotional data?" This allows the system to dynamically provide learning support tailored to the learner's state.

[0395] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0396] Step 1:

[0397] User: Learners input their study time, subjects studied, test results, and emotional data into the device. This input data includes voice and facial expression information. Specifically, the user's voice and facial expressions are recorded using a microphone and camera. This information is then converted into a digital format and stored on the device.

[0398] Step 2:

[0399] Terminal: The collected data is encrypted and prepared for transmission to the server. Specifically, AES encryption technology is used to ensure data security, and HTTPS is used as the communication protocol. Inputs include training data and sentiment data from users, and the output is an encrypted dataset.

[0400] Step 3:

[0401] Server: Analyzes received data and generates a learning plan suitable for the learner. The input is an encrypted dataset from the terminal, which is decrypted using AES encryption and analyzed by the planning mechanism. A generative AI model is used to calculate the optimal learning content, and the learning plan is provided as output. Specifically, a neural network operates to formulate a plan tailored to the learner's characteristics.

[0402] Step 4:

[0403] Server: Based on the learning plan, it extracts key points and automatically generates notes. The input is the learning plan, and the output is notes that can be used by the learner. Specifically, it uses a text generation function integrated into the plan creation mechanism, employing natural language processing (NLP) algorithms.

[0404] Step 5:

[0405] Server: Models question trends based on past exam data and creates predictive questions. The input is exam data, which is analyzed by a machine learning model. The output is a set of predictive questions tailored to the learner. Specifically, a trend analysis mechanism operates, using algorithms such as multilayer perceptrons to analyze trends.

[0406] Step 6:

[0407] Server: Based on training data and sentiment data, it evaluates learning progress and generates feedback. The input is the entire learner's data, which is analyzed using progress evaluation methods. The output is feedback to the learner generated in real time. Specifically, past learning history is queried from the server's database and progress is evaluated.

[0408] Step 7:

[0409] Terminal: Presents learning plans, notes, and problems obtained from the server to the user. The input is educational content provided by the server, which is output via a GUI. Specifically, information is presented graphically on the terminal's display, and the user uses this to progress through their learning.

[0410] (Application Example 2)

[0411] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0412] Traditional learning support systems provided uniform learning plans without considering learners' emotions or fatigue levels, making it difficult to maintain learner motivation and maximize learning effectiveness. Furthermore, there was a need for a support system that could provide feedback at appropriate times and adjust the learning pace for each individual learner.

[0413] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0414] In this invention, the server includes an acquisition means for acquiring input data related to learning, a plan generation means for analyzing the acquired input data and emotional information to generate a learning plan optimized for the learner, and a sensing and adjustment means for sensing the learner's state and adjusting the learning pace. This makes it possible to provide an individually optimized learning experience that responds to the learner's emotional state.

[0415] "Acquisition means" refers to devices and methods used to collect input data related to learning.

[0416] A "plan generation means" is a device or method that analyzes collected data and emotional information to create a learning plan optimized for the learner.

[0417] "Automatic generation means" refers to a device or method that has the function of automatically generating important learning information based on the created learning plan.

[0418] "Analysis means" refers to devices and methods for analyzing past evaluation information and modeling learning trends.

[0419] "Information generation means" refers to devices or methods that have the function of generating predictive information based on modeled learning trends.

[0420] A "sensing and adjusting means" refers to a device or method for sensing the learner's state and appropriately adjusting the learning pace.

[0421] A "feedback provision means" refers to a device or method that has the function of evaluating the learner's progress and providing appropriate feedback.

[0422] The system for implementing this invention is a comprehensive learning support system that incorporates emotion recognition technology to individually optimize the learner's learning experience. The entire system operates primarily through user terminals, a server, and user interaction.

[0423] The user terminal is responsible for acquiring input data from learners. This data includes not only learning-related information such as study time and subjects, but also emotional information through facial expressions and voice data. For this purpose, devices such as cameras and microphones are used. This data is temporarily stored on the terminal, encrypted, and then securely transmitted to the server.

[0424] The server analyzes the received data and generates an optimal learning plan for the learner. This plan generation utilizes an emotion recognition model using Python, and can employ libraries such as TensorFlow and OpenCV. Furthermore, it models learning trends using past training data and generates new prediction problems. For this process, it is recommended to use data processing libraries such as Pandas and NumPy for data analysis.

[0425] Regarding learners' progress, the server evaluates the data and generates feedback through a feedback provision system. This feedback is transmitted to the learner via the user terminal, contributing to adjustments to the learning plan and maintenance of motivation.

[0426] For example, if a middle school student is studying for a history test at home, and the system detects fatigue through voice analysis, it will slow down the learning pace and prioritize less demanding content. This allows the learner to continue studying without undue stress.

[0427] As an example of a prompt, sending an instruction to the AI ​​model's interface such as, "Use audio data to detect learner fatigue and adjust learning appropriately," can simplify the system's operation.

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The device acquires input data from the learner. This input data includes study time, subjects studied, and emotional information through facial expressions and voice data. It operates using the camera and microphone and temporarily stores the data. This data is then encrypted and sent to the server in a secure manner.

[0431] Step 2:

[0432] The server analyzes the input data and emotional information received from the terminal. Using an emotion recognition model, it analyzes emotions from facial expressions and voice using TensorFlow and OpenCV. This allows the server to understand the learner's emotional state, and the analysis results are used to generate a learning plan in the next step.

[0433] Step 3:

[0434] The server generates a learning plan optimized for the learner based on the analysis results. Using Python, it creates more appropriate learning content and progress schedules, taking into account the analyzed sentiment data. The output is the specific learning content that the learner should work on.

[0435] Step 4:

[0436] The server analyzes past evaluation data and models learning trends. It uses Pandas and NumPy to format the data and perform trend analysis. Based on the analysis results, it generates a prediction problem and outputs that problem.

[0437] Step 5:

[0438] The server provides feedback based on the learning plan and predicted questions. It evaluates the learner's progress and creates feedback that should be incorporated into the next learning plan. The output feedback includes information about the learner's progress and areas for improvement.

[0439] Step 6:

[0440] The device displays the learning plan, predicted questions, and feedback sent from the server. This allows learners to check their learning progress and prepare for the next lesson. Specifically, it uses the information acquired by the user to advance the learning process and adjust the plan as needed.

[0441] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0442] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0447] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0449] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0451] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0452] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0453] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0454] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0456] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0457] This invention provides a system for offering individually optimized learning support to learners, primarily through interactions between a server, a terminal, and the user. This document describes the main elements of the system and their functions, and details its capabilities with specific examples.

[0458] Data entry and reception from learners

[0459] User: Learners use a dedicated application to input data such as study time, subjects studied, and test results into their devices. This information serves as the basis for tracking learners' progress in real time and generating individualized learning plans.

[0460] Terminal: Stores the entered data and sends it to the server using a secure communication method.

[0461] Creating study plans and notes

[0462] Server: The server processes the received data using multiple analysis algorithms to generate a learning plan optimized for the learner. This plan includes the allocation of study time for each subject and the content to be reviewed. Furthermore, based on the learning plan, it automatically generates notes summarizing important learning points and provides them to the learner. These notes function as a tool to effectively support the user's learning.

[0463] Analysis of past exam questions and provision of predicted questions

[0464] Server: Analyzes past exam data from the student's target school and models the question trends. For example, if a history exam shows a tendency for many questions to be about names and events, the server generates new predictive questions based on that trend. This allows students to prepare for entrance exams in a more practical way.

[0465] Providing progress feedback

[0466] Server: Analyzes learners' cumulative data to analyze their progress and generates feedback to adjust their next learning plan. This feedback includes identifying subjects to prioritize next and highlighting areas of weakness.

[0467] Enhancing the user experience

[0468] Terminal: Provides learners with an interactive learning experience by instantly displaying learning plans, predicted questions, and feedback provided by the server. This allows learners to independently and effectively manage their daily learning progress.

[0469] For example, if a junior high school student wants to improve their English vocabulary, they can input their learning history into the system. The server will then analyze this data and prioritize incorporating a new vocabulary list and review questions into their plan for the following day. In this way, the present invention provides a learning environment tailored to the individual needs of each learner.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] User: Users input daily learning data, such as study time, subject information, and test results, into a dedicated app. This data serves as foundational information for evaluating learning progress and effectiveness.

[0473] Step 2:

[0474] Terminal: Temporarily stores the entered data. Once saving is complete, it encrypts the data and prepares it for secure transmission to the server.

[0475] Step 3:

[0476] Terminal: Sends data to the server via the internet. During transmission, the data is sent in an encrypted format to ensure data security.

[0477] Step 4:

[0478] Server: Stores received training data in a database and prepares it for analysis. Based on the received data, it evaluates the learner's current learning progress.

[0479] Step 5:

[0480] Server: Applies multiple analysis algorithms to generate a learning plan optimized for each individual learner. The analysis takes into account past learning history, test results, and the learner's goals.

[0481] Step 6:

[0482] Server: Based on the learning plan, it automatically extracts key learning points and provides them to the user in note format. These notes are used by learners to improve the efficiency of review and confirmation.

[0483] Step 7:

[0484] Server: Analyzes past exam data from the target school and models the question trends. Based on the analysis results, it generates predictive questions for use in the next step.

[0485] Step 8:

[0486] Server: Incorporates prediction problems generated based on the model into the user's learning plan and prepares to send them from the server to the terminal.

[0487] Step 9:

[0488] Terminal: Receives data sent from the server and presents the user with new learning plans, notes, and predicted questions. The user then uses this information to proceed with their learning.

[0489] Step 10:

[0490] Server: Analyzes the user's cumulative learning data and evaluates learning progress. Based on this evaluation, it generates feedback to help improve the next learning plan.

[0491] (Example 1)

[0492] Next, we will describe Example 1. 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."

[0493] In the field of education, there is a demand for providing individually optimized learning support tailored to each learner. Traditional educational support systems can only provide uniform learning plans and problems, and have the problem of not being able to adequately address the individual needs of learners. In addition, the monitoring of learning progress and the provision of feedback are insufficient, making it difficult to achieve efficient learning.

[0494] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0495] In this invention, the server includes receiving means for receiving input information from learners, planning means for analyzing the received information and generating a learning plan optimized for the learner, and generating means for using generation AI technology to generate predictive problems. This enables efficient and effective learning tailored to the individual progress and needs of each learner.

[0496] A "receiving means" is a component that has the function of taking in input information from learners into the system.

[0497] A "plan generation means" is a component that has the function of creating a learning plan optimized for the learner based on the received information.

[0498] The "automatic record generation means" is a component that has the function of automatically constructing important learning items based on the learning plan.

[0499] A "trend analysis tool" is a component that has the function of analyzing past exam question information and modeling the trends in the questions asked.

[0500] A "problem generation means" is a component that has the function of creating predicted problems based on modeled question trends.

[0501] A "progress feedback mechanism" is a component that has the function of analyzing the learner's progress and providing feedback based on the results.

[0502] A "generation means" is a component that has the function of creating predictive problems and learning plans using generative AI technology.

[0503] "Display means" refers to a component that has the function of visualizing feedback information or learning plans.

[0504] The educational support system of this invention is designed to provide learners with individually optimized learning plans and to support their progress. This system primarily functions through three elements: a server, a terminal, and a user.

[0505] The server receives input information sent by learners and stores it in a database. This information includes study time, subjects studied, and test results. Based on this information, the server uses analytical algorithms and generative AI models to generate a learning plan optimized for the learner. Programming languages ​​such as Python and R are used for data analysis during this process.

[0506] The server further analyzes past question data and models question trends to generate predicted questions. This utilizes a generative AI model incorporating natural language processing (NLP) technology. This allows learners to engage in practical problem-solving exercises.

[0507] The terminal is responsible for receiving learning plans and practice questions provided by the server and displaying them to the learner. The terminal uses encryption technologies such as SSL / TLS to securely store received data. Furthermore, it provides feedback information and progress status visually through a graphical user interface, presenting it in a way that is easy for learners to understand.

[0508] Users input their learning information using a dedicated app on their device. These input operations are performed via a touchscreen or keyboard, allowing for intuitive operation. For example, if a middle school student wants to improve their English vocabulary, they input their learning history into the system, and the server analyzes that data to generate a learning plan that prioritizes new vocabulary lists and review questions.

[0509] An example of a prompt message would be sending a request to the AI ​​model to "analyze past history exam data and generate predictive questions that will be useful for the next exam." In this way, the system functions as a tool to support learners in efficient and effective learning.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] User: Learners use a dedicated application on their device to input information such as study time, subjects studied, and test results. Input is done using the touchscreen or keyboard. The entered data is temporarily stored in a database on the device.

[0513] Step 2:

[0514] Terminal: The entered data is securely transmitted to the server using the SSL / TLS protocol. At this time, the data is converted to JSON format and configured as a packet. An acknowledgment is used to confirm that the converted data has been transmitted correctly.

[0515] Step 3:

[0516] Server: Receives data and stores it in a database system. The database is structured using SQL. Next, data analysis algorithms are used to extract the basic data needed to generate a learning plan. For example, the frequency of study and performance trends for each subject are analyzed.

[0517] Step 4:

[0518] Server: Based on the extracted data, it utilizes a generative AI model to generate individually optimized learning plans. The generated learning plans include the allocation of study time and review content for each subject. The generative AI model is often implemented using Python libraries.

[0519] Step 5:

[0520] Server: Automatically generates study notes based on the study plan. These notes summarize important learning points. It also analyzes past exam data, models the question trends, and uses a generation AI model to generate predictive questions.

[0521] Step 6:

[0522] Server: Analyzes learners' past progress data and generates feedback to adjust their next learning plan. This feedback includes subjects and assignments that should be prioritized.

[0523] Step 7:

[0524] Terminal: Receives learning plans, notes, practice questions, and feedback sent from the server and displays them to the user. A GUI is used for display, providing a visual and intuitive interface. Learners use this information to plan and execute their next learning activities.

[0525] (Application Example 1)

[0526] Next, we will explain Application Example 1. In the following explanation, 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."

[0527] Conventional technologies for providing personalized learning support have struggled to reflect learners' needs in real time, particularly in analyzing past exam questions and automatically generating learning plans. Furthermore, effectively providing feedback based on learners' progress remains a challenge. Additionally, there is a lack of technologies that provide learning plans and feedback visually and interactively.

[0528] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0529] In this invention, the server includes means for receiving input information from learners, means for generating a learning plan optimized for the learner by analyzing the received input information, means for automatically generating important learning points based on the learning plan, and means for analyzing past exam data from the desired educational institution and modeling the question trends. This enables interactive and effective learning support that reflects the different needs of each learner in real time.

[0530] A "learner" is an individual who aims to acquire specific knowledge or skills.

[0531] "Input information" refers to data that learners provide to the system, including information about learning progress, learning content, and outcomes.

[0532] An "optimized learning plan" is a plan for guiding efficient learning activities that are tailored to the individual circumstances and goals of each learner.

[0533] A "plan generation means" is a component that has the function of analyzing the learner's input information and automatically creating an optimized learning plan.

[0534] An "automatic note generation method" is a component for organizing important learning points based on a learning plan and providing them in note format.

[0535] A "trend analysis tool" is a component that has the function of analyzing past question data and modeling the patterns and trends of those questions.

[0536] An "educational machine" is a machine with interactive features designed to support educational activities.

[0537] The system for realizing this invention has advanced data analysis and interactive functions for educational support. It primarily consists of a server, terminals, and educational equipment working together, with each element playing its individual role while forming the overall system.

[0538] The server functions as a centralized data receiving and processing unit. It receives input information sent from learners and performs analysis. The received information is evaluated using information analysis algorithms to generate an optimized learning plan for each learner. This plan includes the allocation of learning time and review points for specific concepts and areas. Subsequently, a generative AI model analyzes past exam data from the target educational institution to model future exam trends. This process analyzes past exam patterns and generates new predictive questions.

[0539] The terminal is a device operated by the learner, providing data input and communication with the server. The application on the terminal has an interface that allows the learner to input learning time and learning content. Furthermore, the input data is encrypted before being sent to the server. The terminal has a means of displaying learning plans and feedback provided by the server, providing learners with immediate feedback and information.

[0540] Educational machines are devices designed to facilitate learning support and provide physical interaction capabilities. These machines utilize speech recognition and natural language processing technologies to deliver an interactive learning experience to learners. The machines receive learning plans and predicted questions from a server and present them to learners in an interactive manner.

[0541] For example, if an elementary school student instructs a machine to "work on today's math assignment," the machine will present the most suitable problems and review topics for the day, both audibly and visually, based on results generated from the server. An example of a prompt to the server using a generative AI model would be, "Create basic math problems for elementary school students, based on their current learning progress. Design them to be easy for them to understand." This allows learners to receive continuous and optimized instruction.

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] Users input their study time, studied subjects, and evaluation results through a dedicated application on their device. This input data is encrypted and sent to the server. Encryption is performed to ensure the security of user data.

[0545] Step 2:

[0546] The server analyzes the received input data. The analysis algorithm identifies the learner's progress and strengths and weaknesses, and generates an optimal learning plan based on this. This plan includes recommended allocation of study time and areas to focus on.

[0547] Step 3:

[0548] The server automatically generates key learning points based on the learning plan. The generated notes are provided in a concise format, enabling users to acquire knowledge efficiently.

[0549] Step 4:

[0550] The server analyzes past exam data from the target educational institution. It models the question trends and generates predictive questions. This process uses a generative AI model and leverages prompts based on past question patterns. For example, a prompt might say, "Generate new predictive questions based on the past math question trends of your target school."

[0551] Step 5:

[0552] The server sends the generated study plan, notes on key points, and predicted questions to the user's device. This allows the user to instantly access this information and clearly define their next learning activity.

[0553] Step 6:

[0554] Users receive information via their devices and reflect it in the educational machine, allowing them to learn interactively. The educational machine uses speech recognition and natural language processing to respond to user conversations and questions, supporting their learning.

[0555] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0556] The present invention aims to provide a more effective learning experience by combining emotion recognition functionality with a system that individually optimizes learning support for learners. This system is implemented through interaction between a server, a terminal, and the user, and has the following characteristics.

[0557] Data entry and emotion recognition

[0558] User: First, learners input basic information such as study time, subjects studied, and test results into the device. Furthermore, emotional data is collected through devices that capture the user's voice and facial expressions.

[0559] Terminal: Temporarily stores basic and emotional data. Stored data is sent to the server using a communication protocol. Encryption is applied during transmission to ensure data security.

[0560] Creating and adjusting a learning plan

[0561] Server: Analyzes received data and generates a learning plan optimized for the learner. This plan generation incorporates an algorithm that takes the learner's emotional state into account. For example, if the server determines that the learner is tired, it will suggest lighter learning content that prioritizes review.

[0562] Server: After generating a learning plan, it extracts key learning points and automatically generates notes. These notes are designed to aid learners' understanding and enable effective review.

[0563] Providing trend analysis and prediction problems

[0564] Server: Analyzes data, including past exam questions from the target school, to model question trends. It also incorporates sentiment data to provide predictive questions in the least burdensome way possible for learners.

[0565] Progress evaluation and feedback

[0566] Server: Analyzes acquired training data and sentiment data together to evaluate learning progress. Based on the evaluation results, provides feedback to the learner and incorporates it into the next learning plan.

[0567] Enhancing the user experience

[0568] Terminal: Receives data sent from the server and presents the user with a learning plan, notes, and predicted questions. This allows the user to learn in a way that suits their current mental state and maintain a high level of motivation.

[0569] As a concrete example, let's consider a high school student studying mathematics at home. If this student says "I'm tired" using voice data, or if fatigue is detected from their facial expression, the system can reduce the amount of studying for that day and revise the schedule for the next day. This dynamic adjustment aims to maximize learning effectiveness and maintain motivation.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] User: Learners use a learning app to input study time, subjects, and test results. Simultaneously, they activate a device that records their emotions using a microphone and camera. This captures audio and facial data.

[0573] Step 2:

[0574] Device: Temporarily stores acquired training data and sentiment data, encrypts it, and prepares it for transmission to the server. At this stage, simple feedback is provided within the app to verify the accuracy of the data.

[0575] Step 3:

[0576] Terminal: Encrypts the saved data and sends it to the server. During this process, data is transmitted in real time, and the user is shown the transmission status.

[0577] Step 4:

[0578] Server: Receives data, stores it in a database, and performs necessary preprocessing for analysis. In this preparation stage, incomplete data and outliers are removed.

[0579] Step 5:

[0580] Server: Analyzes training data to generate a learning plan optimized for the learner. During this process, emotional data is used to adjust the plan. For example, if the learner is experiencing high stress levels, a less stressful learning pace will be set.

[0581] Step 6:

[0582] Server: Based on the learning plan, it automatically generates notes highlighting key learning points. This includes visual presentations based on the user's interests and level of focus.

[0583] Step 7:

[0584] Server: Extracts question trends from past question data and generates predicted questions at a difficulty level appropriate to the user's mental state. In particular, it optimizes learning effectiveness by providing questions that are neither too easy nor too difficult.

[0585] Step 8:

[0586] Server: Analyzes the feedback received and makes adjustments for the next learning cycle. This feedback includes encouraging and motivating messages tailored to the current emotional state.

[0587] Step 9:

[0588] Terminal: Displays learning plans, notes, practice questions, and feedback received from the server to the user. The user can access new plans and review the next steps through the interface.

[0589] (Example 2)

[0590] Next, we will describe Example 2. 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."

[0591] Conventional learning support systems have limitations in improving learning effectiveness and maintaining motivation because they do not adequately consider the emotional state and progress of individual learners during optimization. Furthermore, the security of received data is insufficient, and protecting privacy is a challenge. Additionally, there is a need to improve the quality of individualized learning due to insufficient analysis of question trends and the provision of necessary feedback.

[0592] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0593] In this invention, the server includes an information receiving means for receiving information from learners, a planning means for analyzing the received information and creating a learning plan suitable for the learner, and an emotion identification means for identifying the learner's emotional state and reflecting it in the learning plan. This makes it possible to optimize the learning plan in real time according to the individual state of the learner and to achieve high learning effectiveness while ensuring data security.

[0594] "Information receiving means" refers to a device or software equipped with the function of receiving input information from learners.

[0595] A "plan creation means" is a device or program that has the function of creating a learning plan suitable for the learner based on the information received.

[0596] "Note-taking means" refers to a device or program that has the function of automatically generating important learning points based on a learning plan.

[0597] A "trend analysis tool" is a device or software equipped with the function of analyzing past exam data and constructing a model of question trends.

[0598] "Problem creation means" refers to a device or program that has the function of generating predicted problems based on trend analysis.

[0599] A "progress evaluation tool" is a device or program that has the function of evaluating the learner's progress and providing appropriate feedback.

[0600] An "emotion identification tool" is a device or program that has the function of identifying a learner's emotional state and reflecting it in the learning plan.

[0601] "Encryption means" refers to a device or program that has the function of performing encryption processing to ensure the security of received information.

[0602] This invention is a system for individually optimizing support for learners, and consists of three components: a server, a terminal, and a user. The system is configured as follows:

[0603] Server: The server is equipped with a receiving mechanism for taking in information. This information includes learner input data and sentiment data, and a planning mechanism is used to analyze this data. This analysis uses a neural network implemented in Python, and a generative AI model operates. The server also models question trends based on past exam data and generates predicted questions as needed. Furthermore, it includes a progress evaluation mechanism that assesses the learner's progress and provides appropriate feedback. It also incorporates a sentiment recognition mechanism that identifies the learner's emotions and reflects them in the learning plan.

[0604] Terminal: The terminal is responsible for temporarily storing information collected from the user and sending it to the server. The HTTPS protocol is used for information transmission, and AES encryption technology is applied to ensure security. The terminal has a GUI that presents learning plans, notes, and problems sent from the server to the user.

[0605] User: Users input information related to their learning through their device. For example, when a user is studying mathematics, they might say "I'm tired" to collect audio data. This speech information and facial expression data are sent to the server, and an appropriate learning plan is adjusted. For instance, if the user feels tired, the learning content is adjusted to be lighter.

[0606] The system is optimized to help learners continue learning more effectively. A concrete example of a prompt is, "How can we optimize the learning plan based on the user's emotional data?" This allows the system to dynamically provide learning support tailored to the learner's state.

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] User: Learners input their study time, subjects studied, test results, and emotional data into the device. This input data includes voice and facial expression information. Specifically, the user's voice and facial expressions are recorded using a microphone and camera. This information is then converted into a digital format and stored on the device.

[0610] Step 2:

[0611] Terminal: The collected data is encrypted and prepared for transmission to the server. Specifically, AES encryption technology is used to ensure data security, and HTTPS is used as the communication protocol. Inputs include training data and sentiment data from users, and the output is an encrypted dataset.

[0612] Step 3:

[0613] Server: Analyzes received data and generates a learning plan suitable for the learner. The input is an encrypted dataset from the terminal, which is decrypted using AES encryption and analyzed by the planning mechanism. A generative AI model is used to calculate the optimal learning content, and the learning plan is provided as output. Specifically, a neural network operates to formulate a plan tailored to the learner's characteristics.

[0614] Step 4:

[0615] Server: Based on the learning plan, it extracts key points and automatically generates notes. The input is the learning plan, and the output is notes that can be used by the learner. Specifically, it uses a text generation function integrated into the plan creation mechanism, employing natural language processing (NLP) algorithms.

[0616] Step 5:

[0617] Server: Models question trends based on past exam data and creates predictive questions. The input is exam data, which is analyzed by a machine learning model. The output is a set of predictive questions tailored to the learner. Specifically, a trend analysis mechanism operates, using algorithms such as multilayer perceptrons to analyze trends.

[0618] Step 6:

[0619] Server: Based on training data and sentiment data, it evaluates learning progress and generates feedback. The input is the entire learner's data, which is analyzed using progress evaluation methods. The output is feedback to the learner generated in real time. Specifically, past learning history is queried from the server's database and progress is evaluated.

[0620] Step 7:

[0621] Terminal: Presents learning plans, notes, and problems obtained from the server to the user. The input is educational content provided by the server, which is output via a GUI. Specifically, information is presented graphically on the terminal's display, and the user uses this to progress through their learning.

[0622] (Application Example 2)

[0623] Next, we will explain application example 2. In the following explanation, 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."

[0624] Traditional learning support systems provided uniform learning plans without considering learners' emotions or fatigue levels, making it difficult to maintain learner motivation and maximize learning effectiveness. Furthermore, there was a need for a support system that could provide feedback at appropriate times and adjust the learning pace for each individual learner.

[0625] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0626] In this invention, the server includes an acquisition means for acquiring input data related to learning, a plan generation means for analyzing the acquired input data and emotional information to generate a learning plan optimized for the learner, and a sensing and adjustment means for sensing the learner's state and adjusting the learning pace. This makes it possible to provide an individually optimized learning experience that responds to the learner's emotional state.

[0627] "Acquisition means" refers to devices and methods used to collect input data related to learning.

[0628] A "plan generation means" is a device or method that analyzes collected data and emotional information to create a learning plan optimized for the learner.

[0629] "Automatic generation means" refers to a device or method that has the function of automatically generating important learning information based on the created learning plan.

[0630] "Analysis means" refers to devices and methods for analyzing past evaluation information and modeling learning trends.

[0631] "Information generation means" refers to devices or methods that have the function of generating predictive information based on modeled learning trends.

[0632] A "sensing and adjusting means" refers to a device or method for sensing the learner's state and appropriately adjusting the learning pace.

[0633] A "feedback provision means" refers to a device or method that has the function of evaluating the learner's progress and providing appropriate feedback.

[0634] The system for implementing this invention is a comprehensive learning support system that incorporates emotion recognition technology to individually optimize the learner's learning experience. The entire system operates primarily through user terminals, a server, and user interaction.

[0635] The user terminal is responsible for acquiring input data from learners. This data includes not only learning-related information such as study time and subjects, but also emotional information through facial expressions and voice data. For this purpose, devices such as cameras and microphones are used. This data is temporarily stored on the terminal, encrypted, and then securely transmitted to the server.

[0636] The server analyzes the received data and generates an optimal learning plan for the learner. This plan generation utilizes an emotion recognition model using Python, and can employ libraries such as TensorFlow and OpenCV. Furthermore, it models learning trends using past training data and generates new prediction problems. For this process, it is recommended to use data processing libraries such as Pandas and NumPy for data analysis.

[0637] Regarding learners' progress, the server evaluates the data and generates feedback through a feedback provision system. This feedback is transmitted to the learner via the user terminal, contributing to adjustments to the learning plan and maintenance of motivation.

[0638] For example, if a middle school student is studying for a history test at home, and the system detects fatigue through voice analysis, it will slow down the learning pace and prioritize less demanding content. This allows the learner to continue studying without undue stress.

[0639] As an example of a prompt, sending an instruction to the AI ​​model's interface such as, "Use audio data to detect learner fatigue and adjust learning appropriately," can simplify the system's operation.

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The device acquires input data from the learner. This input data includes study time, subjects studied, and emotional information through facial expressions and voice data. It operates using the camera and microphone and temporarily stores the data. This data is then encrypted and sent to the server in a secure manner.

[0643] Step 2:

[0644] The server analyzes the input data and emotional information received from the terminal. Using an emotion recognition model, it analyzes emotions from facial expressions and voice using TensorFlow and OpenCV. This allows the server to understand the learner's emotional state, and the analysis results are used to generate a learning plan in the next step.

[0645] Step 3:

[0646] The server generates a learning plan optimized for the learner based on the analysis results. Using Python, it creates more appropriate learning content and progress schedules, taking into account the analyzed sentiment data. The output is the specific learning content that the learner should work on.

[0647] Step 4:

[0648] The server analyzes past evaluation data and models learning trends. It uses Pandas and NumPy to format the data and perform trend analysis. Based on the analysis results, it generates a prediction problem and outputs that problem.

[0649] Step 5:

[0650] The server provides feedback based on the learning plan and predicted questions. It evaluates the learner's progress and creates feedback that should be incorporated into the next learning plan. The output feedback includes information about the learner's progress and areas for improvement.

[0651] Step 6:

[0652] The device displays the learning plan, predicted questions, and feedback sent from the server. This allows learners to check their learning progress and prepare for the next lesson. Specifically, it uses the information acquired by the user to advance the learning process and adjust the plan as needed.

[0653] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0654] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0655] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] As shown in Figure 7, the 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.

[0659] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0660] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0661] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0662] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0663] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0664] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0665] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0666] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0667] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0668] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0669] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] This invention provides a system for offering individually optimized learning support to learners, primarily through interactions between a server, a terminal, and the user. This document describes the main elements of the system and their functions, and details its capabilities with specific examples.

[0671] Data entry and reception from learners

[0672] User: Learners use a dedicated application to input data such as study time, subjects studied, and test results into their devices. This information serves as the basis for tracking learners' progress in real time and generating individualized learning plans.

[0673] Terminal: Stores the entered data and sends it to the server using a secure communication method.

[0674] Creating study plans and notes

[0675] Server: The server processes the received data using multiple analysis algorithms to generate a learning plan optimized for the learner. This plan includes the allocation of study time for each subject and the content to be reviewed. Furthermore, based on the learning plan, it automatically generates notes summarizing important learning points and provides them to the learner. These notes function as a tool to effectively support the user's learning.

[0676] Analysis of past exam questions and provision of predicted questions

[0677] Server: Analyzes past exam data from the student's target school and models the question trends. For example, if a history exam shows a tendency for many questions to be about names and events, the server generates new predictive questions based on that trend. This allows students to prepare for entrance exams in a more practical way.

[0678] Providing progress feedback

[0679] Server: Analyzes learners' cumulative data to analyze their progress and generates feedback to adjust their next learning plan. This feedback includes identifying subjects to prioritize next and highlighting areas of weakness.

[0680] Enhancing the user experience

[0681] Terminal: Provides learners with an interactive learning experience by instantly displaying learning plans, predicted questions, and feedback provided by the server. This allows learners to independently and effectively manage their daily learning progress.

[0682] For example, if a junior high school student wants to improve their English vocabulary, they can input their learning history into the system. The server will then analyze this data and prioritize incorporating a new vocabulary list and review questions into their plan for the following day. In this way, the present invention provides a learning environment tailored to the individual needs of each learner.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] User: Users input daily learning data, such as study time, subject information, and test results, into a dedicated app. This data serves as foundational information for evaluating learning progress and effectiveness.

[0686] Step 2:

[0687] Terminal: Temporarily stores the entered data. Once saving is complete, it encrypts the data and prepares it for secure transmission to the server.

[0688] Step 3:

[0689] Terminal: Sends data to the server via the internet. During transmission, the data is sent in an encrypted format to ensure data security.

[0690] Step 4:

[0691] Server: Stores received training data in a database and prepares it for analysis. Based on the received data, it evaluates the learner's current learning progress.

[0692] Step 5:

[0693] Server: Applies multiple analysis algorithms to generate a learning plan optimized for each individual learner. The analysis takes into account past learning history, test results, and the learner's goals.

[0694] Step 6:

[0695] Server: Based on the learning plan, it automatically extracts key learning points and provides them to the user in note format. These notes are used by learners to improve the efficiency of review and confirmation.

[0696] Step 7:

[0697] Server: Analyzes past exam data from the target school and models the question trends. Based on the analysis results, it generates predictive questions for use in the next step.

[0698] Step 8:

[0699] Server: Incorporates prediction problems generated based on the model into the user's learning plan and prepares to send them from the server to the terminal.

[0700] Step 9:

[0701] Terminal: Receives data sent from the server and presents the user with new learning plans, notes, and predicted questions. The user then uses this information to proceed with their learning.

[0702] Step 10:

[0703] Server: Analyzes the user's cumulative learning data and evaluates learning progress. Based on this evaluation, it generates feedback to help improve the next learning plan.

[0704] (Example 1)

[0705] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] In the field of education, there is a demand for providing individually optimized learning support tailored to each learner. Traditional educational support systems can only provide uniform learning plans and problems, and have the problem of not being able to adequately address the individual needs of learners. In addition, the monitoring of learning progress and the provision of feedback are insufficient, making it difficult to achieve efficient learning.

[0707] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0708] In this invention, the server includes receiving means for receiving input information from learners, planning means for analyzing the received information and generating a learning plan optimized for the learner, and generating means for using generation AI technology to generate predictive problems. This enables efficient and effective learning tailored to the individual progress and needs of each learner.

[0709] A "receiving means" is a component that has the function of taking in input information from learners into the system.

[0710] A "plan generation means" is a component that has the function of creating a learning plan optimized for the learner based on the received information.

[0711] The "automatic record generation means" is a component that has the function of automatically constructing important learning items based on the learning plan.

[0712] A "trend analysis tool" is a component that has the function of analyzing past exam question information and modeling the trends in the questions asked.

[0713] A "problem generation means" is a component that has the function of creating predicted problems based on modeled question trends.

[0714] A "progress feedback mechanism" is a component that has the function of analyzing the learner's progress and providing feedback based on the results.

[0715] A "generation means" is a component that has the function of creating predictive problems and learning plans using generative AI technology.

[0716] "Display means" refers to a component that has the function of visualizing feedback information or learning plans.

[0717] The educational support system of this invention is designed to provide learners with individually optimized learning plans and to support their progress. This system primarily functions through three elements: a server, a terminal, and a user.

[0718] The server receives input information sent by learners and stores it in a database. This information includes study time, subjects studied, and test results. Based on this information, the server uses analytical algorithms and generative AI models to generate a learning plan optimized for the learner. Programming languages ​​such as Python and R are used for data analysis during this process.

[0719] The server further analyzes past question data and models question trends to generate predicted questions. This utilizes a generative AI model incorporating natural language processing (NLP) technology. This allows learners to engage in practical problem-solving exercises.

[0720] The terminal is responsible for receiving learning plans and practice questions provided by the server and displaying them to the learner. The terminal uses encryption technologies such as SSL / TLS to securely store received data. Furthermore, it provides feedback information and progress status visually through a graphical user interface, presenting it in a way that is easy for learners to understand.

[0721] Users input their learning information using a dedicated app on their device. These input operations are performed via a touchscreen or keyboard, allowing for intuitive operation. For example, if a middle school student wants to improve their English vocabulary, they input their learning history into the system, and the server analyzes that data to generate a learning plan that prioritizes new vocabulary lists and review questions.

[0722] An example of a prompt message would be sending a request to the AI ​​model to "analyze past history exam data and generate predictive questions that will be useful for the next exam." In this way, the system functions as a tool to support learners in efficient and effective learning.

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] User: Learners use a dedicated application on their device to input information such as study time, subjects studied, and test results. Input is done using the touchscreen or keyboard. The entered data is temporarily stored in a database on the device.

[0726] Step 2:

[0727] Terminal: The entered data is securely transmitted to the server using the SSL / TLS protocol. At this time, the data is converted to JSON format and configured as a packet. An acknowledgment is used to confirm that the converted data has been transmitted correctly.

[0728] Step 3:

[0729] Server: Receives data and stores it in a database system. The database is structured using SQL. Next, data analysis algorithms are used to extract the basic data needed to generate a learning plan. For example, the frequency of study and performance trends for each subject are analyzed.

[0730] Step 4:

[0731] Server: Based on the extracted data, it utilizes a generative AI model to generate individually optimized learning plans. The generated learning plans include the allocation of study time and review content for each subject. The generative AI model is often implemented using Python libraries.

[0732] Step 5:

[0733] Server: Automatically generates study notes based on the study plan. These notes summarize important learning points. It also analyzes past exam data, models the question trends, and uses a generation AI model to generate predictive questions.

[0734] Step 6:

[0735] Server: Analyzes learners' past progress data and generates feedback to adjust their next learning plan. This feedback includes subjects and assignments that should be prioritized.

[0736] Step 7:

[0737] Terminal: Receives learning plans, notes, practice questions, and feedback sent from the server and displays them to the user. A GUI is used for display, providing a visual and intuitive interface. Learners use this information to plan and execute their next learning activities.

[0738] (Application Example 1)

[0739] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] Conventional technologies for providing personalized learning support have struggled to reflect learners' needs in real time, particularly in analyzing past exam questions and automatically generating learning plans. Furthermore, effectively providing feedback based on learners' progress remains a challenge. Additionally, there is a lack of technologies that provide learning plans and feedback visually and interactively.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0742] In this invention, the server includes means for receiving input information from learners, means for generating a learning plan optimized for the learner by analyzing the received input information, means for automatically generating important learning points based on the learning plan, and means for analyzing past exam data from the desired educational institution and modeling the question trends. This enables interactive and effective learning support that reflects the different needs of each learner in real time.

[0743] A "learner" is an individual who aims to acquire specific knowledge or skills.

[0744] "Input information" refers to data that learners provide to the system, including information about learning progress, learning content, and outcomes.

[0745] An "optimized learning plan" is a plan for guiding efficient learning activities that are tailored to the individual circumstances and goals of each learner.

[0746] A "plan generation means" is a component that has the function of analyzing the learner's input information and automatically creating an optimized learning plan.

[0747] An "automatic note generation method" is a component for organizing important learning points based on a learning plan and providing them in note format.

[0748] A "trend analysis tool" is a component that has the function of analyzing past question data and modeling the patterns and trends of those questions.

[0749] An "educational machine" is a machine with interactive features designed to support educational activities.

[0750] The system for realizing this invention has advanced data analysis and interactive functions for educational support. It primarily consists of a server, terminals, and educational equipment working together, with each element playing its individual role while forming the overall system.

[0751] The server functions as a centralized data receiving and processing unit. It receives input information sent from learners and performs analysis. The received information is evaluated using information analysis algorithms to generate an optimized learning plan for each learner. This plan includes the allocation of learning time and review points for specific concepts and areas. Subsequently, a generative AI model analyzes past exam data from the target educational institution to model future exam trends. This process analyzes past exam patterns and generates new predictive questions.

[0752] The terminal is a device operated by the learner, providing data input and communication with the server. The application on the terminal has an interface that allows the learner to input learning time and learning content. Furthermore, the input data is encrypted before being sent to the server. The terminal has a means of displaying learning plans and feedback provided by the server, providing learners with immediate feedback and information.

[0753] Educational machines are devices designed to facilitate learning support and provide physical interaction capabilities. These machines utilize speech recognition and natural language processing technologies to deliver an interactive learning experience to learners. The machines receive learning plans and predicted questions from a server and present them to learners in an interactive manner.

[0754] For example, if an elementary school student instructs a machine to "work on today's math assignment," the machine will present the most suitable problems and review topics for the day, both audibly and visually, based on results generated from the server. An example of a prompt to the server using a generative AI model would be, "Create basic math problems for elementary school students, based on their current learning progress. Design them to be easy for them to understand." This allows learners to receive continuous and optimized instruction.

[0755] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0756] Step 1:

[0757] Users input their study time, studied subjects, and evaluation results through a dedicated application on their device. This input data is encrypted and sent to the server. Encryption is performed to ensure the security of user data.

[0758] Step 2:

[0759] The server analyzes the received input data. The analysis algorithm identifies the learner's progress and strengths and weaknesses, and generates an optimal learning plan based on this. This plan includes recommended allocation of study time and areas to focus on.

[0760] Step 3:

[0761] The server automatically generates key learning points based on the learning plan. The generated notes are provided in a concise format, enabling users to acquire knowledge efficiently.

[0762] Step 4:

[0763] The server analyzes past exam data from the target educational institution. It models the question trends and generates predictive questions. This process uses a generative AI model and leverages prompts based on past question patterns. For example, a prompt might say, "Generate new predictive questions based on the past math question trends of your target school."

[0764] Step 5:

[0765] The server sends the generated study plan, notes on key points, and predicted questions to the user's device. This allows the user to instantly access this information and clearly define their next learning activity.

[0766] Step 6:

[0767] Users receive information via their devices and reflect it in the educational machine, allowing them to learn interactively. The educational machine uses speech recognition and natural language processing to respond to user conversations and questions, supporting their learning.

[0768] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0769] The present invention aims to provide a more effective learning experience by combining emotion recognition functionality with a system that individually optimizes learning support for learners. This system is implemented through interaction between a server, a terminal, and the user, and has the following characteristics.

[0770] Data entry and emotion recognition

[0771] User: First, learners input basic information such as study time, subjects studied, and test results into the device. Furthermore, emotional data is collected through devices that capture the user's voice and facial expressions.

[0772] Terminal: Temporarily stores basic and emotional data. Stored data is sent to the server using a communication protocol. Encryption is applied during transmission to ensure data security.

[0773] Creating and adjusting a learning plan

[0774] Server: Analyzes received data and generates a learning plan optimized for the learner. This plan generation incorporates an algorithm that takes the learner's emotional state into account. For example, if the server determines that the learner is tired, it will suggest lighter learning content that prioritizes review.

[0775] Server: After generating a learning plan, it extracts key learning points and automatically generates notes. These notes are designed to aid learners' understanding and enable effective review.

[0776] Providing trend analysis and prediction problems

[0777] Server: Analyzes data, including past exam questions from the target school, to model question trends. It also incorporates sentiment data to provide predictive questions in the least burdensome way possible for learners.

[0778] Progress evaluation and feedback

[0779] Server: Analyzes acquired training data and sentiment data together to evaluate learning progress. Based on the evaluation results, provides feedback to the learner and incorporates it into the next learning plan.

[0780] Enhancing the user experience

[0781] Terminal: Receives data sent from the server and presents the user with a learning plan, notes, and predicted questions. This allows the user to learn in a way that suits their current mental state and maintain a high level of motivation.

[0782] As a concrete example, let's consider a high school student studying mathematics at home. If this student says "I'm tired" using voice data, or if fatigue is detected from their facial expression, the system can reduce the amount of studying for that day and revise the schedule for the next day. This dynamic adjustment aims to maximize learning effectiveness and maintain motivation.

[0783] The following describes the processing flow.

[0784] Step 1:

[0785] User: Learners use a learning app to input study time, subjects, and test results. Simultaneously, they activate a device that records their emotions using a microphone and camera. This captures audio and facial data.

[0786] Step 2:

[0787] Device: Temporarily stores acquired training data and sentiment data, encrypts it, and prepares it for transmission to the server. At this stage, simple feedback is provided within the app to verify the accuracy of the data.

[0788] Step 3:

[0789] Terminal: Encrypts the saved data and sends it to the server. During this process, data is transmitted in real time, and the user is shown the transmission status.

[0790] Step 4:

[0791] Server: Receives data, stores it in a database, and performs necessary preprocessing for analysis. In this preparation stage, incomplete data and outliers are removed.

[0792] Step 5:

[0793] Server: Analyzes training data to generate a learning plan optimized for the learner. During this process, emotional data is used to adjust the plan. For example, if the learner is experiencing high stress levels, a less stressful learning pace will be set.

[0794] Step 6:

[0795] Server: Based on the learning plan, it automatically generates notes highlighting key learning points. This includes visual presentations based on the user's interests and level of focus.

[0796] Step 7:

[0797] Server: Extracts question trends from past question data and generates predicted questions at a difficulty level appropriate to the user's mental state. In particular, it optimizes learning effectiveness by providing questions that are neither too easy nor too difficult.

[0798] Step 8:

[0799] Server: Analyzes the feedback received and makes adjustments for the next learning cycle. This feedback includes encouraging and motivating messages tailored to the current emotional state.

[0800] Step 9:

[0801] Terminal: Displays learning plans, notes, practice questions, and feedback received from the server to the user. The user can access new plans and review the next steps through the interface.

[0802] (Example 2)

[0803] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0804] Conventional learning support systems have limitations in improving learning effectiveness and maintaining motivation because they do not adequately consider the emotional state and progress of individual learners during optimization. Furthermore, the security of received data is insufficient, and protecting privacy is a challenge. Additionally, there is a need to improve the quality of individualized learning due to insufficient analysis of question trends and the provision of necessary feedback.

[0805] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0806] In this invention, the server includes an information receiving means for receiving information from learners, a planning means for analyzing the received information and creating a learning plan suitable for the learner, and an emotion identification means for identifying the learner's emotional state and reflecting it in the learning plan. This makes it possible to optimize the learning plan in real time according to the individual state of the learner and to achieve high learning effectiveness while ensuring data security.

[0807] "Information receiving means" refers to a device or software equipped with the function of receiving input information from learners.

[0808] A "plan creation means" is a device or program that has the function of creating a learning plan suitable for the learner based on the information received.

[0809] "Note-taking means" refers to a device or program that has the function of automatically generating important learning points based on a learning plan.

[0810] A "trend analysis tool" is a device or software equipped with the function of analyzing past exam data and constructing a model of question trends.

[0811] "Problem creation means" refers to a device or program that has the function of generating predicted problems based on trend analysis.

[0812] A "progress evaluation tool" is a device or program that has the function of evaluating the learner's progress and providing appropriate feedback.

[0813] An "emotion identification tool" is a device or program that has the function of identifying a learner's emotional state and reflecting it in the learning plan.

[0814] "Encryption means" refers to a device or program that has the function of performing encryption processing to ensure the security of received information.

[0815] This invention is a system for individually optimizing support for learners, and consists of three components: a server, a terminal, and a user. The system is configured as follows:

[0816] Server: The server is equipped with a receiving mechanism for taking in information. This information includes learner input data and sentiment data, and a planning mechanism is used to analyze this data. This analysis uses a neural network implemented in Python, and a generative AI model operates. The server also models question trends based on past exam data and generates predicted questions as needed. Furthermore, it includes a progress evaluation mechanism that assesses the learner's progress and provides appropriate feedback. It also incorporates a sentiment recognition mechanism that identifies the learner's emotions and reflects them in the learning plan.

[0817] Terminal: The terminal is responsible for temporarily storing information collected from the user and sending it to the server. The HTTPS protocol is used for information transmission, and AES encryption technology is applied to ensure security. The terminal has a GUI that presents learning plans, notes, and problems sent from the server to the user.

[0818] User: Users input information related to their learning through their device. For example, when a user is studying mathematics, they might say "I'm tired" to collect audio data. This speech information and facial expression data are sent to the server, and an appropriate learning plan is adjusted. For instance, if the user feels tired, the learning content is adjusted to be lighter.

[0819] The system is optimized to help learners continue learning more effectively. A concrete example of a prompt is, "How can we optimize the learning plan based on the user's emotional data?" This allows the system to dynamically provide learning support tailored to the learner's state.

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] User: Learners input their study time, subjects studied, test results, and emotional data into the device. This input data includes voice and facial expression information. Specifically, the user's voice and facial expressions are recorded using a microphone and camera. This information is then converted into a digital format and stored on the device.

[0823] Step 2:

[0824] Terminal: The collected data is encrypted and prepared for transmission to the server. Specifically, AES encryption technology is used to ensure data security, and HTTPS is used as the communication protocol. Inputs include training data and sentiment data from users, and the output is an encrypted dataset.

[0825] Step 3:

[0826] Server: Analyzes received data and generates a learning plan suitable for the learner. The input is an encrypted dataset from the terminal, which is decrypted using AES encryption and analyzed by the planning mechanism. A generative AI model is used to calculate the optimal learning content, and the learning plan is provided as output. Specifically, a neural network operates to formulate a plan tailored to the learner's characteristics.

[0827] Step 4:

[0828] Server: Based on the learning plan, it extracts key points and automatically generates notes. The input is the learning plan, and the output is notes that can be used by the learner. Specifically, it uses a text generation function integrated into the plan creation mechanism, employing natural language processing (NLP) algorithms.

[0829] Step 5:

[0830] Server: Models question trends based on past exam data and creates predictive questions. The input is exam data, which is analyzed by a machine learning model. The output is a set of predictive questions tailored to the learner. Specifically, a trend analysis mechanism operates, using algorithms such as multilayer perceptrons to analyze trends.

[0831] Step 6:

[0832] Server: Based on training data and sentiment data, it evaluates learning progress and generates feedback. The input is the entire learner's data, which is analyzed using progress evaluation methods. The output is feedback to the learner generated in real time. Specifically, past learning history is queried from the server's database and progress is evaluated.

[0833] Step 7:

[0834] Terminal: Presents learning plans, notes, and problems obtained from the server to the user. The input is educational content provided by the server, which is output via a GUI. Specifically, information is presented graphically on the terminal's display, and the user uses this to progress through their learning.

[0835] (Application Example 2)

[0836] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0837] Traditional learning support systems provided uniform learning plans without considering learners' emotions or fatigue levels, making it difficult to maintain learner motivation and maximize learning effectiveness. Furthermore, there was a need for a support system that could provide feedback at appropriate times and adjust the learning pace for each individual learner.

[0838] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0839] In this invention, the server includes an acquisition means for acquiring input data related to learning, a plan generation means for analyzing the acquired input data and emotional information to generate a learning plan optimized for the learner, and a sensing and adjustment means for sensing the learner's state and adjusting the learning pace. This makes it possible to provide an individually optimized learning experience that responds to the learner's emotional state.

[0840] "Acquisition means" refers to devices and methods used to collect input data related to learning.

[0841] A "plan generation means" is a device or method that analyzes collected data and emotional information to create a learning plan optimized for the learner.

[0842] "Automatic generation means" refers to a device or method that has the function of automatically generating important learning information based on the created learning plan.

[0843] "Analysis means" refers to devices and methods for analyzing past evaluation information and modeling learning trends.

[0844] "Information generation means" refers to devices or methods that have the function of generating predictive information based on modeled learning trends.

[0845] A "sensing and adjusting means" refers to a device or method for sensing the learner's state and appropriately adjusting the learning pace.

[0846] A "feedback provision means" refers to a device or method that has the function of evaluating the learner's progress and providing appropriate feedback.

[0847] The system for implementing this invention is a comprehensive learning support system that incorporates emotion recognition technology to individually optimize the learner's learning experience. The entire system operates primarily through user terminals, a server, and user interaction.

[0848] The user terminal is responsible for acquiring input data from learners. This data includes not only learning-related information such as study time and subjects, but also emotional information through facial expressions and voice data. For this purpose, devices such as cameras and microphones are used. This data is temporarily stored on the terminal, encrypted, and then securely transmitted to the server.

[0849] The server analyzes the received data and generates an optimal learning plan for the learner. This plan generation utilizes an emotion recognition model using Python, and can employ libraries such as TensorFlow and OpenCV. Furthermore, it models learning trends using past training data and generates new prediction problems. For this process, it is recommended to use data processing libraries such as Pandas and NumPy for data analysis.

[0850] Regarding learners' progress, the server evaluates the data and generates feedback through a feedback provision system. This feedback is transmitted to the learner via the user terminal, contributing to adjustments to the learning plan and maintenance of motivation.

[0851] For example, if a middle school student is studying for a history test at home, and the system detects fatigue through voice analysis, it will slow down the learning pace and prioritize less demanding content. This allows the learner to continue studying without undue stress.

[0852] As an example of a prompt, sending an instruction to the AI ​​model's interface such as, "Use audio data to detect learner fatigue and adjust learning appropriately," can simplify the system's operation.

[0853] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0854] Step 1:

[0855] The device acquires input data from the learner. This input data includes study time, subjects studied, and emotional information through facial expressions and voice data. It operates using the camera and microphone and temporarily stores the data. This data is then encrypted and sent to the server in a secure manner.

[0856] Step 2:

[0857] The server analyzes the input data and emotional information received from the terminal. Using an emotion recognition model, it analyzes emotions from facial expressions and voice using TensorFlow and OpenCV. This allows the server to understand the learner's emotional state, and the analysis results are used to generate a learning plan in the next step.

[0858] Step 3:

[0859] The server generates a learning plan optimized for the learner based on the analysis results. Using Python, it creates more appropriate learning content and progress schedules, taking into account the analyzed sentiment data. The output is the specific learning content that the learner should work on.

[0860] Step 4:

[0861] The server analyzes past evaluation data and models learning trends. It uses Pandas and NumPy to format the data and perform trend analysis. Based on the analysis results, it generates a prediction problem and outputs that problem.

[0862] Step 5:

[0863] The server provides feedback based on the learning plan and predicted questions. It evaluates the learner's progress and creates feedback that should be incorporated into the next learning plan. The output feedback includes information about the learner's progress and areas for improvement.

[0864] Step 6:

[0865] The device displays the learning plan, predicted questions, and feedback sent from the server. This allows learners to check their learning progress and prepare for the next lesson. Specifically, it uses the information acquired by the user to advance the learning process and adjust the plan as needed.

[0866] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0867] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0868] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0869] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0870] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0871] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0872] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0873] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0874] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0875] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0876] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0877] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0878] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0879] 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.

[0880] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0881] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0882] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0883] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0884] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0885] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0886] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0887] The following is further disclosed regarding the embodiments described above.

[0888] (Claim 1)

[0889] A receiving means for receiving input data from learners,

[0890] A plan generation means that analyzes received input data and generates a learning plan optimized for the learner,

[0891] An automated note generation method that automatically generates important learning points based on a learning plan,

[0892] A trend analysis method that analyzes past exam data of the target school and models the trends in the types of questions asked,

[0893] A question generation method that generates predictive questions based on modeled question trends,

[0894] A progress feedback system that analyzes learners' progress and provides feedback,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, wherein the learner's input data includes study time, subjects studied, and test results.

[0898] (Claim 3)

[0899] The system according to claim 1, wherein encryption processing is performed when the input data is received.

[0900] "Example 1"

[0901] (Claim 1)

[0902] A receiving means for receiving input information from learners,

[0903] A plan generation means that analyzes received input information and generates a learning plan optimized for the learner,

[0904] An automatic record generation means that automatically generates important learning items based on the learning plan,

[0905] A trend analysis method that analyzes past exam information of the desired educational institution and models the trends in the types of questions asked,

[0906] A question generation method that generates predicted questions based on modeled question trends,

[0907] A progress feedback system that analyzes learners' progress and provides feedback,

[0908] A generation method that uses generative AI technology to generate prediction problems,

[0909] A display means for visualizing feedback information,

[0910] An educational support system that includes this.

[0911] (Claim 2)

[0912] The educational support system according to claim 1, wherein the learner's input information includes learning time, subjects studied, and evaluation results.

[0913] (Claim 3)

[0914] The educational support system according to claim 1, wherein encryption processing is performed when receiving input information.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] A means of receiving input information from learners,

[0918] A plan generation means that analyzes received input information and generates a learning plan optimized for the learner,

[0919] An automated note generation method that automatically generates important learning points based on a learning plan,

[0920] A trend analysis method that analyzes past exam data of the desired educational institution and models the trends in the types of questions asked,

[0921] A question generation method that generates predictive questions based on modeled question trends,

[0922] A progress feedback system that analyzes learners' progress and provides feedback,

[0923] A means of providing learning plans and feedback interactively through educational machines,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, wherein the learner's input information includes activity time, studied subject area, and evaluation result.

[0927] (Claim 3)

[0928] The system according to claim 1, wherein encoding processing is performed when input information is received.

[0929] "Example 2 of combining an emotion engine"

[0930] (Claim 1)

[0931] Information receiving means for taking in information from learners,

[0932] A planning tool that analyzes the information it has gathered and creates a learning plan suitable for the learner,

[0933] A note-taking method that automatically generates important learning points according to the learning plan,

[0934] A trend analysis method that analyzes past exam data to model the trends in questions,

[0935] A question creation method that generates predicted questions based on modeled question trends,

[0936] A progress assessment tool that evaluates learners' progress and provides feedback,

[0937] An emotion identification method that identifies the emotional state of learners and reflects it in the learning plan,

[0938] Encryption means to ensure the security of received information,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, wherein learner information includes time spent studying, subjects studied, evaluation results, and sentiment data.

[0942] (Claim 3)

[0943] The system according to claim 1, wherein encryption processing is performed when information is taken in and when it is transmitted.

[0944] "Application example 2 when combining with an emotional engine"

[0945] (Claim 1)

[0946] A means of acquiring input data related to learning,

[0947] A plan generation means that analyzes acquired input data and emotional information to generate a learning plan optimized for the learner,

[0948] An automated generation means that automatically generates important learning information based on a learning plan,

[0949] An analytical method that analyzes past evaluation information and models learning trends,

[0950] Information generation means that generates predictive information based on modeled learning trends,

[0951] A sensing and adjusting means that senses the learner's state and adjusts the learning pace,

[0952] A feedback provision method that evaluates learners' progress and provides feedback,

[0953] A system that includes this.

[0954] (Claim 2)

[0955] The system according to claim 1, wherein emotional information includes voice and facial expression data.

[0956] (Claim 3)

[0957] The system according to claim 1, wherein protection processing is performed when data is acquired. [Explanation of symbols]

[0958] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A receiving means for receiving input data from learners, A plan generation means that analyzes received input data and generates a learning plan optimized for the learner, An automated note generation method that automatically generates important learning points based on a learning plan, A trend analysis method that analyzes past exam data of the target school and models the trends in the types of questions asked, A question generation method that generates predictive questions based on modeled question trends, A progress feedback system that analyzes learners' progress and provides feedback, A system that includes this.

2. The system according to claim 1, wherein the learner's input data includes study time, subjects studied, and test results.

3. The system according to claim 1, wherein encryption processing is performed when the input data is received.

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