system

Abstract

We provide the system. [Solution] A computer means for generating a learning plan based on the user's objectives and schedule, A computer means for managing progress according to a generated learning plan, A computer means for generating tests to periodically evaluate the user's level of understanding, A computer means that provides feedback based on evaluation results, A computer system that provides community functions enabling information sharing and question-and-answer sessions among users, 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In online learning, it is difficult for learners to manage themselves, maintain motivation, and grasp the understanding level of learning content. In addition, there is a lack of communication among learners, and they may fall into a situation of learning loneliness and difficulty in asking questions, resulting in a problem of reduced learning efficiency. To solve these problems, support optimized for each learner is required.

Means for Solving the Problems

[0005] This invention provides a computer system that generates learning plans tailored to the user's objectives and schedule, and manages their progress. It also addresses individual learning needs by generating periodic tests to assess the user's understanding and providing feedback based on the evaluation results. Furthermore, it includes a community function to facilitate information sharing and question-and-answer sessions among users, reducing feelings of isolation during learning and promoting peer learning. In addition, it features a display mechanism that visually shows learning progress and a message generation mechanism that sends encouragement and reminders at appropriate times, enabling efficient and effective support in online learning.

[0006] "User" refers to an individual or group that uses an online learning system to study.

[0007] "Purpose" refers to the specific results or goals that a user aims to achieve when using an online learning system.

[0008] "Schedule" refers to the time plan set out for users to progress through their learning using the online learning system.

[0009] A "learning plan" refers to a specific learning schedule built based on the user's goals and schedule.

[0010] "Progress management" refers to the process of tracking and managing the user's progress in line with their learning plan.

[0011] "Computer means" refers to hardware or software components designed to perform a specific function.

[0012] A "test to assess comprehension" refers to an exam or quiz conducted to measure the depth of a user's understanding of the learning material.

[0013] "Feedback" refers to advice and suggestions for improvement given based on the results of an assessment of the user's understanding.

[0014] "Information sharing" refers to the process of exchanging knowledge, data, or messages among users.

[0015] "Question and answer" refers to the process in which a user asks a question to resolve a doubt and an answer is provided in response.

[0016] "Community function" refers to a platform for users to communicate and share information with each other.

[0017] "Display means" refers to a device or function for providing visual information to a user.

[0018] "Message generation means" refers to a system or function designed to send specific information or notifications to a user.

Brief Description of the Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0022] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] 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."

[0027] [First Embodiment]

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

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0034] 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.

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

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is a system that supports users' online learning and is primarily executed via a server and a terminal. The server generates a learning plan based on the user's goals and schedule. This plan is optimized to the individual user's needs and delivered to the terminal as a specific learning schedule.

[0041] As the learning plan is executed, the server periodically generates tests to assess understanding. When the user takes these tests, feedback is generated based on the results. This feedback is integrated into the user's learning plan, optimizing the individual learning experience while adjusting progress.

[0042] Furthermore, the server provides community features to facilitate information sharing and question-and-answer sessions among users. Users can access the community through their devices and interact with other learners. This reduces feelings of isolation during learning and promotes peer learning.

[0043] The server also sends data to the device to visually display the user's learning progress. The device receives this data and helps maintain the user's motivation by showing them their level of achievement. Furthermore, the server generates encouraging and reminder messages for the user at appropriate times and notifies them through the device.

[0044] For example, if a user wants to prepare for an English exam, the system will create a study plan considering the user's target exam date and the required study time. The plan will include daily study tasks, periodic mini-tests, and feedback to check progress. The user can follow this plan and progress while receiving notifications from their device. At the same time, if questions arise during their studies, they can ask other learners through the community and deepen their understanding by receiving answers.

[0045] As a result, users can achieve personalized online learning tailored to their own pace and receive efficient and effective support to achieve their goals.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user logs into their device and enters their learning objectives, goals they want to achieve, and available time.

[0049] Step 2:

[0050] The terminal sends the entered user information to the server.

[0051] Step 3:

[0052] The server queries an internal database and analyzes past learning data and trends to generate an optimal learning plan based on the user's learning objectives and schedule.

[0053] Step 4:

[0054] The server creates an optimized learning plan and sends it to the terminal.

[0055] Step 5:

[0056] The device displays the user's learning plan and notifies them of daily learning tasks.

[0057] Step 6:

[0058] The server periodically generates quizzes and tests to assess the user's understanding and sends that information to the terminal.

[0059] Step 7:

[0060] The user takes a test presented on their device and enters their answers.

[0061] Step 8:

[0062] The device sends the user's answer results to the server.

[0063] Step 9:

[0064] The server analyzes the answer results, evaluates the user's level of understanding, and generates feedback.

[0065] Step 10:

[0066] The device notifies the user of feedback received from the server, displaying areas for improvement and the next learning task.

[0067] Step 11:

[0068] Users access the learning community through their devices to ask questions and exchange information.

[0069] Step 12:

[0070] The server manages user interaction data and provides terminals with statistics and information to facilitate peer learning.

[0071] Step 13:

[0072] The server evaluates the user's progress, generates visual data, and sends it to the terminal.

[0073] Step 14:

[0074] The device displays graphs and dashboards that visually show the user's learning progress and achievements.

[0075] Step 15:

[0076] The server will generate encouraging and reminder messages for the user as needed and notify their device.

[0077] (Example 1)

[0078] 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."

[0079] In online learning, for individual learners to efficiently and effectively achieve their goals, they need customized learning plans, appropriate assessment of understanding, progress-based feedback, a platform for information sharing, and mechanisms to support the continuity of learning. However, traditional methods have fragmented these elements, making it difficult for learners to receive consistent support.

[0080] 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.

[0081] In this invention, the server includes computation means for generating an educational plan based on the user's goals and schedule, computation means for monitoring progress according to the generated educational plan, and computation means for creating tests to periodically evaluate the user's level of understanding. This enables the provision of personalized learning support.

[0082] A "user" is an individual or group that uses the system for the purpose of online learning.

[0083] A "goal" is the learning content or skill level that a user hopes to achieve through their learning process.

[0084] "Schedule" refers to scheduling information that indicates the time or period that a user allocates to learning.

[0085] An "educational plan" is a teaching guide that outlines how to proceed with learning, optimized to the user's goals and schedule.

[0086] "Computational means" refers to a method consisting of a computer or program used to perform a specific process or function.

[0087] "Progress" is an indicator that shows the degree of achievement a user has made in the process of learning according to their educational plan.

[0088] "Monitoring" is the act of tracking progress according to the plan and making adjustments in real time as needed.

[0089] "Testing" refers to an evaluation method used to measure a user's level of understanding, and is typically conducted in the form of a test or quiz.

[0090] "Collaboration features" refer to online communication methods designed to facilitate information sharing and question-and-answer sessions among users.

[0091] "Visually displaying" refers to showing learning data, such as progress and achievement levels, in a way that users can intuitively understand.

[0092] A "message generation method" is a function that creates messages to provide users with notifications and encouragement at the appropriate time.

[0093] This invention is a system that supports users' online learning and is implemented via a server and a terminal, which are its main components.

[0094] The server uses a generative AI model to generate personalized learning plans based on the goals and schedules provided by the user. This generative AI model is used to design optimal learning content that aligns with the user's specific needs and schedule.

[0095] The server also monitors progress according to the plan and creates tests to periodically assess the user's understanding. Based on the results of these tests, the server generates feedback and incorporates it into the plan to optimize the user's learning experience.

[0096] The device receives educational plans and feedback sent from the server and presents them to the user in an intuitively understandable format. The device also visually displays learning progress and provides timely reminders and encouraging messages to maintain user motivation.

[0097] Users can progress through their learning by following educational plans provided from the server via their devices. Furthermore, users can deepen their learning by sharing information with other users and asking questions using collaborative features.

[0098] For example, if a user wants to prepare for an English exam, the server creates a study plan working backward from the exam date, incorporating daily learning tasks and periodic checks to assess understanding. Following this plan, the user can efficiently progress through their studies while receiving notifications on their device. If questions arise during learning, they can ask other learners through the collaborative function and receive answers.

[0099] Example of a prompt:

[0100] "Please create a three-month study plan to prepare for the next English exam. I would like to allocate one hour of study time per day and include a comprehension test once a week."

[0101] The above describes specific embodiments for carrying out this invention.

[0102] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0103] Step 1:

[0104] Users enter necessary information such as learning goals and schedules through an online platform. This sends the user's learning needs to the server. Specifically, users enter the exam date, desired study time, and the conditions necessary to achieve their goals on a web form.

[0105] Step 2:

[0106] The server inputs information received from the user into a generating AI model to create a personalized learning plan. This input includes learning objectives, exam dates, and available study time. Based on this data, the AI ​​designs an optimal learning schedule and delivers it to the user as a plan. The resulting learning plan includes daily tasks and scheduled periodic check-ups.

[0107] Step 3:

[0108] The server sends the generated learning plan to the user's device. This allows the user to begin learning based on the plan. Specifically, the server sends the plan data to the device, which then displays it visually in a calendar app or a dedicated app.

[0109] Step 4:

[0110] Users progress through their learning according to the educational plan using their devices and report their progress to the server. Questions and progress that arise during learning are sent to the server and recorded. Specifically, the system checks the user's progress after they complete a learning task and adjusts the schedule as needed.

[0111] Step 5:

[0112] The server periodically generates tests based on progress data. The input data is the user's progress, and the server designs tests to assess understanding based on this data. The output tests function as tools to measure the user's level of understanding.

[0113] Step 6:

[0114] The user takes a comprehension test presented on their device and sends the results to the server. Specifically, this involves the user completing the test and uploading the results to the server.

[0115] Step 7:

[0116] The server evaluates the received test results and generates feedback on the user's learning progress. This feedback is then sent back to the user's device to help improve their learning plan. Specifically, the server uses an analysis algorithm to evaluate the results and constructs feedback based on that analysis.

[0117] Step 8:

[0118] Users adjust their learning plans and optimize their progress based on feedback received from the server. Specifically, users view the feedback and set new goals and tasks.

[0119] Step 9:

[0120] The server enables information sharing among users through collaborative functions within the system and facilitates question-and-answer sessions between users. Specifically, the server monitors and manages forums and chat platforms to support smooth communication.

[0121] Step 10:

[0122] The device visually displays the user's learning progress and, as needed, displays notifications and encouraging messages from the server. Specifically, it displays progress on a dashboard and provides motivational messages via pop-ups.

[0123] (Application Example 1)

[0124] 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."

[0125] In online learning environments, the challenge lies in effectively managing learning plans and progress according to individual learning needs, while also stimulating communication among learners and improving their motivation to learn.

[0126] 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.

[0127] In this invention, the server includes means for generating a learning plan based on the user's goals and schedule, means for managing progress according to the generated learning plan, and means for providing personalized motivational notifications. This enables individual learners to learn efficiently at their own pace and deepen their learning while interacting with other learners.

[0128] A "user" refers to a learner who uses this system, and is an individual with their own learning needs and goals.

[0129] A "learning plan" refers to a set of plans, including learning content and schedules, created based on the user's goals and schedule.

[0130] "Progress management" refers to the process of monitoring the user's learning progress according to the generated learning plan and making appropriate adjustments.

[0131] A "test" refers to a set of questions or problems generated to periodically assess a user's level of understanding.

[0132] "Feedback" refers to information provided to users based on the evaluation results of tests, including areas for improvement and progress updates.

[0133] "Community features" refer to functions that enable information sharing and question-and-answer sessions among users, and play a role in promoting interaction among learners.

[0134] "Encouragement notifications" refer to a feature that automatically sends encouraging or reminder messages to users to motivate them to continue learning.

[0135] An "interface" refers to a visual display method that allows users to visually check their learning progress.

[0136] The "feedback generation process" refers to a series of procedures for shaping and providing feedback that is adapted to the user's learning schedule.

[0137] The system that implements this application is designed to efficiently support learners' online learning. The server generates individualized learning plans according to the user's goals and schedule. The plan creation uses Python and Django to build a backend system, leveraging optimization algorithms to create personalized learning schedules.

[0138] The generated learning plan is provided to the user through a smartphone interface developed with React Native. Here, the user can visually check their learning progress. This interface also serves to provide appropriate feedback based on their learning status.

[0139] The server periodically generates tests using machine learning libraries such as Scikit-learn and provides feedback based on the evaluation of those tests. Furthermore, it includes a feature to notify users with personalized motivational messages based on their learning progress and evaluation results.

[0140] Furthermore, to facilitate interaction among users, the server provides community features. These features allow users to exchange information and ask questions with other learners, making it easier to resolve any doubts they may have during their studies.

[0141] For example, in the case of a learner aiming to prepare for an English exam, the system receives the exam date and current score target as input and presents daily learning items. By including weekly practice tests in the schedule, learning can proceed more systematically and efficiently. Furthermore, learners can seek advice on the listening section from other learners through the community function.

[0142] Examples of prompts for the generative AI model include, "Please provide an algorithm that takes a user's target exam date and builds a personalized study schedule," and "Please suggest the most effective learning content and delivery methods for TOEIC test preparation." This enables support in providing learners with the optimal learning experience.

[0143] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0144] Step 1:

[0145] The server receives data on the user's goals and schedule. Based on this information, it generates a learning plan using Python. Here, an optimization algorithm is used to design the schedule best suited to the user. The input to this process is the user's goals and schedule information, and the output is a customized learning plan.

[0146] Step 2:

[0147] The server generates a learning plan and delivers it to the device. The device uses React Native to visually display the plan details so that the user can review them. At this stage, the input is the learning plan data from the server, and the output is a visualized learning schedule on the user interface.

[0148] Step 3:

[0149] The server periodically generates tests to evaluate the user's learning progress. It uses the machine learning library Scikit-learn to create test questions based on evaluation metrics. The input is user progress data, and the output is an evaluation test tailored to each individual user.

[0150] Step 4:

[0151] The user answers the test via a device. The device sends the received answers to the server. The server takes this answer data as input and performs evaluation calculations, including correctness determination. The output is the evaluation result, which is used to generate feedback.

[0152] Step 5:

[0153] The server generates feedback based on the evaluation results. This feedback includes adjustments and motivational messages for future learning. In this process, the evaluation results are the input, and the personalized feedback is the output.

[0154] Step 6:

[0155] The device notifies the user with feedback, including encouragement and reminders for learning achievements, to promote the user's continued learning. This allows the user to check their learning style and progress and move on to the next step.

[0156] Step 7:

[0157] The server provides community features to enable information sharing among users. Users can use their devices to interact with other learners, gaining new perspectives and knowledge through questions and answers. The introduction of this feature creates a space for users to share their own experiences.

[0158] 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.

[0159] This invention is a system that combines an emotion engine to improve the user's online learning experience. This system consists of a server that generates an optimal learning plan based on the user's goals and schedule, and a terminal used by the user. The server considers the user's learning objectives and schedule to create a learning plan tailored to their individual needs. This plan is transmitted to the terminal and visually confirmed by the user.

[0160] The emotion engine recognizes the user's emotions in real time and dynamically adjusts the learning plan and feedback according to the user's emotional state. This emotion recognition is performed by analyzing the user's facial expressions and tone of voice using sensors such as cameras and microphones. For example, if the user is feeling stressed, the server adjusts the plan to reduce the learning load and provide a more relaxed learning environment.

[0161] During the learning process, the server periodically generates tests to assess the user's understanding and delivers them to the user via their device. The user takes the test, and the answers are sent to the server. The server analyzes the answers and provides feedback based on the evaluation results. This feedback takes into account the user's emotional state and includes appropriate advice and encouraging messages.

[0162] Furthermore, emotional information recognized by the emotion engine is also utilized in community features to facilitate communication among users. The server suggests appropriate information sharing and question-and-answer sessions within the system. The terminal displays this information on a visual dashboard, supporting users in effectively interacting with their learning partners.

[0163] As a concrete example, let's assume a user is studying for an exam. If the emotion engine detects that this user is feeling anxious before the exam, the server generates an encouraging message to alleviate that anxiety and notifies the user through their device. Furthermore, if the user is struggling with a particular problem, the server provides feedback, including advice on adjusting their study pace.

[0164] In this way, the present invention provides a system that takes into account the user's emotional state and improves their individual learning experience. Furthermore, by incorporating an emotion engine, users can achieve a more comfortable and effective learning environment.

[0165] The following describes the processing flow.

[0166] Step 1:

[0167] Users log in to their devices and enter the subjects they want to study, their goals, and the time they have available. This information may include upcoming exams or long-term learning goals.

[0168] Step 2:

[0169] The terminal collects user input information and sends it to the server. The server uses this information to formulate a learning plan.

[0170] Step 3:

[0171] The server optimizes the learning process by referencing past learning patterns and learning material information stored in the database, and generates individually customized learning plans.

[0172] Step 4:

[0173] The server sends the generated learning plan to the terminal, which then visually displays the plan to the user. The display includes daily assignments and learning progress guidelines.

[0174] Step 5:

[0175] The user begins learning according to the device's learning plan. During this process, the device uses sensors to recognize the user's facial expressions and voice through its emotion engine.

[0176] Step 6:

[0177] The emotion engine analyzes the user's emotional state in real time, and if it detects, for example, stress or anxiety, it sends instructions to the server to adjust the learning load.

[0178] Step 7:

[0179] Based on the analysis results of the emotion engine, the server dynamically adjusts the learning plan and provides the user on the device with appropriate feedback and encouraging messages.

[0180] Step 8:

[0181] The server periodically generates comprehension tests and delivers them to users via their devices. The tests include questions tailored to the user's current learning progress.

[0182] Step 9:

[0183] Users take comprehension tests on their devices, and the results are sent back to the server. This allows the server to understand the user's learning progress and identify areas for improvement.

[0184] Step 10:

[0185] The server analyzes the test results, generates feedback that takes into account the user's emotional state, and notifies the user via the device. This feedback includes specific areas for improvement and suggestions for further learning.

[0186] Step 11:

[0187] Users can participate in learning communities via their devices, exchanging information and answering questions with other learners. Feedback from the community is also processed by an emotion engine and displayed in a way that is appropriate for the user.

[0188] Step 12:

[0189] The server periodically collects user learning and sentiment data and provides it to the device as a dashboard. This visualizes the overall progress of learning and helps users maintain motivation.

[0190] (Example 2)

[0191] 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 as the "terminal".

[0192] In online learning, users rely on the creation of appropriate learning plans, progress management, and feedback tailored to their level of understanding. However, traditional systems have difficulty flexibly responding to users' emotional states and individual progress, making it difficult to maximize learning effectiveness. Therefore, there is a need for systems that enable dynamic adjustment of learning plans according to each user's situation and effective information sharing.

[0193] 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.

[0194] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule; an information processing device that analyzes the user's emotional state in real time and dynamically adjusts the learning plan; and an information processing device that provides interaction functions enabling information sharing and responses to questions among users. This makes it possible to flexibly adjust the user's learning experience according to individual needs, enabling a more effective and communicative learning environment.

[0195] An "information processing device" is a computer system that receives and analyzes data and performs specific tasks.

[0196] A "learning plan" is a planned framework that defines the content and order in which to be learned, based on the user's goals and schedule.

[0197] "Progress management" is the process of understanding and appropriately managing a user's learning progress according to their learning plan.

[0198] A "test" is an evaluation method designed to assess a user's level of understanding.

[0199] "Feedback" is a method of providing information about learning progress and pointing out areas for improvement based on test results and user requests.

[0200] "Emotional state" refers to the user's mental state, primarily consisting of psychological tendencies and reactions inferred from facial expressions and tone of voice.

[0201] "Real-time analysis" refers to the process of collecting data and providing results immediately.

[0202] The "communication function" is a feature that provides an interactive platform for users to share information and answer questions.

[0203] "Dynamic adjustment" refers to a system automatically changing its operation or plan in response to specific conditions or changes in data.

[0204] This invention is a system that utilizes an information processing device to optimize the user experience in online learning. The system has a basic configuration of a server and a terminal, and includes various sensors and a generative AI model. This enables the generation of learning plans tailored to the user's goals and schedule, real-time monitoring of emotional state, and provision of appropriate feedback.

[0205] The server, acting as an information processing device, generates learning plans based on the user's objectives and schedule using an AI model. This model is based on natural language processing technology and selects the most suitable learning materials and content for the user's learning objectives. The server constructs the learning plan based on these materials and also stores data used to dynamically adjust the plan.

[0206] The device provides an interface with the user, analyzing the user's emotional state using sensors such as cameras and microphones, and transmitting this data to the server in real time. Based on this, the server interprets the user's current state using an emotion engine and adjusts the learning plan as needed. This makes it possible to provide a more optimal learning environment for the user.

[0207] For example, if a user is studying English while feeling nervous before an exam, the server can recognize their facial expressions through the camera and use a generative AI model to send encouraging messages to their device to help them relax. This can improve learning efficiency.

[0208] A concrete example of a prompt would be: "The user's learning objective is to improve their English proficiency, and their goal is to pass next month's exam. If the emotion engine detects user stress, what adjustments to the learning plan or messages should be generated?" This prompt allows the system to quickly grasp the user's individual needs and provide appropriate responses.

[0209] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0210] Step 1:

[0211] The server collects information from the user regarding their learning objectives and schedule. This information is sent via forms or applications that the user fills out. The server stores the received data in a database and prepares it as input data for the generated AI model. Specifically, when the user clicks the "Submit" button, the input information is transferred to the server.

[0212] Step 2:

[0213] The server generates a learning plan from the received user data using a generative AI model. This AI model utilizes natural language processing techniques to determine the optimal learning content and progression order. It takes the user's objectives and schedule as input and creates a learning plan with set priorities as output. Specifically, the server refers to the learning material database and saves the generated plan back into the database.

[0214] Step 3:

[0215] The server sends the generated learning plan to the terminal. The terminal receives this plan and displays it visually in the user interface. It receives the learning plan as input and displays it on the screen in a user-friendly format as output. Specifically, the terminal displays a notification pop-up and provides a link to view the detailed plan.

[0216] Step 4:

[0217] The device collects the user's facial expressions and voice through sensors and sends them to the server. User emotion data is mainly obtained from the camera and microphone. The server receives this data and performs real-time analysis using an emotion engine. Based on this input data, it infers the user's psychological state and outputs a specific emotional state. Specifically, the device periodically activates the camera and captures the user's face.

[0218] Step 5:

[0219] The server dynamically adjusts the learning plan based on the analysis results of the emotion engine. It receives the user's emotional state as input and generates a learning plan optimized for the user as output. Specifically, if stress is detected, it adds relaxing content to the plan and redistributes it to the device.

[0220] Step 6:

[0221] The server generates assessment tests tailored to the user's learning progress and delivers them to the user via the terminal. It creates test content based on learning progress data as input and provides the test as output. Specifically, the server selects test questions from specific educational resources and incorporates them into the test format.

[0222] Step 7:

[0223] The user takes a test delivered to their device and sends the results to the server. The system receives the user's test result data as input and generates feedback as output. Specifically, the user presses the "Start" button to display the test screen and submits their answers using the "Submit" button.

[0224] Step 8:

[0225] The server analyzes the received test results and generates feedback to facilitate user learning. This feedback includes advice that takes into account the user's emotional state. The generated feedback is sent to the terminal and provided to the user. Specifically, the server automatically aggregates the results and suggests areas for improvement based on the analysis.

[0226] (Application Example 2)

[0227] 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".

[0228] In online learning environments, a problem arises because uniform learning plans are provided that disregard the emotional state of each user, preventing them from receiving a learning experience tailored to their individual needs. Furthermore, the lack of technology to support learning while considering emotions in real time results in inefficiencies in learning efficiency and motivation.

[0229] 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.

[0230] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule, an analysis device that detects emotions and adjusts the learning plan, and an interaction device that outputs the generated message. This enables personalized learning support that responds to the user's emotional state.

[0231] An "information processing device" is a device that generates and provides an appropriate learning plan based on the user's objectives and schedule.

[0232] An "analysis device" is a device that has the function of detecting the user's emotions and adjusting the learning plan in real time.

[0233] A "dialogue device" is a device that outputs generated messages to the user, enabling two-way communication.

[0234] A "display device" is a device that visually shows a user's learning progress, allowing them to understand their learning status.

[0235] A "message generation device" is a device that generates messages to appropriately send encouragement and reminders to users.

[0236] The system for carrying out this invention includes multiple information processing devices to support the user's learning activities. First, the server generates an optimal learning plan based on the user's objectives and schedule. This plan is transmitted to the user's terminal and can be visually confirmed on a display device built into the terminal.

[0237] Furthermore, the server has an emotion detection and analysis device that recognizes the user's emotional state in real time from their facial expressions and voice. Specifically, it analyzes data collected using a camera and microphone to determine the user's stress level and concentration level. For example, it could utilize a platform commonly used as an emotion recognition API. Based on this data, the server dynamically adjusts the learning plan to provide a learning environment that is more suitable for the user's state.

[0238] The dialogue device outputs generated messages directly to the user, enabling two-way communication. Through the generating AI, it can provide encouragement and specific learning advice, thereby improving the user's learning efficiency. This prevents learning from becoming monotonous and allows for more active learning.

[0239] As a concrete example, if the server detects a distressed expression on a user's face while they are studying for an exam, it generates an encouraging message such as "Relax and think it through slowly" and outputs it through the interactive device. This helps the user to alleviate tension and continue studying efficiently.

[0240] Furthermore, as an example of a prompt message for the generating AI, instructions can be given to the AI ​​in the form of, "The user is showing signs of anxiety while preparing for the exam. Please generate an appropriate message of encouragement for this situation."

[0241] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0242] Step 1:

[0243] The server receives the user's objectives and schedule, and generates a learning plan based on this information. The input is the user's learning goals and schedule information, and an algorithm is used to formulate a personalized learning plan based on this information. The output is the generated learning plan, which is stored in the database.

[0244] Step 2:

[0245] The terminal receives the learning plan from the server and displays it visually to the user. The input is the learning plan data from the server, and the output is the visualization of the learning plan on the terminal's screen.

[0246] Step 3:

[0247] The server activates an analysis device to recognize the user's emotional state, collecting facial and audio data using a camera and microphone. This sensor data is the input, which is analyzed by an algorithm, and the user's emotional state is output.

[0248] Step 4:

[0249] The server dynamically adjusts the learning plan based on the user's emotional state obtained through analysis. The input is the current learning plan and the user's emotional state, and the output is the adjusted learning plan. This result is then sent back to the terminal.

[0250] Step 5:

[0251] The dialogue device uses a generative AI model to generate encouraging and advice messages for the user, outputting them as voice or text. The input consists of information about the user's emotional state and learning progress; prompts are sent to the generative AI to output messages.

[0252] Step 6:

[0253] The user receives messages from the dialogue device and learns accordingly. User feedback is also analyzed, and the system records it in a database for future adjustments.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] [Second Embodiment]

[0258] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0259] 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.

[0260] 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).

[0261] 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.

[0262] 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.

[0263] 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).

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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".

[0270] This invention is a system that supports users' online learning and is primarily executed via a server and a terminal. The server generates a learning plan based on the user's goals and schedule. This plan is optimized to the individual user's needs and delivered to the terminal as a specific learning schedule.

[0271] As the learning plan is executed, the server periodically generates tests to assess understanding. When the user takes these tests, feedback is generated based on the results. This feedback is integrated into the user's learning plan, optimizing the individual learning experience while adjusting progress.

[0272] Furthermore, the server provides community features to facilitate information sharing and question-and-answer sessions among users. Users can access the community through their devices and interact with other learners. This reduces feelings of isolation during learning and promotes peer learning.

[0273] The server also sends data to the device to visually display the user's learning progress. The device receives this data and helps maintain the user's motivation by showing them their level of achievement. Furthermore, the server generates encouraging and reminder messages for the user at appropriate times and notifies them through the device.

[0274] For example, if a user wants to prepare for an English exam, the system will create a study plan considering the user's target exam date and the required study time. The plan will include daily study tasks, periodic mini-tests, and feedback to check progress. The user can follow this plan and progress while receiving notifications from their device. At the same time, if questions arise during their studies, they can ask other learners through the community and deepen their understanding by receiving answers.

[0275] As a result, users can achieve personalized online learning tailored to their own pace and receive efficient and effective support to achieve their goals.

[0276] The following describes the processing flow.

[0277] Step 1:

[0278] The user logs into their device and enters their learning objectives, goals they want to achieve, and available time.

[0279] Step 2:

[0280] The terminal sends the entered user information to the server.

[0281] Step 3:

[0282] The server queries the internal database and analyzes past learning data and trends to generate an optimal learning plan based on the user's learning objectives and schedule.

[0283] Step 4:

[0284] The server creates an optimized learning plan and sends it to the terminal.

[0285] Step 5:

[0286] The terminal displays the learning plan to the user and notifies daily learning tasks.

[0287] Step 6:

[0288] The server periodically generates quizzes or tests to evaluate the user's understanding and sends that information to the terminal.

[0289] Step 7:

[0290] The user takes the test presented on the terminal and enters the answers.

[0291] Step 8:

[0292] The terminal sends the user's answer results to the server.

[0293] Step 9:

[0294] The server analyzes the answer results, evaluates the user's understanding, and generates feedback.

[0295] Step 10:

[0296] The terminal notifies the user of the feedback received from the server and displays areas for improvement and the next learning tasks.

[0297] Step 11:

[0298] The user accesses the learning community through the terminal to ask questions and exchange information.

[0299] Step 12:

[0300] The server manages the communication data between users and provides the terminal with statistics and information for promoting peer learning.

[0301] Step 13:

[0302] The server evaluates the progress of the user, generates visual data, and transmits it to the terminal.

[0303] Step 14:

[0304] The terminal displays graphs and dashboards that visually show the learning progress and achievement level of the user.

[0305] Step 15:

[0306] As appropriate, the server generates encouragement and reminder messages for the user and notifies the terminal.

[0307] (Example 1)

[0308] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In online learning, in order for individual learners to efficiently and effectively achieve their goals, customization of the learning plan, appropriate evaluation of understanding, feedback according to progress, a place for information sharing, and a mechanism to support the continuity of learning are necessary. However, in the conventional method, each element is fragmented, and there is a problem that it is difficult for learners to receive consistent support.

[0310] 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.

[0311] In this invention, the server includes computation means for generating an educational plan based on the user's goals and schedule, computation means for monitoring progress according to the generated educational plan, and computation means for creating tests to periodically evaluate the user's level of understanding. This enables the provision of personalized learning support.

[0312] A "user" is an individual or group that uses the system for the purpose of online learning.

[0313] A "goal" is the learning content or skill level that a user hopes to achieve through their learning process.

[0314] "Schedule" refers to scheduling information that indicates the time or period that a user allocates to learning.

[0315] An "educational plan" is a teaching guide that outlines how to proceed with learning, optimized to the user's goals and schedule.

[0316] "Computational means" refers to a method consisting of a computer or program used to perform a specific process or function.

[0317] "Progress" is an indicator that shows the degree of achievement a user has made in the process of learning according to their educational plan.

[0318] "Monitoring" is the act of tracking progress according to the plan and making adjustments in real time as needed.

[0319] "Testing" refers to an evaluation method used to measure a user's level of understanding, and is typically conducted in the form of a test or quiz.

[0320] "Collaboration features" refer to online communication methods designed to facilitate information sharing and question-and-answer sessions among users.

[0321] "Visually displaying" refers to showing learning data, such as progress and achievement levels, in a way that users can intuitively understand.

[0322] A "message generation method" is a function that creates messages to provide users with notifications and encouragement at the appropriate time.

[0323] This invention is a system that supports users' online learning and is implemented via a server and a terminal, which are its main components.

[0324] The server uses a generative AI model to generate personalized learning plans based on the goals and schedules provided by the user. This generative AI model is used to design optimal learning content that aligns with the user's specific needs and schedule.

[0325] The server also monitors progress according to the plan and creates tests to periodically assess the user's understanding. Based on the results of these tests, the server generates feedback and incorporates it into the plan to optimize the user's learning experience.

[0326] The device receives educational plans and feedback sent from the server and presents them to the user in an intuitively understandable format. The device also visually displays learning progress and provides timely reminders and encouraging messages to maintain user motivation.

[0327] Users can progress through their learning by following educational plans provided from the server via their devices. Furthermore, users can deepen their learning by sharing information with other users and asking questions using collaborative features.

[0328] For example, if a user wants to prepare for an English exam, the server creates a study plan working backward from the exam date, incorporating daily learning tasks and periodic checks to assess understanding. Following this plan, the user can efficiently progress through their studies while receiving notifications on their device. If questions arise during learning, they can ask other learners through the collaborative function and receive answers.

[0329] Example of a prompt:

[0330] "Please create a three-month study plan to prepare for the next English exam. I would like to allocate one hour of study time per day and include a comprehension test once a week."

[0331] The above describes specific embodiments for carrying out this invention.

[0332] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0333] Step 1:

[0334] Users enter necessary information such as learning goals and schedules through an online platform. This sends the user's learning needs to the server. Specifically, users enter the exam date, desired study time, and the conditions necessary to achieve their goals on a web form.

[0335] Step 2:

[0336] The server inputs information received from the user into a generating AI model to create a personalized learning plan. This input includes learning objectives, exam dates, and available study time. Based on this data, the AI ​​designs an optimal learning schedule and delivers it to the user as a plan. The resulting learning plan includes daily tasks and scheduled periodic check-ups.

[0337] Step 3:

[0338] The server sends the generated learning plan to the user's device. This allows the user to begin learning based on the plan. Specifically, the server sends the plan data to the device, which then displays it visually in a calendar app or a dedicated app.

[0339] Step 4:

[0340] Users progress through their learning according to the educational plan using their devices and report their progress to the server. Questions and progress that arise during learning are sent to the server and recorded. Specifically, the system checks the user's progress after they complete a learning task and adjusts the schedule as needed.

[0341] Step 5:

[0342] The server periodically generates tests based on progress data. The input data is the user's progress, and the server designs tests to assess understanding based on this data. The output tests function as tools to measure the user's level of understanding.

[0343] Step 6:

[0344] The user takes a comprehension test presented on their device and sends the results to the server. Specifically, this involves the user completing the test and uploading the results to the server.

[0345] Step 7:

[0346] The server evaluates the received test results and generates feedback on the user's learning progress. This feedback is then sent back to the user's device to help improve their learning plan. Specifically, the server uses an analysis algorithm to evaluate the results and constructs feedback based on that analysis.

[0347] Step 8:

[0348] Users adjust their learning plans and optimize their progress based on feedback received from the server. Specifically, users view the feedback and set new goals and tasks.

[0349] Step 9:

[0350] The server enables information sharing among users through collaborative functions within the system and facilitates question-and-answer sessions between users. Specifically, the server monitors and manages forums and chat platforms to support smooth communication.

[0351] Step 10:

[0352] The device visually displays the user's learning progress and, as needed, displays notifications and encouraging messages from the server. Specifically, it displays progress on a dashboard and provides motivational messages via pop-ups.

[0353] (Application Example 1)

[0354] 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."

[0355] In online learning environments, the challenge lies in effectively managing learning plans and progress according to individual learning needs, while also stimulating communication among learners and improving their motivation to learn.

[0356] 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.

[0357] In this invention, the server includes means for generating a learning plan based on the user's goals and schedule, means for managing progress according to the generated learning plan, and means for providing personalized motivational notifications. This enables individual learners to learn efficiently at their own pace and deepen their learning while interacting with other learners.

[0358] A "user" refers to a learner who uses this system, and is an individual with their own learning needs and goals.

[0359] A "learning plan" refers to a set of plans, including learning content and schedules, created based on the user's goals and schedule.

[0360] "Progress management" refers to the process of monitoring the user's learning progress according to the generated learning plan and making appropriate adjustments.

[0361] A "test" refers to a set of questions or problems generated to periodically assess a user's level of understanding.

[0362] "Feedback" refers to information provided to users based on the evaluation results of tests, including areas for improvement and progress updates.

[0363] "Community features" refer to functions that enable information sharing and question-and-answer sessions among users, and play a role in promoting interaction among learners.

[0364] "Encouragement notifications" refer to a feature that automatically sends encouraging or reminder messages to users to motivate them to continue learning.

[0365] An "interface" refers to a visual display method that allows users to visually check their learning progress.

[0366] The "feedback generation process" refers to a series of procedures for shaping and providing feedback that is adapted to the user's learning schedule.

[0367] The system that implements this application is designed to efficiently support learners' online learning. The server generates individualized learning plans according to the user's goals and schedule. The plan creation uses Python and Django to build a backend system, leveraging optimization algorithms to create personalized learning schedules.

[0368] The generated learning plan is provided to the user through a smartphone interface developed with React Native. Here, the user can visually check their learning progress. This interface also serves to provide appropriate feedback based on their learning status.

[0369] The server periodically generates tests using machine learning libraries such as Scikit-learn and provides feedback based on the evaluation of those tests. Furthermore, it includes a feature to notify users with personalized motivational messages based on their learning progress and evaluation results.

[0370] Furthermore, to facilitate interaction among users, the server provides community features. These features allow users to exchange information and ask questions with other learners, making it easier to resolve any doubts they may have during their studies.

[0371] For example, in the case of a learner aiming to prepare for an English exam, the system receives the exam date and current score target as input and presents daily learning items. By including weekly practice tests in the schedule, learning can proceed more systematically and efficiently. Furthermore, learners can seek advice on the listening section from other learners through the community function.

[0372] Examples of prompts for the generative AI model include, "Please provide an algorithm that takes a user's target exam date and builds a personalized study schedule," and "Please suggest the most effective learning content and delivery methods for TOEIC test preparation." This enables support in providing learners with the optimal learning experience.

[0373] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0374] Step 1:

[0375] The server receives data on the user's goals and schedule. Based on this information, it generates a learning plan using Python. Here, an optimization algorithm is used to design the schedule best suited to the user. The input to this process is the user's goals and schedule information, and the output is a customized learning plan.

[0376] Step 2:

[0377] The server generates a learning plan and delivers it to the device. The device uses React Native to visually display the plan details so that the user can review them. At this stage, the input is the learning plan data from the server, and the output is a visualized learning schedule on the user interface.

[0378] Step 3:

[0379] The server periodically generates tests to evaluate the user's learning progress. It uses the machine learning library Scikit-learn to create test questions based on evaluation metrics. The input is user progress data, and the output is an evaluation test tailored to each individual user.

[0380] Step 4:

[0381] The user answers the test via a device. The device sends the received answers to the server. The server takes this answer data as input and performs evaluation calculations, including correctness determination. The output is the evaluation result, which is used to generate feedback.

[0382] Step 5:

[0383] The server generates feedback based on the evaluation results. This feedback includes adjustments and motivational messages for future learning. In this process, the evaluation results are the input, and the personalized feedback is the output.

[0384] Step 6:

[0385] The device notifies the user with feedback, including encouragement and reminders for learning achievements, to promote the user's continued learning. This allows the user to check their learning style and progress and move on to the next step.

[0386] Step 7:

[0387] The server provides community features to enable information sharing among users. Users can use their devices to interact with other learners, gaining new perspectives and knowledge through questions and answers. The introduction of this feature creates a space for users to share their own experiences.

[0388] 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.

[0389] This invention is a system that combines an emotion engine to improve the user's online learning experience. This system consists of a server that generates an optimal learning plan based on the user's goals and schedule, and a terminal used by the user. The server considers the user's learning objectives and schedule to create a learning plan tailored to their individual needs. This plan is transmitted to the terminal and visually confirmed by the user.

[0390] The emotion engine recognizes the user's emotions in real time and dynamically adjusts the learning plan and feedback according to the user's emotional state. This emotion recognition is performed by analyzing the user's facial expressions and tone of voice using sensors such as cameras and microphones. For example, if the user is feeling stressed, the server adjusts the plan to reduce the learning load and provide a more relaxed learning environment.

[0391] During the learning process, the server periodically generates tests to assess the user's understanding and delivers them to the user via their device. The user takes the test, and the answers are sent to the server. The server analyzes the answers and provides feedback based on the evaluation results. This feedback takes into account the user's emotional state and includes appropriate advice and encouraging messages.

[0392] Furthermore, emotional information recognized by the emotion engine is also utilized in community features to facilitate communication among users. The server suggests appropriate information sharing and question-and-answer sessions within the system. The terminal displays this information on a visual dashboard, supporting users in effectively interacting with their learning partners.

[0393] As a concrete example, let's assume a user is studying for an exam. If the emotion engine detects that this user is feeling anxious before the exam, the server generates an encouraging message to alleviate that anxiety and notifies the user through their device. Furthermore, if the user is struggling with a particular problem, the server provides feedback, including advice on adjusting their study pace.

[0394] In this way, the present invention provides a system that takes into account the user's emotional state and improves their individual learning experience. Furthermore, by incorporating an emotion engine, users can achieve a more comfortable and effective learning environment.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] Users log in to their devices and enter the subjects they want to study, their goals, and the time they have available. This information may include upcoming exams or long-term learning goals.

[0398] Step 2:

[0399] The terminal collects user input information and sends it to the server. The server uses this information to formulate a learning plan.

[0400] Step 3:

[0401] The server optimizes the learning process by referencing past learning patterns and learning material information stored in the database, and generates individually customized learning plans.

[0402] Step 4:

[0403] The server sends the generated learning plan to the terminal, which then visually displays the plan to the user. The display includes daily assignments and learning progress guidelines.

[0404] Step 5:

[0405] The user begins learning according to the device's learning plan. During this process, the device uses sensors to recognize the user's facial expressions and voice through its emotion engine.

[0406] Step 6:

[0407] The emotion engine analyzes the user's emotional state in real time, and if it detects, for example, stress or anxiety, it sends instructions to the server to adjust the learning load.

[0408] Step 7:

[0409] Based on the analysis results of the emotion engine, the server dynamically adjusts the learning plan and provides the user on the device with appropriate feedback and encouraging messages.

[0410] Step 8:

[0411] The server periodically generates comprehension tests and delivers them to users via their devices. The tests include questions tailored to the user's current learning progress.

[0412] Step 9:

[0413] Users take comprehension tests on their devices, and the results are sent back to the server. This allows the server to understand the user's learning progress and identify areas for improvement.

[0414] Step 10:

[0415] The server analyzes the test results, generates feedback that takes into account the user's emotional state, and notifies the user via the device. This feedback includes specific areas for improvement and suggestions for further learning.

[0416] Step 11:

[0417] Users can participate in learning communities via their devices, exchanging information and answering questions with other learners. Feedback from the community is also processed by an emotion engine and displayed in a way that is appropriate for the user.

[0418] Step 12:

[0419] The server periodically collects user learning and sentiment data and provides it to the device as a dashboard. This visualizes the overall progress of learning and helps users maintain motivation.

[0420] (Example 2)

[0421] 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".

[0422] In online learning, users rely on the creation of appropriate learning plans, progress management, and feedback tailored to their level of understanding. However, traditional systems have difficulty flexibly responding to users' emotional states and individual progress, making it difficult to maximize learning effectiveness. Therefore, there is a need for systems that enable dynamic adjustment of learning plans according to each user's situation and effective information sharing.

[0423] 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.

[0424] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule; an information processing device that analyzes the user's emotional state in real time and dynamically adjusts the learning plan; and an information processing device that provides interaction functions enabling information sharing and responses to questions among users. This makes it possible to flexibly adjust the user's learning experience according to individual needs, enabling a more effective and communicative learning environment.

[0425] An "information processing device" is a computer system that receives and analyzes data and performs specific tasks.

[0426] A "learning plan" is a planned framework that defines the content and order in which to be learned, based on the user's goals and schedule.

[0427] "Progress management" is the process of understanding and appropriately managing a user's learning progress according to their learning plan.

[0428] A "test" is an evaluation method designed to assess a user's level of understanding.

[0429] "Feedback" is a method of providing information about learning progress and pointing out areas for improvement based on test results and user requests.

[0430] "Emotional state" refers to the user's mental state, primarily consisting of psychological tendencies and reactions inferred from facial expressions and tone of voice.

[0431] "Real-time analysis" refers to the process of collecting data and providing results immediately.

[0432] The "communication function" is a feature that provides an interactive platform for users to share information and answer questions.

[0433] "Dynamic adjustment" refers to a system automatically changing its operation or plan in response to specific conditions or changes in data.

[0434] This invention is a system that utilizes an information processing device to optimize the user experience in online learning. The system has a basic configuration of a server and a terminal, and includes various sensors and a generative AI model. This enables the generation of learning plans tailored to the user's goals and schedule, real-time monitoring of emotional state, and provision of appropriate feedback.

[0435] The server, acting as an information processing device, generates learning plans based on the user's objectives and schedule using an AI model. This model is based on natural language processing technology and selects the most suitable learning materials and content for the user's learning objectives. The server constructs the learning plan based on these materials and also stores data used to dynamically adjust the plan.

[0436] The device provides an interface with the user, analyzing the user's emotional state using sensors such as cameras and microphones, and transmitting this data to the server in real time. Based on this, the server interprets the user's current state using an emotion engine and adjusts the learning plan as needed. This makes it possible to provide a more optimal learning environment for the user.

[0437] For example, if a user is studying English while feeling nervous before an exam, the server can recognize their facial expressions through the camera and use a generative AI model to send encouraging messages to their device to help them relax. This can improve learning efficiency.

[0438] A concrete example of a prompt would be: "The user's learning objective is to improve their English proficiency, and their goal is to pass next month's exam. If the emotion engine detects user stress, what adjustments to the learning plan or messages should be generated?" This prompt allows the system to quickly grasp the user's individual needs and provide appropriate responses.

[0439] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0440] Step 1:

[0441] The server collects information from the user regarding their learning objectives and schedule. This information is sent via forms or applications that the user fills out. The server stores the received data in a database and prepares it as input data for the generated AI model. Specifically, when the user clicks the "Submit" button, the input information is transferred to the server.

[0442] Step 2:

[0443] The server generates a learning plan from the received user data using a generative AI model. This AI model utilizes natural language processing techniques to determine the optimal learning content and progression order. It takes the user's objectives and schedule as input and creates a learning plan with set priorities as output. Specifically, the server refers to the learning material database and saves the generated plan back into the database.

[0444] Step 3:

[0445] The server sends the generated learning plan to the terminal. The terminal receives this plan and displays it visually in the user interface. It receives the learning plan as input and displays it on the screen in a user-friendly format as output. Specifically, the terminal displays a notification pop-up and provides a link to view the detailed plan.

[0446] Step 4:

[0447] The device collects the user's facial expressions and voice through sensors and sends them to the server. User emotion data is mainly obtained from the camera and microphone. The server receives this data and performs real-time analysis using an emotion engine. Based on this input data, it infers the user's psychological state and outputs a specific emotional state. Specifically, the device periodically activates the camera and captures the user's face.

[0448] Step 5:

[0449] The server dynamically adjusts the learning plan based on the analysis results of the emotion engine. It receives the user's emotional state as input and generates a learning plan optimized for the user as output. Specifically, if stress is detected, it adds relaxing content to the plan and redistributes it to the device.

[0450] Step 6:

[0451] The server generates assessment tests tailored to the user's learning progress and delivers them to the user via the terminal. It creates test content based on learning progress data as input and provides the test as output. Specifically, the server selects test questions from specific educational resources and incorporates them into the test format.

[0452] Step 7:

[0453] The user takes a test delivered to their device and sends the results to the server. The system receives the user's test result data as input and generates feedback as output. Specifically, the user presses the "Start" button to display the test screen and submits their answers using the "Submit" button.

[0454] Step 8:

[0455] The server analyzes the received test results and generates feedback to facilitate user learning. This feedback includes advice that takes into account the user's emotional state. The generated feedback is sent to the terminal and provided to the user. Specifically, the server automatically aggregates the results and suggests areas for improvement based on the analysis.

[0456] (Application Example 2)

[0457] 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."

[0458] In online learning environments, a problem arises because uniform learning plans are provided that disregard the emotional state of each user, preventing them from receiving a learning experience tailored to their individual needs. Furthermore, the lack of technology to support learning while considering emotions in real time results in inefficiencies in learning efficiency and motivation.

[0459] 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.

[0460] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule, an analysis device that detects emotions and adjusts the learning plan, and an interaction device that outputs the generated message. This enables personalized learning support that responds to the user's emotional state.

[0461] An "information processing device" is a device that generates and provides an appropriate learning plan based on the user's objectives and schedule.

[0462] An "analysis device" is a device that has the function of detecting the user's emotions and adjusting the learning plan in real time.

[0463] A "dialogue device" is a device that outputs generated messages to the user, enabling two-way communication.

[0464] A "display device" is a device that visually shows a user's learning progress, allowing them to understand their learning status.

[0465] A "message generation device" is a device that generates messages to appropriately send encouragement and reminders to users.

[0466] The system for carrying out this invention includes multiple information processing devices to support the user's learning activities. First, the server generates an optimal learning plan based on the user's objectives and schedule. This plan is transmitted to the user's terminal and can be visually confirmed on a display device built into the terminal.

[0467] Furthermore, the server has an emotion detection and analysis device that recognizes the user's emotional state in real time from their facial expressions and voice. Specifically, it analyzes data collected using a camera and microphone to determine the user's stress level and concentration level. For example, it could utilize a platform commonly used as an emotion recognition API. Based on this data, the server dynamically adjusts the learning plan to provide a learning environment that is more suitable for the user's state.

[0468] The dialogue device outputs generated messages directly to the user, enabling two-way communication. Through the generating AI, it can provide encouragement and specific learning advice, thereby improving the user's learning efficiency. This prevents learning from becoming monotonous and allows for more active learning.

[0469] As a concrete example, if the server detects a distressed expression on a user's face while they are studying for an exam, it generates an encouraging message such as "Relax and think it through slowly" and outputs it through the interactive device. This helps the user to alleviate tension and continue studying efficiently.

[0470] Furthermore, as an example of a prompt message for the generating AI, instructions can be given to the AI ​​in the form of, "The user is showing signs of anxiety while preparing for the exam. Please generate an appropriate message of encouragement for this situation."

[0471] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0472] Step 1:

[0473] The server receives the user's objectives and schedule, and generates a learning plan based on this information. The input is the user's learning goals and schedule information, and an algorithm is used to formulate a personalized learning plan based on this information. The output is the generated learning plan, which is stored in the database.

[0474] Step 2:

[0475] The terminal receives the learning plan from the server and displays it visually to the user. The input is the learning plan data from the server, and the output is the visualization of the learning plan on the terminal's screen.

[0476] Step 3:

[0477] The server activates an analysis device to recognize the user's emotional state, collecting facial and audio data using a camera and microphone. This sensor data is the input, which is analyzed by an algorithm, and the user's emotional state is output.

[0478] Step 4:

[0479] The server dynamically adjusts the learning plan based on the user's emotional state obtained through analysis. The input is the current learning plan and the user's emotional state, and the output is the adjusted learning plan. This result is then sent back to the terminal.

[0480] Step 5:

[0481] The dialogue device uses a generative AI model to generate encouraging and advice messages for the user, outputting them as voice or text. The input consists of information about the user's emotional state and learning progress; prompts are sent to the generative AI to output messages.

[0482] Step 6:

[0483] The user receives messages from the dialogue device and learns accordingly. User feedback is also analyzed, and the system records it in a database for future adjustments.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] [Third Embodiment]

[0488] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0489] 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.

[0490] 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).

[0491] 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.

[0492] 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.

[0493] 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).

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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".

[0500] This invention is a system that supports users' online learning and is primarily executed via a server and a terminal. The server generates a learning plan based on the user's goals and schedule. This plan is optimized to the individual user's needs and delivered to the terminal as a specific learning schedule.

[0501] As the learning plan is executed, the server periodically generates tests to assess understanding. When the user takes these tests, feedback is generated based on the results. This feedback is integrated into the user's learning plan, optimizing the individual learning experience while adjusting progress.

[0502] Furthermore, the server provides community features to facilitate information sharing and question-and-answer sessions among users. Users can access the community through their devices and interact with other learners. This reduces feelings of isolation during learning and promotes peer learning.

[0503] The server also sends data to the device to visually display the user's learning progress. The device receives this data and helps maintain the user's motivation by showing them their level of achievement. Furthermore, the server generates encouraging and reminder messages for the user at appropriate times and notifies them through the device.

[0504] For example, if a user wants to prepare for an English exam, the system will create a study plan considering the user's target exam date and the required study time. The plan will include daily study tasks, periodic mini-tests, and feedback to check progress. The user can follow this plan and progress while receiving notifications from their device. At the same time, if questions arise during their studies, they can ask other learners through the community and deepen their understanding by receiving answers.

[0505] As a result, users can achieve personalized online learning tailored to their own pace and receive efficient and effective support to achieve their goals.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The user logs into their device and enters their learning objectives, goals they want to achieve, and available time.

[0509] Step 2:

[0510] The terminal sends the entered user information to the server.

[0511] Step 3:

[0512] The server queries an internal database and analyzes past learning data and trends to generate an optimal learning plan based on the user's learning objectives and schedule.

[0513] Step 4:

[0514] The server creates an optimized learning plan and sends it to the terminal.

[0515] Step 5:

[0516] The device displays the user's learning plan and notifies them of daily learning tasks.

[0517] Step 6:

[0518] The server periodically generates quizzes and tests to assess the user's understanding and sends that information to the terminal.

[0519] Step 7:

[0520] The user takes a test presented on their device and enters their answers.

[0521] Step 8:

[0522] The device sends the user's answer results to the server.

[0523] Step 9:

[0524] The server analyzes the answer results, evaluates the user's level of understanding, and generates feedback.

[0525] Step 10:

[0526] The device notifies the user of feedback received from the server, displaying areas for improvement and the next learning task.

[0527] Step 11:

[0528] Users access the learning community through their devices to ask questions and exchange information.

[0529] Step 12:

[0530] The server manages user interaction data and provides terminals with statistics and information to facilitate peer learning.

[0531] Step 13:

[0532] The server evaluates the user's progress, generates visual data, and sends it to the terminal.

[0533] Step 14:

[0534] The device displays graphs and dashboards that visually show the user's learning progress and achievements.

[0535] Step 15:

[0536] The server will generate encouraging and reminder messages for the user as needed and notify their device.

[0537] (Example 1)

[0538] 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."

[0539] In online learning, for individual learners to efficiently and effectively achieve their goals, they need customized learning plans, appropriate assessment of understanding, progress-based feedback, a platform for information sharing, and mechanisms to support the continuity of learning. However, traditional methods have fragmented these elements, making it difficult for learners to receive consistent support.

[0540] 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.

[0541] In this invention, the server includes computation means for generating an educational plan based on the user's goals and schedule, computation means for monitoring progress according to the generated educational plan, and computation means for creating tests to periodically evaluate the user's level of understanding. This enables the provision of personalized learning support.

[0542] A "user" is an individual or group that uses the system for the purpose of online learning.

[0543] A "goal" is the learning content or skill level that a user hopes to achieve through their learning process.

[0544] "Schedule" refers to scheduling information that indicates the time or period that a user allocates to learning.

[0545] An "educational plan" is a teaching guide that outlines how to proceed with learning, optimized to the user's goals and schedule.

[0546] "Computational means" refers to a method consisting of a computer or program used to perform a specific process or function.

[0547] "Progress" is an indicator that shows the degree of achievement a user has made in the process of learning according to their educational plan.

[0548] "Monitoring" is the act of tracking progress according to the plan and making adjustments in real time as needed.

[0549] "Testing" refers to an evaluation method used to measure a user's level of understanding, and is typically conducted in the form of a test or quiz.

[0550] "Collaboration features" refer to online communication methods designed to facilitate information sharing and question-and-answer sessions among users.

[0551] "Visually displaying" refers to showing learning data, such as progress and achievement levels, in a way that users can intuitively understand.

[0552] A "message generation method" is a function that creates messages to provide users with notifications and encouragement at the appropriate time.

[0553] This invention is a system that supports users' online learning and is implemented via a server and a terminal, which are its main components.

[0554] The server uses a generative AI model to generate personalized learning plans based on the goals and schedules provided by the user. This generative AI model is used to design optimal learning content that aligns with the user's specific needs and schedule.

[0555] The server also monitors progress according to the plan and creates tests to periodically assess the user's understanding. Based on the results of these tests, the server generates feedback and incorporates it into the plan to optimize the user's learning experience.

[0556] The device receives educational plans and feedback sent from the server and presents them to the user in an intuitively understandable format. The device also visually displays learning progress and provides timely reminders and encouraging messages to maintain user motivation.

[0557] Users can progress through their learning by following educational plans provided from the server via their devices. Furthermore, users can deepen their learning by sharing information with other users and asking questions using collaborative features.

[0558] For example, if a user wants to prepare for an English exam, the server creates a study plan working backward from the exam date, incorporating daily learning tasks and periodic checks to assess understanding. Following this plan, the user can efficiently progress through their studies while receiving notifications on their device. If questions arise during learning, they can ask other learners through the collaborative function and receive answers.

[0559] Example of a prompt:

[0560] "Please create a three-month study plan to prepare for the next English exam. I would like to allocate one hour of study time per day and include a comprehension test once a week."

[0561] The above describes specific embodiments for carrying out this invention.

[0562] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0563] Step 1:

[0564] Users enter necessary information such as learning goals and schedules through an online platform. This sends the user's learning needs to the server. Specifically, users enter the exam date, desired study time, and the conditions necessary to achieve their goals on a web form.

[0565] Step 2:

[0566] The server inputs information received from the user into a generating AI model to create a personalized learning plan. This input includes learning objectives, exam dates, and available study time. Based on this data, the AI ​​designs an optimal learning schedule and delivers it to the user as a plan. The resulting learning plan includes daily tasks and scheduled periodic check-ups.

[0567] Step 3:

[0568] The server sends the generated learning plan to the user's device. This allows the user to begin learning based on the plan. Specifically, the server sends the plan data to the device, which then displays it visually in a calendar app or a dedicated app.

[0569] Step 4:

[0570] Users progress through their learning according to the educational plan using their devices and report their progress to the server. Questions and progress that arise during learning are sent to the server and recorded. Specifically, the system checks the user's progress after they complete a learning task and adjusts the schedule as needed.

[0571] Step 5:

[0572] The server periodically generates tests based on progress data. The input data is the user's progress, and the server designs tests to assess understanding based on this data. The output tests function as tools to measure the user's level of understanding.

[0573] Step 6:

[0574] The user takes a comprehension test presented on their device and sends the results to the server. Specifically, this involves the user completing the test and uploading the results to the server.

[0575] Step 7:

[0576] The server evaluates the received test results and generates feedback on the user's learning progress. This feedback is then sent back to the user's device to help improve their learning plan. Specifically, the server uses an analysis algorithm to evaluate the results and constructs feedback based on that analysis.

[0577] Step 8:

[0578] Users adjust their learning plans and optimize their progress based on feedback received from the server. Specifically, users view the feedback and set new goals and tasks.

[0579] Step 9:

[0580] The server enables information sharing among users through collaborative functions within the system and facilitates question-and-answer sessions between users. Specifically, the server monitors and manages forums and chat platforms to support smooth communication.

[0581] Step 10:

[0582] The device visually displays the user's learning progress and, as needed, displays notifications and encouraging messages from the server. Specifically, it displays progress on a dashboard and provides motivational messages via pop-ups.

[0583] (Application Example 1)

[0584] 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."

[0585] In online learning environments, the challenge lies in effectively managing learning plans and progress according to individual learning needs, while also stimulating communication among learners and improving their motivation to learn.

[0586] 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.

[0587] In this invention, the server includes means for generating a learning plan based on the user's goals and schedule, means for managing progress according to the generated learning plan, and means for providing personalized motivational notifications. This enables individual learners to learn efficiently at their own pace and deepen their learning while interacting with other learners.

[0588] A "user" refers to a learner who uses this system, and is an individual with their own learning needs and goals.

[0589] A "learning plan" refers to a set of plans, including learning content and schedules, created based on the user's goals and schedule.

[0590] "Progress management" refers to the process of monitoring the user's learning progress according to the generated learning plan and making appropriate adjustments.

[0591] A "test" refers to a set of questions or problems generated to periodically assess a user's level of understanding.

[0592] "Feedback" refers to information provided to users based on the evaluation results of tests, including areas for improvement and progress updates.

[0593] "Community features" refer to functions that enable information sharing and question-and-answer sessions among users, and play a role in promoting interaction among learners.

[0594] "Encouragement notifications" refer to a feature that automatically sends encouraging or reminder messages to users to motivate them to continue learning.

[0595] An "interface" refers to a visual display method that allows users to visually check their learning progress.

[0596] The "feedback generation process" refers to a series of procedures for shaping and providing feedback that is adapted to the user's learning schedule.

[0597] The system that implements this application is designed to efficiently support learners' online learning. The server generates individualized learning plans according to the user's goals and schedule. The plan creation uses Python and Django to build a backend system, leveraging optimization algorithms to create personalized learning schedules.

[0598] The generated learning plan is provided to the user through a smartphone interface developed with React Native. Here, the user can visually check their learning progress. This interface also serves to provide appropriate feedback based on their learning status.

[0599] The server periodically generates tests using machine learning libraries such as Scikit-learn and provides feedback based on the evaluation of those tests. Furthermore, it includes a feature to notify users with personalized motivational messages based on their learning progress and evaluation results.

[0600] Furthermore, to facilitate interaction among users, the server provides community features. These features allow users to exchange information and ask questions with other learners, making it easier to resolve any doubts they may have during their studies.

[0601] For example, in the case of a learner aiming to prepare for an English exam, the system receives the exam date and current score target as input and presents daily learning items. By including weekly practice tests in the schedule, learning can proceed more systematically and efficiently. Furthermore, learners can seek advice on the listening section from other learners through the community function.

[0602] Examples of prompts for the generative AI model include, "Please provide an algorithm that takes a user's target exam date and builds a personalized study schedule," and "Please suggest the most effective learning content and delivery methods for TOEIC test preparation." This enables support in providing learners with the optimal learning experience.

[0603] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0604] Step 1:

[0605] The server receives data on the user's goals and schedule. Based on this information, it generates a learning plan using Python. Here, an optimization algorithm is used to design the schedule best suited to the user. The input to this process is the user's goals and schedule information, and the output is a customized learning plan.

[0606] Step 2:

[0607] The server generates a learning plan and delivers it to the device. The device uses React Native to visually display the plan details so that the user can review them. At this stage, the input is the learning plan data from the server, and the output is a visualized learning schedule on the user interface.

[0608] Step 3:

[0609] The server periodically generates tests to evaluate the user's learning progress. It uses the machine learning library Scikit-learn to create test questions based on evaluation metrics. The input is user progress data, and the output is an evaluation test tailored to each individual user.

[0610] Step 4:

[0611] The user answers the test via a device. The device sends the received answers to the server. The server takes this answer data as input and performs evaluation calculations, including correctness determination. The output is the evaluation result, which is used to generate feedback.

[0612] Step 5:

[0613] The server generates feedback based on the evaluation results. This feedback includes adjustments and motivational messages for future learning. In this process, the evaluation results are the input, and the personalized feedback is the output.

[0614] Step 6:

[0615] The device notifies the user with feedback, including encouragement and reminders for learning achievements, to promote the user's continued learning. This allows the user to check their learning style and progress and move on to the next step.

[0616] Step 7:

[0617] The server provides community features to enable information sharing among users. Users can use their devices to interact with other learners, gaining new perspectives and knowledge through questions and answers. The introduction of this feature creates a space for users to share their own experiences.

[0618] 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.

[0619] This invention is a system that combines an emotion engine to improve the user's online learning experience. This system consists of a server that generates an optimal learning plan based on the user's goals and schedule, and a terminal used by the user. The server considers the user's learning objectives and schedule to create a learning plan tailored to their individual needs. This plan is transmitted to the terminal and visually confirmed by the user.

[0620] The emotion engine recognizes the user's emotions in real time and dynamically adjusts the learning plan and feedback according to the user's emotional state. This emotion recognition is performed by analyzing the user's facial expressions and tone of voice using sensors such as cameras and microphones. For example, if the user is feeling stressed, the server adjusts the plan to reduce the learning load and provide a more relaxed learning environment.

[0621] During the learning process, the server periodically generates tests to assess the user's understanding and delivers them to the user via their device. The user takes the test, and the answers are sent to the server. The server analyzes the answers and provides feedback based on the evaluation results. This feedback takes into account the user's emotional state and includes appropriate advice and encouraging messages.

[0622] Furthermore, emotional information recognized by the emotion engine is also utilized in community features to facilitate communication among users. The server suggests appropriate information sharing and question-and-answer sessions within the system. The terminal displays this information on a visual dashboard, supporting users in effectively interacting with their learning partners.

[0623] As a concrete example, let's assume a user is studying for an exam. If the emotion engine detects that this user is feeling anxious before the exam, the server generates an encouraging message to alleviate that anxiety and notifies the user through their device. Furthermore, if the user is struggling with a particular problem, the server provides feedback, including advice on adjusting their study pace.

[0624] In this way, the present invention provides a system that takes into account the user's emotional state and improves their individual learning experience. Furthermore, by incorporating an emotion engine, users can achieve a more comfortable and effective learning environment.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] Users log in to their devices and enter the subjects they want to study, their goals, and the time they have available. This information may include upcoming exams or long-term learning goals.

[0628] Step 2:

[0629] The terminal collects user input information and sends it to the server. The server uses this information to formulate a learning plan.

[0630] Step 3:

[0631] The server optimizes the learning process by referencing past learning patterns and learning material information stored in the database, and generates individually customized learning plans.

[0632] Step 4:

[0633] The server sends the generated learning plan to the terminal, which then visually displays the plan to the user. The display includes daily assignments and learning progress guidelines.

[0634] Step 5:

[0635] The user begins learning according to the device's learning plan. During this process, the device uses sensors to recognize the user's facial expressions and voice through its emotion engine.

[0636] Step 6:

[0637] The emotion engine analyzes the user's emotional state in real time, and if it detects, for example, stress or anxiety, it sends instructions to the server to adjust the learning load.

[0638] Step 7:

[0639] Based on the analysis results of the emotion engine, the server dynamically adjusts the learning plan and provides the user on the device with appropriate feedback and encouraging messages.

[0640] Step 8:

[0641] The server periodically generates comprehension tests and delivers them to users via their devices. The tests include questions tailored to the user's current learning progress.

[0642] Step 9:

[0643] Users take comprehension tests on their devices, and the results are sent back to the server. This allows the server to understand the user's learning progress and identify areas for improvement.

[0644] Step 10:

[0645] The server analyzes the test results, generates feedback that takes into account the user's emotional state, and notifies the user via the device. This feedback includes specific areas for improvement and suggestions for further learning.

[0646] Step 11:

[0647] Users can participate in learning communities via their devices, exchanging information and answering questions with other learners. Feedback from the community is also processed by an emotion engine and displayed in a way that is appropriate for the user.

[0648] Step 12:

[0649] The server periodically collects user learning and sentiment data and provides it to the device as a dashboard. This visualizes the overall progress of learning and helps users maintain motivation.

[0650] (Example 2)

[0651] 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."

[0652] In online learning, users rely on the creation of appropriate learning plans, progress management, and feedback tailored to their level of understanding. However, traditional systems have difficulty flexibly responding to users' emotional states and individual progress, making it difficult to maximize learning effectiveness. Therefore, there is a need for systems that enable dynamic adjustment of learning plans according to each user's situation and effective information sharing.

[0653] 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.

[0654] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule; an information processing device that analyzes the user's emotional state in real time and dynamically adjusts the learning plan; and an information processing device that provides interaction functions enabling information sharing and responses to questions among users. This makes it possible to flexibly adjust the user's learning experience according to individual needs, enabling a more effective and communicative learning environment.

[0655] An "information processing device" is a computer system that receives and analyzes data and performs specific tasks.

[0656] A "learning plan" is a planned framework that defines the content and order in which to be learned, based on the user's goals and schedule.

[0657] "Progress management" is the process of understanding and appropriately managing a user's learning progress according to their learning plan.

[0658] A "test" is an evaluation method designed to assess a user's level of understanding.

[0659] "Feedback" is a method of providing information about learning progress and pointing out areas for improvement based on test results and user requests.

[0660] "Emotional state" refers to the user's mental state, primarily consisting of psychological tendencies and reactions inferred from facial expressions and tone of voice.

[0661] "Real-time analysis" refers to the process of collecting data and providing results immediately.

[0662] The "communication function" is a feature that provides an interactive platform for users to share information and answer questions.

[0663] "Dynamic adjustment" refers to a system automatically changing its operation or plan in response to specific conditions or changes in data.

[0664] This invention is a system that utilizes an information processing device to optimize the user experience in online learning. The system has a basic configuration of a server and a terminal, and includes various sensors and a generative AI model. This enables the generation of learning plans tailored to the user's goals and schedule, real-time monitoring of emotional state, and provision of appropriate feedback.

[0665] The server, acting as an information processing device, generates learning plans based on the user's objectives and schedule using an AI model. This model is based on natural language processing technology and selects the most suitable learning materials and content for the user's learning objectives. The server constructs the learning plan based on these materials and also stores data used to dynamically adjust the plan.

[0666] The device provides an interface with the user, analyzing the user's emotional state using sensors such as cameras and microphones, and transmitting this data to the server in real time. Based on this, the server interprets the user's current state using an emotion engine and adjusts the learning plan as needed. This makes it possible to provide a more optimal learning environment for the user.

[0667] For example, if a user is studying English while feeling nervous before an exam, the server can recognize their facial expressions through the camera and use a generative AI model to send encouraging messages to their device to help them relax. This can improve learning efficiency.

[0668] A concrete example of a prompt would be: "The user's learning objective is to improve their English proficiency, and their goal is to pass next month's exam. If the emotion engine detects user stress, what adjustments to the learning plan or messages should be generated?" This prompt allows the system to quickly grasp the user's individual needs and provide appropriate responses.

[0669] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0670] Step 1:

[0671] The server collects information from the user regarding their learning objectives and schedule. This information is sent via forms or applications that the user fills out. The server stores the received data in a database and prepares it as input data for the generated AI model. Specifically, when the user clicks the "Submit" button, the input information is transferred to the server.

[0672] Step 2:

[0673] The server generates a learning plan from the received user data using a generative AI model. This AI model utilizes natural language processing techniques to determine the optimal learning content and progression order. It takes the user's objectives and schedule as input and creates a learning plan with set priorities as output. Specifically, the server refers to the learning material database and saves the generated plan back into the database.

[0674] Step 3:

[0675] The server sends the generated learning plan to the terminal. The terminal receives this plan and displays it visually in the user interface. It receives the learning plan as input and displays it on the screen in a user-friendly format as output. Specifically, the terminal displays a notification pop-up and provides a link to view the detailed plan.

[0676] Step 4:

[0677] The device collects the user's facial expressions and voice through sensors and sends them to the server. User emotion data is mainly obtained from the camera and microphone. The server receives this data and performs real-time analysis using an emotion engine. Based on this input data, it infers the user's psychological state and outputs a specific emotional state. Specifically, the device periodically activates the camera and captures the user's face.

[0678] Step 5:

[0679] The server dynamically adjusts the learning plan based on the analysis results of the emotion engine. It receives the user's emotional state as input and generates a learning plan optimized for the user as output. Specifically, if stress is detected, it adds relaxing content to the plan and redistributes it to the device.

[0680] Step 6:

[0681] The server generates assessment tests tailored to the user's learning progress and delivers them to the user via the terminal. It creates test content based on learning progress data as input and provides the test as output. Specifically, the server selects test questions from specific educational resources and incorporates them into the test format.

[0682] Step 7:

[0683] The user takes a test delivered to their device and sends the results to the server. The system receives the user's test result data as input and generates feedback as output. Specifically, the user presses the "Start" button to display the test screen and submits their answers using the "Submit" button.

[0684] Step 8:

[0685] The server analyzes the received test results and generates feedback to facilitate user learning. This feedback includes advice that takes into account the user's emotional state. The generated feedback is sent to the terminal and provided to the user. Specifically, the server automatically aggregates the results and suggests areas for improvement based on the analysis.

[0686] (Application Example 2)

[0687] 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."

[0688] In online learning environments, a problem arises because uniform learning plans are provided that disregard the emotional state of each user, preventing them from receiving a learning experience tailored to their individual needs. Furthermore, the lack of technology to support learning while considering emotions in real time results in inefficiencies in learning efficiency and motivation.

[0689] 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.

[0690] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule, an analysis device that detects emotions and adjusts the learning plan, and an interaction device that outputs the generated message. This enables personalized learning support that responds to the user's emotional state.

[0691] An "information processing device" is a device that generates and provides an appropriate learning plan based on the user's objectives and schedule.

[0692] An "analysis device" is a device that has the function of detecting the user's emotions and adjusting the learning plan in real time.

[0693] A "dialogue device" is a device that outputs generated messages to the user, enabling two-way communication.

[0694] A "display device" is a device that visually shows a user's learning progress, allowing them to understand their learning status.

[0695] A "message generation device" is a device that generates messages to appropriately send encouragement and reminders to users.

[0696] The system for carrying out this invention includes multiple information processing devices to support the user's learning activities. First, the server generates an optimal learning plan based on the user's objectives and schedule. This plan is transmitted to the user's terminal and can be visually confirmed on a display device built into the terminal.

[0697] Furthermore, the server has an emotion detection and analysis device that recognizes the user's emotional state in real time from their facial expressions and voice. Specifically, it analyzes data collected using a camera and microphone to determine the user's stress level and concentration level. For example, it could utilize a platform commonly used as an emotion recognition API. Based on this data, the server dynamically adjusts the learning plan to provide a learning environment that is more suitable for the user's state.

[0698] The dialogue device outputs generated messages directly to the user, enabling two-way communication. Through the generating AI, it can provide encouragement and specific learning advice, thereby improving the user's learning efficiency. This prevents learning from becoming monotonous and allows for more active learning.

[0699] As a concrete example, if the server detects a distressed expression on a user's face while they are studying for an exam, it generates an encouraging message such as "Relax and think it through slowly" and outputs it through the interactive device. This helps the user to alleviate tension and continue studying efficiently.

[0700] Furthermore, as an example of a prompt message for the generating AI, instructions can be given to the AI ​​in the form of, "The user is showing signs of anxiety while preparing for the exam. Please generate an appropriate message of encouragement for this situation."

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The server receives the user's objectives and schedule, and generates a learning plan based on this information. The input is the user's learning goals and schedule information, and an algorithm is used to formulate a personalized learning plan based on this information. The output is the generated learning plan, which is stored in the database.

[0704] Step 2:

[0705] The terminal receives the learning plan from the server and displays it visually to the user. The input is the learning plan data from the server, and the output is the visualization of the learning plan on the terminal's screen.

[0706] Step 3:

[0707] The server activates an analysis device to recognize the user's emotional state, collecting facial and audio data using a camera and microphone. This sensor data is the input, which is analyzed by an algorithm, and the user's emotional state is output.

[0708] Step 4:

[0709] The server dynamically adjusts the learning plan based on the user's emotional state obtained through analysis. The input is the current learning plan and the user's emotional state, and the output is the adjusted learning plan. This result is then sent back to the terminal.

[0710] Step 5:

[0711] The dialogue device uses a generative AI model to generate encouraging and advice messages for the user, outputting them as voice or text. The input consists of information about the user's emotional state and learning progress; prompts are sent to the generative AI to output messages.

[0712] Step 6:

[0713] The user receives messages from the dialogue device and learns accordingly. User feedback is also analyzed, and the system records it in a database for future adjustments.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Fourth Embodiment]

[0718] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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".

[0731] This invention is a system that supports users' online learning and is primarily executed via a server and a terminal. The server generates a learning plan based on the user's goals and schedule. This plan is optimized to the individual user's needs and delivered to the terminal as a specific learning schedule.

[0732] As the learning plan is executed, the server periodically generates tests to assess understanding. When the user takes these tests, feedback is generated based on the results. This feedback is integrated into the user's learning plan, optimizing the individual learning experience while adjusting progress.

[0733] Furthermore, the server provides community features to facilitate information sharing and question-and-answer sessions among users. Users can access the community through their devices and interact with other learners. This reduces feelings of isolation during learning and promotes peer learning.

[0734] The server also sends data to the device to visually display the user's learning progress. The device receives this data and helps maintain the user's motivation by showing them their level of achievement. Furthermore, the server generates encouraging and reminder messages for the user at appropriate times and notifies them through the device.

[0735] For example, if a user wants to prepare for an English exam, the system will create a study plan considering the user's target exam date and the required study time. The plan will include daily study tasks, periodic mini-tests, and feedback to check progress. The user can follow this plan and progress while receiving notifications from their device. At the same time, if questions arise during their studies, they can ask other learners through the community and deepen their understanding by receiving answers.

[0736] As a result, users can achieve personalized online learning tailored to their own pace and receive efficient and effective support to achieve their goals.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The user logs into their device and enters their learning objectives, goals they want to achieve, and available time.

[0740] Step 2:

[0741] The terminal sends the entered user information to the server.

[0742] Step 3:

[0743] The server queries an internal database and analyzes past learning data and trends to generate an optimal learning plan based on the user's learning objectives and schedule.

[0744] Step 4:

[0745] The server creates an optimized learning plan and sends it to the terminal.

[0746] Step 5:

[0747] The device displays the user's learning plan and notifies them of daily learning tasks.

[0748] Step 6:

[0749] The server periodically generates quizzes and tests to assess the user's understanding and sends that information to the terminal.

[0750] Step 7:

[0751] The user takes a test presented on their device and enters their answers.

[0752] Step 8:

[0753] The device sends the user's answer results to the server.

[0754] Step 9:

[0755] The server analyzes the answer results, evaluates the user's level of understanding, and generates feedback.

[0756] Step 10:

[0757] The device notifies the user of feedback received from the server, displaying areas for improvement and the next learning task.

[0758] Step 11:

[0759] Users access the learning community through their devices to ask questions and exchange information.

[0760] Step 12:

[0761] The server manages user interaction data and provides terminals with statistics and information to facilitate peer learning.

[0762] Step 13:

[0763] The server evaluates the user's progress, generates visual data, and sends it to the terminal.

[0764] Step 14:

[0765] The device displays graphs and dashboards that visually show the user's learning progress and achievements.

[0766] Step 15:

[0767] The server will generate encouraging and reminder messages for the user as needed and notify their device.

[0768] (Example 1)

[0769] 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".

[0770] In online learning, for individual learners to efficiently and effectively achieve their goals, they need customized learning plans, appropriate assessment of understanding, progress-based feedback, a platform for information sharing, and mechanisms to support the continuity of learning. However, traditional methods have fragmented these elements, making it difficult for learners to receive consistent support.

[0771] 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.

[0772] In this invention, the server includes computation means for generating an educational plan based on the user's goals and schedule, computation means for monitoring progress according to the generated educational plan, and computation means for creating tests to periodically evaluate the user's level of understanding. This enables the provision of personalized learning support.

[0773] A "user" is an individual or group that uses the system for the purpose of online learning.

[0774] A "goal" is the learning content or skill level that a user hopes to achieve through their learning process.

[0775] "Schedule" refers to scheduling information that indicates the time or period that a user allocates to learning.

[0776] An "educational plan" is a teaching guide that outlines how to proceed with learning, optimized to the user's goals and schedule.

[0777] "Computational means" refers to a method consisting of a computer or program used to perform a specific process or function.

[0778] "Progress" is an indicator that shows the degree of achievement a user has made in the process of learning according to their educational plan.

[0779] "Monitoring" is the act of tracking progress according to the plan and making adjustments in real time as needed.

[0780] "Testing" refers to an evaluation method used to measure a user's level of understanding, and is typically conducted in the form of a test or quiz.

[0781] "Collaboration features" refer to online communication methods designed to facilitate information sharing and question-and-answer sessions among users.

[0782] "Visually displaying" refers to showing learning data, such as progress and achievement levels, in a way that users can intuitively understand.

[0783] A "message generation method" is a function that creates messages to provide users with notifications and encouragement at the appropriate time.

[0784] This invention is a system that supports users' online learning and is implemented via a server and a terminal, which are its main components.

[0785] The server uses a generative AI model to generate personalized learning plans based on the goals and schedules provided by the user. This generative AI model is used to design optimal learning content that aligns with the user's specific needs and schedule.

[0786] The server also monitors progress according to the plan and creates tests to periodically assess the user's understanding. Based on the results of these tests, the server generates feedback and incorporates it into the plan to optimize the user's learning experience.

[0787] The device receives educational plans and feedback sent from the server and presents them to the user in an intuitively understandable format. The device also visually displays learning progress and provides timely reminders and encouraging messages to maintain user motivation.

[0788] Users can progress through their learning by following educational plans provided from the server via their devices. Furthermore, users can deepen their learning by sharing information with other users and asking questions using collaborative features.

[0789] For example, if a user wants to prepare for an English exam, the server creates a study plan working backward from the exam date, incorporating daily learning tasks and periodic checks to assess understanding. Following this plan, the user can efficiently progress through their studies while receiving notifications on their device. If questions arise during learning, they can ask other learners through the collaborative function and receive answers.

[0790] Example of a prompt:

[0791] "Please create a three-month study plan to prepare for the next English exam. I would like to allocate one hour of study time per day and include a comprehension test once a week."

[0792] The above describes specific embodiments for carrying out this invention.

[0793] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0794] Step 1:

[0795] Users enter necessary information such as learning goals and schedules through an online platform. This sends the user's learning needs to the server. Specifically, users enter the exam date, desired study time, and the conditions necessary to achieve their goals on a web form.

[0796] Step 2:

[0797] The server inputs information received from the user into a generating AI model to create a personalized learning plan. This input includes learning objectives, exam dates, and available study time. Based on this data, the AI ​​designs an optimal learning schedule and delivers it to the user as a plan. The resulting learning plan includes daily tasks and scheduled periodic check-ups.

[0798] Step 3:

[0799] The server sends the generated learning plan to the user's device. This allows the user to begin learning based on the plan. Specifically, the server sends the plan data to the device, which then displays it visually in a calendar app or a dedicated app.

[0800] Step 4:

[0801] Users progress through their learning according to the educational plan using their devices and report their progress to the server. Questions and progress that arise during learning are sent to the server and recorded. Specifically, the system checks the user's progress after they complete a learning task and adjusts the schedule as needed.

[0802] Step 5:

[0803] The server periodically generates tests based on progress data. The input data is the user's progress, and the server designs tests to assess understanding based on this data. The output tests function as tools to measure the user's level of understanding.

[0804] Step 6:

[0805] The user takes a comprehension test presented on their device and sends the results to the server. Specifically, this involves the user completing the test and uploading the results to the server.

[0806] Step 7:

[0807] The server evaluates the received test results and generates feedback on the user's learning progress. This feedback is then sent back to the user's device to help improve their learning plan. Specifically, the server uses an analysis algorithm to evaluate the results and constructs feedback based on that analysis.

[0808] Step 8:

[0809] Users adjust their learning plans and optimize their progress based on feedback received from the server. Specifically, users view the feedback and set new goals and tasks.

[0810] Step 9:

[0811] The server enables information sharing among users through collaborative functions within the system and facilitates question-and-answer sessions between users. Specifically, the server monitors and manages forums and chat platforms to support smooth communication.

[0812] Step 10:

[0813] The device visually displays the user's learning progress and, as needed, displays notifications and encouraging messages from the server. Specifically, it displays progress on a dashboard and provides motivational messages via pop-ups.

[0814] (Application Example 1)

[0815] 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".

[0816] In online learning environments, the challenge lies in effectively managing learning plans and progress according to individual learning needs, while also stimulating communication among learners and improving their motivation to learn.

[0817] 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.

[0818] In this invention, the server includes means for generating a learning plan based on the user's goals and schedule, means for managing progress according to the generated learning plan, and means for providing personalized motivational notifications. This enables individual learners to learn efficiently at their own pace and deepen their learning while interacting with other learners.

[0819] A "user" refers to a learner who uses this system, and is an individual with their own learning needs and goals.

[0820] A "learning plan" refers to a set of plans, including learning content and schedules, created based on the user's goals and schedule.

[0821] "Progress management" refers to the process of monitoring the user's learning progress according to the generated learning plan and making appropriate adjustments.

[0822] A "test" refers to a set of questions or problems generated to periodically assess a user's level of understanding.

[0823] "Feedback" refers to information provided to users based on the evaluation results of tests, including areas for improvement and progress updates.

[0824] "Community features" refer to functions that enable information sharing and question-and-answer sessions among users, and play a role in promoting interaction among learners.

[0825] "Encouragement notifications" refer to a feature that automatically sends encouraging or reminder messages to users to motivate them to continue learning.

[0826] An "interface" refers to a visual display method that allows users to visually check their learning progress.

[0827] The "feedback generation process" refers to a series of procedures for shaping and providing feedback that is adapted to the user's learning schedule.

[0828] The system that implements this application is designed to efficiently support learners' online learning. The server generates individualized learning plans according to the user's goals and schedule. The plan creation uses Python and Django to build a backend system, leveraging optimization algorithms to create personalized learning schedules.

[0829] The generated learning plan is provided to the user through a smartphone interface developed with React Native. Here, the user can visually check their learning progress. This interface also serves to provide appropriate feedback based on their learning status.

[0830] The server periodically generates tests using machine learning libraries such as Scikit-learn and provides feedback based on the evaluation of those tests. Furthermore, it includes a feature to notify users with personalized motivational messages based on their learning progress and evaluation results.

[0831] Furthermore, to facilitate interaction among users, the server provides community features. These features allow users to exchange information and ask questions with other learners, making it easier to resolve any doubts they may have during their studies.

[0832] For example, in the case of a learner aiming to prepare for an English exam, the system receives the exam date and current score target as input and presents daily learning items. By including weekly practice tests in the schedule, learning can proceed more systematically and efficiently. Furthermore, learners can seek advice on the listening section from other learners through the community function.

[0833] Examples of prompts for the generative AI model include, "Please provide an algorithm that takes a user's target exam date and builds a personalized study schedule," and "Please suggest the most effective learning content and delivery methods for TOEIC test preparation." This enables support in providing learners with the optimal learning experience.

[0834] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0835] Step 1:

[0836] The server receives data on the user's goals and schedule. Based on this information, it generates a learning plan using Python. Here, an optimization algorithm is used to design the schedule best suited to the user. The input to this process is the user's goals and schedule information, and the output is a customized learning plan.

[0837] Step 2:

[0838] The server generates a learning plan and delivers it to the device. The device uses React Native to visually display the plan details so that the user can review them. At this stage, the input is the learning plan data from the server, and the output is a visualized learning schedule on the user interface.

[0839] Step 3:

[0840] The server periodically generates tests to evaluate the user's learning progress. It uses the machine learning library Scikit-learn to create test questions based on evaluation metrics. The input is user progress data, and the output is an evaluation test tailored to each individual user.

[0841] Step 4:

[0842] The user answers the test via a device. The device sends the received answers to the server. The server takes this answer data as input and performs evaluation calculations, including correctness determination. The output is the evaluation result, which is used to generate feedback.

[0843] Step 5:

[0844] The server generates feedback based on the evaluation results. This feedback includes adjustments and motivational messages for future learning. In this process, the evaluation results are the input, and the personalized feedback is the output.

[0845] Step 6:

[0846] The device notifies the user with feedback, including encouragement and reminders for learning achievements, to promote the user's continued learning. This allows the user to check their learning style and progress and move on to the next step.

[0847] Step 7:

[0848] The server provides community features to enable information sharing among users. Users can use their devices to interact with other learners, gaining new perspectives and knowledge through questions and answers. The introduction of this feature creates a space for users to share their own experiences.

[0849] 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.

[0850] This invention is a system that combines an emotion engine to improve the user's online learning experience. This system consists of a server that generates an optimal learning plan based on the user's goals and schedule, and a terminal used by the user. The server considers the user's learning objectives and schedule to create a learning plan tailored to their individual needs. This plan is transmitted to the terminal and visually confirmed by the user.

[0851] The emotion engine recognizes the user's emotions in real time and dynamically adjusts the learning plan and feedback according to the user's emotional state. This emotion recognition is performed by analyzing the user's facial expressions and tone of voice using sensors such as cameras and microphones. For example, if the user is feeling stressed, the server adjusts the plan to reduce the learning load and provide a more relaxed learning environment.

[0852] During the learning process, the server periodically generates tests to assess the user's understanding and delivers them to the user via their device. The user takes the test, and the answers are sent to the server. The server analyzes the answers and provides feedback based on the evaluation results. This feedback takes into account the user's emotional state and includes appropriate advice and encouraging messages.

[0853] Furthermore, emotional information recognized by the emotion engine is also utilized in community features to facilitate communication among users. The server suggests appropriate information sharing and question-and-answer sessions within the system. The terminal displays this information on a visual dashboard, supporting users in effectively interacting with their learning partners.

[0854] As a concrete example, let's assume a user is studying for an exam. If the emotion engine detects that this user is feeling anxious before the exam, the server generates an encouraging message to alleviate that anxiety and notifies the user through their device. Furthermore, if the user is struggling with a particular problem, the server provides feedback, including advice on adjusting their study pace.

[0855] In this way, the present invention provides a system that takes into account the user's emotional state and improves their individual learning experience. Furthermore, by incorporating an emotion engine, users can achieve a more comfortable and effective learning environment.

[0856] The following describes the processing flow.

[0857] Step 1:

[0858] Users log in to their devices and enter the subjects they want to study, their goals, and the time they have available. This information may include upcoming exams or long-term learning goals.

[0859] Step 2:

[0860] The terminal collects user input information and sends it to the server. The server uses this information to formulate a learning plan.

[0861] Step 3:

[0862] The server optimizes the learning process by referencing past learning patterns and learning material information stored in the database, and generates individually customized learning plans.

[0863] Step 4:

[0864] The server sends the generated learning plan to the terminal, which then visually displays the plan to the user. The display includes daily assignments and learning progress guidelines.

[0865] Step 5:

[0866] The user begins learning according to the device's learning plan. During this process, the device uses sensors to recognize the user's facial expressions and voice through its emotion engine.

[0867] Step 6:

[0868] The emotion engine analyzes the user's emotional state in real time, and if it detects, for example, stress or anxiety, it sends instructions to the server to adjust the learning load.

[0869] Step 7:

[0870] Based on the analysis results of the emotion engine, the server dynamically adjusts the learning plan and provides the user on the device with appropriate feedback and encouraging messages.

[0871] Step 8:

[0872] The server periodically generates comprehension tests and delivers them to users via their devices. The tests include questions tailored to the user's current learning progress.

[0873] Step 9:

[0874] Users take comprehension tests on their devices, and the results are sent back to the server. This allows the server to understand the user's learning progress and identify areas for improvement.

[0875] Step 10:

[0876] The server analyzes the test results, generates feedback that takes into account the user's emotional state, and notifies the user via the device. This feedback includes specific areas for improvement and suggestions for further learning.

[0877] Step 11:

[0878] Users can participate in learning communities via their devices, exchanging information and answering questions with other learners. Feedback from the community is also processed by an emotion engine and displayed in a way that is appropriate for the user.

[0879] Step 12:

[0880] The server periodically collects user learning and sentiment data and provides it to the device as a dashboard. This visualizes the overall progress of learning and helps users maintain motivation.

[0881] (Example 2)

[0882] 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".

[0883] In online learning, users rely on the creation of appropriate learning plans, progress management, and feedback tailored to their level of understanding. However, traditional systems have difficulty flexibly responding to users' emotional states and individual progress, making it difficult to maximize learning effectiveness. Therefore, there is a need for systems that enable dynamic adjustment of learning plans according to each user's situation and effective information sharing.

[0884] 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.

[0885] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule; an information processing device that analyzes the user's emotional state in real time and dynamically adjusts the learning plan; and an information processing device that provides interaction functions enabling information sharing and responses to questions among users. This makes it possible to flexibly adjust the user's learning experience according to individual needs, enabling a more effective and communicative learning environment.

[0886] An "information processing device" is a computer system that receives and analyzes data and performs specific tasks.

[0887] A "learning plan" is a planned framework that defines the content and order in which to be learned, based on the user's goals and schedule.

[0888] "Progress management" is the process of understanding and appropriately managing a user's learning progress according to their learning plan.

[0889] A "test" is an evaluation method designed to assess a user's level of understanding.

[0890] "Feedback" is a method of providing information about learning progress and pointing out areas for improvement based on test results and user requests.

[0891] "Emotional state" refers to the user's mental state, primarily consisting of psychological tendencies and reactions inferred from facial expressions and tone of voice.

[0892] "Real-time analysis" refers to the process of collecting data and providing results immediately.

[0893] The "communication function" is a feature that provides an interactive platform for users to share information and answer questions.

[0894] "Dynamic adjustment" refers to a system automatically changing its operation or plan in response to specific conditions or changes in data.

[0895] This invention is a system that utilizes an information processing device to optimize the user experience in online learning. The system has a basic configuration of a server and a terminal, and includes various sensors and a generative AI model. This enables the generation of learning plans tailored to the user's goals and schedule, real-time monitoring of emotional state, and provision of appropriate feedback.

[0896] The server, acting as an information processing device, generates learning plans based on the user's objectives and schedule using an AI model. This model is based on natural language processing technology and selects the most suitable learning materials and content for the user's learning objectives. The server constructs the learning plan based on these materials and also stores data used to dynamically adjust the plan.

[0897] The device provides an interface with the user, analyzing the user's emotional state using sensors such as cameras and microphones, and transmitting this data to the server in real time. Based on this, the server interprets the user's current state using an emotion engine and adjusts the learning plan as needed. This makes it possible to provide a more optimal learning environment for the user.

[0898] For example, if a user is studying English while feeling nervous before an exam, the server can recognize their facial expressions through the camera and use a generative AI model to send encouraging messages to their device to help them relax. This can improve learning efficiency.

[0899] A concrete example of a prompt would be: "The user's learning objective is to improve their English proficiency, and their goal is to pass next month's exam. If the emotion engine detects user stress, what adjustments to the learning plan or messages should be generated?" This prompt allows the system to quickly grasp the user's individual needs and provide appropriate responses.

[0900] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0901] Step 1:

[0902] The server collects information from the user regarding their learning objectives and schedule. This information is sent via forms or applications that the user fills out. The server stores the received data in a database and prepares it as input data for the generated AI model. Specifically, when the user clicks the "Submit" button, the input information is transferred to the server.

[0903] Step 2:

[0904] The server generates a learning plan from the received user data using a generative AI model. This AI model utilizes natural language processing techniques to determine the optimal learning content and progression order. It takes the user's objectives and schedule as input and creates a learning plan with set priorities as output. Specifically, the server refers to the learning material database and saves the generated plan back into the database.

[0905] Step 3:

[0906] The server sends the generated learning plan to the terminal. The terminal receives this plan and displays it visually in the user interface. It receives the learning plan as input and displays it on the screen in a user-friendly format as output. Specifically, the terminal displays a notification pop-up and provides a link to view the detailed plan.

[0907] Step 4:

[0908] The device collects the user's facial expressions and voice through sensors and sends them to the server. User emotion data is mainly obtained from the camera and microphone. The server receives this data and performs real-time analysis using an emotion engine. Based on this input data, it infers the user's psychological state and outputs a specific emotional state. Specifically, the device periodically activates the camera and captures the user's face.

[0909] Step 5:

[0910] The server dynamically adjusts the learning plan based on the analysis results of the emotion engine. It receives the user's emotional state as input and generates a learning plan optimized for the user as output. Specifically, if stress is detected, it adds relaxing content to the plan and redistributes it to the device.

[0911] Step 6:

[0912] The server generates assessment tests tailored to the user's learning progress and delivers them to the user via the terminal. It creates test content based on learning progress data as input and provides the test as output. Specifically, the server selects test questions from specific educational resources and incorporates them into the test format.

[0913] Step 7:

[0914] The user takes a test delivered to their device and sends the results to the server. The system receives the user's test result data as input and generates feedback as output. Specifically, the user presses the "Start" button to display the test screen and submits their answers using the "Submit" button.

[0915] Step 8:

[0916] The server analyzes the received test results and generates feedback to facilitate user learning. This feedback includes advice that takes into account the user's emotional state. The generated feedback is sent to the terminal and provided to the user. Specifically, the server automatically aggregates the results and suggests areas for improvement based on the analysis.

[0917] (Application Example 2)

[0918] 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".

[0919] In online learning environments, a problem arises because uniform learning plans are provided that disregard the emotional state of each user, preventing them from receiving a learning experience tailored to their individual needs. Furthermore, the lack of technology to support learning while considering emotions in real time results in inefficiencies in learning efficiency and motivation.

[0920] 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.

[0921] In this invention, the server includes an information processing device that generates a learning plan based on the user's objectives and schedule, an analysis device that detects emotions and adjusts the learning plan, and an interaction device that outputs the generated message. This enables personalized learning support that responds to the user's emotional state.

[0922] An "information processing device" is a device that generates and provides an appropriate learning plan based on the user's objectives and schedule.

[0923] An "analysis device" is a device that has the function of detecting the user's emotions and adjusting the learning plan in real time.

[0924] A "dialogue device" is a device that outputs generated messages to the user, enabling two-way communication.

[0925] A "display device" is a device that visually shows a user's learning progress, allowing them to understand their learning status.

[0926] A "message generation device" is a device that generates messages to appropriately send encouragement and reminders to users.

[0927] The system for carrying out this invention includes multiple information processing devices to support the user's learning activities. First, the server generates an optimal learning plan based on the user's objectives and schedule. This plan is transmitted to the user's terminal and can be visually confirmed on a display device built into the terminal.

[0928] Furthermore, the server has an emotion detection and analysis device that recognizes the user's emotional state in real time from their facial expressions and voice. Specifically, it analyzes data collected using a camera and microphone to determine the user's stress level and concentration level. For example, it could utilize a platform commonly used as an emotion recognition API. Based on this data, the server dynamically adjusts the learning plan to provide a learning environment that is more suitable for the user's state.

[0929] The dialogue device outputs generated messages directly to the user, enabling two-way communication. Through the generating AI, it can provide encouragement and specific learning advice, thereby improving the user's learning efficiency. This prevents learning from becoming monotonous and allows for more active learning.

[0930] As a concrete example, if the server detects a distressed expression on a user's face while they are studying for an exam, it generates an encouraging message such as "Relax and think it through slowly" and outputs it through the interactive device. This helps the user to alleviate tension and continue studying efficiently.

[0931] Furthermore, as an example of a prompt message for the generating AI, instructions can be given to the AI ​​in the form of, "The user is showing signs of anxiety while preparing for the exam. Please generate an appropriate message of encouragement for this situation."

[0932] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0933] Step 1:

[0934] The server receives the user's objectives and schedule, and generates a learning plan based on this information. The input is the user's learning goals and schedule information, and an algorithm is used to formulate a personalized learning plan based on this information. The output is the generated learning plan, which is stored in the database.

[0935] Step 2:

[0936] The terminal receives the learning plan from the server and displays it visually to the user. The input is the learning plan data from the server, and the output is the visualization of the learning plan on the terminal's screen.

[0937] Step 3:

[0938] The server activates an analysis device to recognize the user's emotional state, collecting facial and audio data using a camera and microphone. This sensor data is the input, which is analyzed by an algorithm, and the user's emotional state is output.

[0939] Step 4:

[0940] The server dynamically adjusts the learning plan based on the user's emotional state obtained through analysis. The input is the current learning plan and the user's emotional state, and the output is the adjusted learning plan. This result is then sent back to the terminal.

[0941] Step 5:

[0942] The dialogue device uses a generative AI model to generate encouraging and advice messages for the user, outputting them as voice or text. The input consists of information about the user's emotional state and learning progress; prompts are sent to the generative AI to output messages.

[0943] Step 6:

[0944] The user receives messages from the dialogue device and learns accordingly. User feedback is also analyzed, and the system records it in a database for future adjustments.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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."

[0954] 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.

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] The following is further disclosed regarding the embodiments described above.

[0967] (Claim 1)

[0968] A computer means for generating a learning plan based on the user's objectives and schedule,

[0969] A computer means for managing progress according to a generated learning plan,

[0970] A computer means for generating tests to periodically evaluate the user's level of understanding,

[0971] A computer means that provides feedback based on evaluation results,

[0972] A computer system that provides community functions enabling information sharing and question-and-answer sessions among users,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, further comprising a display means for visually indicating the user's learning progress.

[0976] (Claim 3)

[0977] The system according to claim 1, further comprising means for generating messages to send encouragement or reminders to users.

[0978] "Example 1"

[0979] (Claim 1)

[0980] A calculation means for generating an educational plan based on the user's goals and schedule,

[0981] A calculation means for monitoring progress according to the generated educational plan,

[0982] A computational means for creating tests to periodically evaluate the user's level of understanding,

[0983] A computational means that provides feedback based on the evaluation results,

[0984] A computing means that provides a cooperative function enabling information exchange and question answering among users,

[0985] A system that includes this.

[0986] (Claim 2)

[0987] The system according to claim 1, further comprising a display means for visually displaying the user's learning progress.

[0988] (Claim 3)

[0989] The system according to claim 1, further comprising means for generating messages to send prompts or notifications to users.

[0990] "Application Example 1"

[0991] (Claim 1)

[0992] A computer means for generating a learning plan based on the user's objectives and schedule,

[0993] A computer means for managing progress according to a generated learning plan,

[0994] A computer means for generating tests to periodically evaluate the user's level of understanding,

[0995] A computer means that provides feedback based on evaluation results,

[0996] A computer system that provides community functions enabling information sharing and question-and-answer sessions among users,

[0997] A computer system that provides personalized motivational notifications based on learning progress and evaluation results,

[0998] A system that includes this.

[0999] (Claim 2)

[1000] The system according to claim 1, further comprising an interface for displaying the user's learning progress visually.

[1001] (Claim 3)

[1002] The system according to claim 1, comprising a process for generating feedback to adapt to a learning schedule.

[1003] "Example 2 of combining an emotion engine"

[1004] (Claim 1)

[1005] An information processing device that generates a learning plan based on the user's objectives and schedule,

[1006] An information processing device that manages progress according to the generated learning plan,

[1007] An information processing device that generates tests to periodically evaluate the user's level of understanding,

[1008] An information processing device that provides feedback based on evaluation results,

[1009] An information processing device that analyzes the user's emotional state in real time and dynamically adjusts the learning plan,

[1010] An information processing device that provides communication functions enabling information sharing and responses to inquiries among users,

[1011] A system that includes this.

[1012] (Claim 2)

[1013] The system according to claim 1, further comprising a display device for visually indicating the user's learning progress.

[1014] (Claim 3)

[1015] The system according to claim 1, further comprising a message generation device for sending encouragement or notifications to a user.

[1016] "Application example 2 when combining with an emotional engine"

[1017] (Claim 1)

[1018] An information processing device that generates a learning plan based on the user's objectives and schedule,

[1019] An information processing device that manages progress according to the generated learning plan,

[1020] An information processing device that generates tests to periodically evaluate the user's level of understanding,

[1021] An information processing device that provides feedback based on evaluation results,

[1022] An information processing device that provides community functions enabling information sharing and question answering among users,

[1023] An analysis device for detecting emotions and adjusting the learning plan,

[1024] An interactive device that outputs the generated message,

[1025] A system that includes this.

[1026] (Claim 2)

[1027] The system according to claim 1, further comprising a display device that visually indicates the user's learning progress.

[1028] (Claim 3)

[1029] The system according to claim 1, further comprising a message generation device for sending encouragement or reminders to users. [Explanation of Symbols]

[1030] 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 computer means for generating a learning plan based on the user's objectives and schedule, A computer means for managing progress according to a generated learning plan, A computer means for generating tests to periodically evaluate the user's level of understanding, A computer means that provides feedback based on evaluation results, A computer system that provides community functions enabling information sharing and question-and-answer sessions among users, A system that includes this.

2. The system according to claim 1, further comprising a display means for visually indicating the user's learning progress.

3. The system according to claim 1, further comprising means for generating messages to send encouragement or reminders to users.

Images