system

Abstract

We provide the system. [Solution] A plan generation means that generates an optimal learning plan based on goal and activity plan information received from learners, In the process of learning according to the generated learning plan, a progress management means is used to collect learner progress information, An assessment method that periodically provides questions to evaluate the learner's level of understanding and adjusts the learning content based on the results, A display means for displaying learners' learning outcomes and generating feedback to maintain motivation, A means of communication that creates a learning environment and supports information sharing with other learners, A recommendation method for optimizing learning plans based on learner progress information using a generative AI model, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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] Conventional online learning systems have problems in that it is difficult to create a flexible learning plan according to the individual goals and progress of learners. In addition, since there is a lack of a function to effectively evaluate the understanding level of learners and dynamically adjust the learning content based on the results, there are limitations in maximizing the learning effect. Furthermore, there is insufficient visualization means to sustain the motivation of learners, and there are also problems in providing a communication platform to reduce the sense of isolation.

Means for Solving the Problems

[0005] This invention addresses individual learning needs by providing a plan generation means that generates an optimal learning plan based on goal and schedule information received from learners. Furthermore, it improves learning effectiveness by managing progress information and adjusting learning content based on periodic test results using a comprehension evaluation means. It also includes a display means that visualizes learning outcomes and generates feedback, thereby maintaining learner motivation. In addition, it facilitates information sharing among learners through a communication means, reducing feelings of isolation.

[0006] A "learner" is an individual who is responsible for executing a learning plan and managing its progress.

[0007] A "goal" is a specific outcome or target that learners want to achieve.

[0008] "Schedule information" refers to information about how learners can allocate their time for studying.

[0009] A "learning plan" is a guideline created based on the learner's goals and schedule, outlining the content to be studied and the order in which it should be studied.

[0010] A "plan generation tool" is a mechanism for deriving the optimal learning plan based on information from learners.

[0011] "Progress information" refers to data that shows how well learners are progressing according to their learning plan.

[0012] A "progress management system" is a mechanism for recording and evaluating a learner's learning progress.

[0013] "Comprehension level" is an indicator that shows how well learners understand the material they are studying.

[0014] "Evaluation methods" refer to methods for measuring learners' level of understanding and appropriately adjusting the learning content based on the results.

[0015] "Presentation means" is a tool for visually presenting the learning achievements and feedback of learners.

[0016] "Feedback" is information including the evaluation results and advice on the learning achievements of learners.

[0017] "Communication means" is a method for learners to share information and communicate with each other.

Brief Explanation of Drawings

[0018] [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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] 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.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention aims to improve learning efficiency by providing an optimal learning environment tailored to the individual learning needs of learners using an online learning support system. Specific embodiments are shown below.

[0040] First, the server receives goal and schedule information from the learner and stores it in a database. Next, it uses a plan generation system to create an optimal learning plan tailored to the learner's goals. This plan includes learning content, learning sequence, and progress checkpoints.

[0041] The terminal presents the learning plan, sent from the server, to the learner through a user interface. The learner can progress through the learning process using this interface, and the terminal further records the learner's progress using a progress management system. This information is sent to the server, and an assessment of comprehension is performed based on the progress of the plan.

[0042] The server periodically provides quizzes and tests to assess learners' understanding. Upon receiving the results from learners, the server analyzes them using comprehension assessment tools and suggests the next learning steps. The learning plan is dynamically adjusted as needed during this process.

[0043] The device also displays feedback and learning outcomes sent from the server. This allows learners to visualize their learning progress and achievements, helping them maintain their motivation.

[0044] Furthermore, the means of supporting communication allow learners to share information with other learners, post questions, and answer them. This feature contributes to reducing feelings of isolation and deepening the understanding of the learning material.

[0045] For example, if a learner sets the goal of "passing an English proficiency test," the server analyzes the learner's available schedule and assigns weekly learning content (vocabulary, grammar, listening, etc.). Based on this plan, the learner proceeds with their daily studies, and the server incorporates feedback into the next learning content based on the results of regularly administered mini-tests. As a result, the system provides support for achieving goals efficiently and effectively.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user logs into the learning support system and enters their goals and planned study time. The device then sends this information to the server.

[0049] Step 2:

[0050] The server stores the received goal and schedule information in a database. Based on this information, it activates the plan generation mechanism to create a learning plan.

[0051] Step 3:

[0052] The server uses machine learning algorithms to analyze past learning history and learning trends, and generates an optimal learning plan for the learner.

[0053] Step 4:

[0054] The generated learning plan is sent from the server to the terminal, which then displays it in the user interface. The user reviews the presented learning plan and proceeds with their learning accordingly.

[0055] Step 5:

[0056] As users progress through their learning, they use a progress management system to input their learning progress into their device.

[0057] Step 6:

[0058] The terminal sends progress information to the server. The server stores the progress information in a database and evaluates the learner's level of understanding using a comprehension evaluation tool.

[0059] Step 7:

[0060] The server periodically creates and provides tests to users to assess their understanding. Based on these results, the learning progress and level of understanding are re-evaluated.

[0061] Step 8:

[0062] The server adjusts the learning plan based on test results and progress information, and updates the next learning content. The updated plan is sent to the terminal.

[0063] Step 9:

[0064] The device displays feedback and analysis results from the server to the user. This allows the user to understand their learning progress and comprehension level, and use that information to guide their next learning steps.

[0065] Step 10:

[0066] If necessary, users can share information with other learners through their devices and use communication tools to ask questions and exchange opinions.

[0067] (Example 1)

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

[0069] There is a growing need to improve learners' learning efficiency by providing an optimal learning environment tailored to their individual learning needs. However, conventional systems have challenges in adequately creating optimized plans for each learner and efficiently collecting and analyzing progress information. This can lead to delays in achieving learning goals and a decline in motivation.

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

[0071] In this invention, the server includes a plan generation means for creating an optimal learning plan based on goal and schedule information obtained from the user, a progress management means for collecting user progress information as the user progresses through the learning according to the constructed learning plan, and an evaluation means for providing periodic tests to assess the user's understanding and improving the learning content based on the results. This makes it possible to provide an efficient and effective learning environment that meets the individual learning needs of learners.

[0072] A "plan generation means" is a component for creating a learning plan optimized for individual learning needs based on goal and schedule information obtained from the user.

[0073] A "progress management system" is a system component equipped with functions to efficiently collect and record the progress of a user as they proceed with their learning according to their learning plan.

[0074] "Evaluation methods" refer to methods and mechanisms for providing periodic tests to assess users' understanding, analyzing the results, and improving the learning content.

[0075] A "display mechanism" is a system that visualizes the user's learning outcomes and presents them efficiently to the user in order to maintain and promote their motivation to learn.

[0076] "Means of interaction" refer to elements that have the function of forming learning groups and supporting information sharing and communication among users.

[0077] "Interface means" refers to a user interface that visually presents the generated learning plan and progress, enabling users to operate it intuitively.

[0078] This invention is a system for supporting online learning environments and aims to provide an optimal learning plan according to the individual learning needs of learners. The following describes embodiments for carrying out this invention.

[0079] The server collects learning objectives and schedule information provided by users and stores this information in a database. Relational database management systems are often used as the database, specifically MySQL® or PostgreSQL. On the server, programs written in Python or Java® process the objectives and schedule information and function as a plan generation mechanism. This generates a learning plan that includes individual learning content and sequence.

[0080] The device presents the user with a learning plan sent from the server via a dedicated application. This application runs on commonly used mobile operating systems such as iOS and Android®. The user interface displayed on the device is built using React Native and designed for intuitive operation. Users use this interface to progress through their daily studies, and their progress is recorded.

[0081] User progress information is sent from the device to the server and periodically analyzed by comprehension assessment tools. Regular assessments are conducted using online testing platforms such as Google Forms and Quizlet. Based on the assessment results, the server dynamically adjusts the next learning steps and content to provide an optimal learning environment. This feedback helps maintain user motivation and effectively achieve learning goals.

[0082] For example, if a user has the goal of "improving their English vocabulary," the server analyzes their past learning history and suggests a vocabulary list and related quizzes to complete this week. The user can check their daily progress through their device and continue learning while receiving feedback from the server as needed.

[0083] An example of a prompt message to input into a generative AI model is shown below:

[0084] "Please suggest the best schedule for learning English vocabulary next week."

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server receives goal and schedule information from the user through an input form. This input data is processed by the server as an HTTP request and stored in a database. This process executes algorithms to verify the validity of the data and ensure that no invalid data is present. The output is normalized learning goals and schedule information stored in the database.

[0088] Step 2:

[0089] The server creates a learning plan using a plan generation mechanism based on stored goal and schedule information. Based on the goal data received as input, it determines the priority of learning content and selects practice problems and reference materials. An algorithm written in Python processes learning content accessible from the database and generates a plan as output, which includes specific learning steps and progress checkpoints.

[0090] Step 3:

[0091] The terminal displays the learning plan sent from the server in a user interface. The input here is the plan data received from the server, and the plan is visualized using a UI built with React Native. In this process, design templates are used to make it easy for the user to intuitively check their learning progress. The output is a display of the learning plan in a format that the user can review.

[0092] Step 4:

[0093] Users progress through their daily studies according to the learning plan presented via their device. As they progress, users record their progress. Inputs include the completion status of daily learning content and the time spent studying, which are stored on the device as progress management data. Output is a progress report synchronized with the server.

[0094] Step 5:

[0095] The server operates by evaluating understanding based on progress information. For example, it utilizes learning management methods to analyze test results obtained as input. Online platforms such as Google Forms are used for tests, and the results are sent back to the server as digital data. Based on this output, the server suggests adjustments to be applied to the next learning plan and designs a more appropriate and effective learning path.

[0096] Step 6:

[0097] The terminal displays feedback and learning outcome information obtained from the server in a user interface. The input is feedback data sent from the server, and the terminal visually presents the results. Based on this, the user can check their own learning performance and reset their goals. The output is a screen display visually showing the feedback.

[0098] (Application Example 1)

[0099] 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."

[0100] Conventional learning support systems have struggled to provide dynamically optimized learning plans based on individual learning goals and progress for each learner. Furthermore, they lacked mechanisms to efficiently support information sharing and interaction among learners, posing challenges to maintaining learner motivation and improving comprehension. Additionally, the use of AI to propose personalized learning processes has been insufficient, highlighting the need for improved learning efficiency.

[0101] 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.

[0102] In this invention, the server includes a plan generation means for generating an optimal learning plan based on goal and activity plan information received from the learner, a progress management means for collecting learner progress information, an evaluation means for providing problems to assess comprehension, and a recommendation means for optimizing the learning plan using a generated AI model. This enables dynamic and optimized learning support tailored to individual learning needs, allowing learners to efficiently achieve their goals.

[0103] A "learner" refers to an individual who uses a learning support system to improve their own knowledge and skills.

[0104] A "goal" is the specific outcome or skill acquisition objective that a learner wishes to achieve.

[0105] "Activity plan information" refers to information about the schedule and action plan necessary to achieve the goals set by the learner.

[0106] A "plan generation means" is a function that executes a process to create an optimal learning plan based on the learner's goals and activity plan information.

[0107] A "progress management system" is a function that continuously measures and records the learner's learning progress and achievement level.

[0108] "Evaluation methods" refer to functions that quantitatively or qualitatively measure the level of understanding based on the results of problems or tests provided to learners.

[0109] "Feedback" is the process of providing information that reflects learners' learning outcomes and improves their motivation.

[0110] "Communication tools" refer to functions that provide a platform or system for learners to share information and ask questions.

[0111] A "generative AI model" is an algorithm or model that uses artificial intelligence to analyze data and dynamically optimize the learning plan.

[0112] A "prompt statement" is an input statement used to prompt a generative AI model to produce a specific output or perform a particular action.

[0113] "Recommendation methods" refers to a feature that uses a generative AI model to suggest the optimal learning plan and next learning steps based on the learner's data.

[0114] The system implementing this invention consists of a server and a terminal, providing a learning environment optimized for the learner. The server receives goal and activity plan information from the learner and generates a learning plan using a generative AI model based on this information. Python and the Django framework are used as the plan generation means to perform data management and calculations.

[0115] The device visually presents the learning plan to the learner through a user interface and collects data based on user input. It uses a web browser or a dedicated application and employs React Native. For progress management, it tracks the learner's progress in real time and sends the data to the server.

[0116] The server analyzes progress information and learning history, and conducts periodic tests to assess comprehension. Based on the evaluation results, the evaluation system utilizes a generative AI model to dynamically optimize the learning plan by adjusting the next learning content. Furthermore, the generative AI model is trained on learning content using prompt sentences, and provides feedback and recommendations tailored to the learner.

[0117] For example, if a learner wants to learn a new language, the AI ​​will recommend a daily learning plan based on the user's set goals and present quizzes according to their progress. This measures their proficiency, and the next learning steps are suggested. Through this process, learners can efficiently achieve their goals.

[0118] As an example of a prompt to input into the generative AI model, we will use the text: "Generate an appropriate learning plan and tasks daily based on the goals set by the user. Analyze progress and suggest the next steps." Based on this prompt, the generative AI model will recommend appropriate learning content.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users input learning objectives and activity plan information using a terminal. This information is sent to the server as structured data by the terminal. Input includes details such as the user's goals and available schedule, while output is structured data sent to the server. The terminal provides a screen that allows users to input this information in an easy-to-understand format.

[0122] Step 2:

[0123] The server inputs the received data into a generating AI model and generates an optimized learning plan. This process uses prompts to instruct the AI ​​model to generate daily learning tasks and content. The input is formatted user data, and the output is a specific learning plan. The server saves the generated plan to a database.

[0124] Step 3:

[0125] The server sends the generated learning plan to the terminal. The terminal configures a display screen so that the learner can visually confirm the plan. The plan is organized in a way that facilitates progress. The input is the learning plan data received from the server, and the output is the display on the user interface.

[0126] Step 4:

[0127] Users progress through their learning on their device according to their learning plan. The device records the user's progress in real time and periodically sends it to the server. Input is the user's learning activity, and output is progress data. The device provides an easy-to-use interface to support learning habits.

[0128] Step 5:

[0129] The server uses evaluation tools to determine the level of understanding based on progress data and proposes the next learning step using a generative AI model. In this process, the AI ​​determines the optimal learning content using prompts. The input is progress data, and the output is the next learning suggestion. The AI ​​model has the ability to flexibly adapt to the user's characteristics.

[0130] Step 6:

[0131] The server sends the next learning plan to the terminal, which displays it in the user interface. The user receives feedback and obtains information that helps them continue learning. The input is information about the next learning step, and the output is the displayed feedback. The terminal presents information that includes elements to enhance the learner's motivation.

[0132] 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.

[0133] This invention provides more individually optimized learning support by combining an emotion engine that recognizes and analyzes learners' emotions with an online learning system. A specific embodiment is shown below.

[0134] First, learners log into the system and enter their goals and schedules. The terminal sends this information to the server, where it is stored in a database. The server uses this data to generate an optimal learning plan and activates the plan generation mechanism. Up to this point, it is the same as conventional systems, but the addition of an emotion engine provides more advanced learning support.

[0135] The emotion engine analyzes the learner's facial expressions and voice tone through sensors such as cameras and microphones built into the user's device, collecting emotion data in real time. The server receives this data and uses an emotion recognition algorithm to classify the learner's emotional state (for example, whether they are focused or stressed).

[0136] Based on this emotional information, the server suggests learning content and methods that are best suited to the learner's current state. For example, if a learner is feeling stressed, it can recommend more relaxing content and approaches. Conversely, if a high level of concentration is detected, it can suggest tasks with increased difficulty.

[0137] Furthermore, the server dynamically adjusts the learning plan and generates feedback using data analyzed by the emotion engine. This allows learners to visualize their learning progress and emotional changes, providing customized support to boost their motivation.

[0138] As a concrete example, suppose a learner has the goal of "learning a new language." The server creates a standard learning plan and displays it on the device, while simultaneously monitoring the learner's emotional state during learning using an emotion engine. If the server detects that the learner is feeling stressed while tackling complex grammatical points, it suggests switching to learning with simpler example sentences. This reduces the learner's stress and allows them to continue learning while maintaining motivation.

[0139] Thus, a learning support system that incorporates an emotional engine provides a learning environment tailored to the individual needs of learners, offering a means to achieve efficient and effective learning.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user logs into the online learning system and enters their learning goals and schedule into their device. The device then sends this information to the server.

[0143] Step 2:

[0144] The server stores the received goal and schedule information in a database. A plan generation mechanism is then activated to create a learning plan.

[0145] Step 3:

[0146] The plan generation system formulates an optimal learning plan based on the learner's goals, taking into account the content of the previous lesson. This plan is then transmitted to the terminal.

[0147] Step 4:

[0148] The device displays the received learning plan on the user interface. The user reviews the plan and begins their learning activities.

[0149] Step 5:

[0150] During learning, the device uses its built-in emotion engine to analyze the user's facial expressions and voice in real time. The results of this analysis are sent to the server as emotion data.

[0151] Step 6:

[0152] The server receives emotional data and uses an emotion recognition algorithm to classify the user's emotional state. For example, it determines states such as "concentrated," "stressed," and "relaxed."

[0153] Step 7:

[0154] The server adjusts the learning content based on the user's emotional state. If the user's emotions indicate stress, the learning plan is dynamically changed, for example, by switching to content more suitable for relaxation.

[0155] Step 8:

[0156] The adjusted learning plan is sent back to the device, and the user is presented with the new plan. This allows the user to continue with optimal learning activities tailored to their emotional state.

[0157] Step 9:

[0158] The server generates feedback based on the user's progress and sentiment data. The feedback is displayed on the device, allowing the user to track their learning progress and emotional changes.

[0159] Step 10:

[0160] If necessary, users can exchange opinions with other learners regarding sentiment data and progress, and communication features are supported. This allows users to gain new perspectives on their learning.

[0161] (Example 2)

[0162] 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".

[0163] In online learning systems, it has been difficult to appropriately recognize the emotional state of individual learners and provide learning support accordingly. Furthermore, there has been a lack of effective feedback to maintain learners' motivation and methods to visualize learning progress. This invention aims to solve these problems by providing a highly individualized learning environment that incorporates emotional data.

[0164] 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.

[0165] In this invention, the server includes a plan generation means, a progress management means, an emotion recognition means, a suggestion means, a display means, and an information transmission means. This makes it possible to provide an individually optimized learning plan according to the learner's emotional state and to support the effective progress of learning while maintaining the learner's motivation.

[0166] A "plan generation means" is a method or apparatus equipped with the function of generating an optimal learning plan based on goal and schedule information received from the learner.

[0167] "Progress management means" refers to a method or device for collecting and managing learner progress information during the learning process in accordance with the generated learning plan.

[0168] "Emotion recognition means" refers to a method or apparatus for collecting learner's emotional data via voice and image input devices, and for recognizing and classifying that emotional state.

[0169] "Proposed means" refers to a method or apparatus for providing learning content suitable for learners based on emotional information and for dynamically adjusting the learning plan.

[0170] "Display means" refers to a method or device for visualizing learners' learning outcomes and generating and displaying feedback to maintain motivation.

[0171] "Information transmission means" refers to methods or devices for forming learning communities and supporting information sharing among learners.

[0172] This specification provides an individually optimized online learning support system that takes learners' emotions into account. This system uses emotion recognition technology to analyze the learner's real-time emotional state and dynamically adjust the learning experience. The hardware and software used, data processing methods, and specific examples are described below.

[0173] Hardware and software

[0174] The device is equipped with a camera and microphone to collect the learner's facial expressions and voice. The device has emotion recognition software installed to analyze the collected audio and image data in real time.

[0175] The server is equipped with a database management system that stores goals, schedules, and sentiment data received from learners. The server also implements algorithms for classifying sentiment data and generative AI models.

[0176] Data processing and calculations

[0177] When a user enters their goals and schedule, the device sends this information to the server, where it is registered in the database.

[0178] Emotional data collected by the device is sent to a server and analyzed by an emotion recognition algorithm. This classifies the learner's emotional state into categories such as concentration, stress, and relaxation.

[0179] Based on emotional information, the server suggests learning content suitable for the learner and automatically adjusts the learning plan to provide an optimal learning environment.

[0180] Specific example

[0181] For example, suppose a learner has the goal of "learning a new language." While the learner is working on a difficult grammatical section, the device's camera and microphone detect signs of stress from the user's facial expressions and voice. The server then suggests switching to practicing simpler phrases and displays this information on the device.

[0182] Example of a prompt

[0183] When using a generative AI model, the prompt message used is "Please suggest ways to adjust the learning plan when the user is feeling stressed."

[0184] This system enables personalized learning support through real-time analysis of learners' emotions.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The user logs into the online learning platform and enters their goals and schedule information into their device. The device then sends the entered goals and schedule information to the server. The server records this information in a database and uses it as basic data for planning future learning. The input is the goals and schedule information, and the output is registration to the database.

[0188] Step 2:

[0189] The server executes a plan generation algorithm based on the database to generate the optimal learning plan for the learner. The generated learning plan takes the learner's goals and schedule into account, setting sequential learning steps. Here, learner information from the database is used as input data, and the output is a specific learning plan. The generated learning plan is sent to the terminal and presented to the user.

[0190] Step 3:

[0191] The user begins learning using a device equipped with a camera and microphone. The device's emotion recognition software collects the user's facial expressions and voice via the camera and microphone, and analyzes the emotion data in real time. The device software receives the collected audio and image data as input, uses an emotion recognition algorithm to determine a specific emotional state, and sends the result to the server. The output is the learner's emotional state.

[0192] Step 4:

[0193] The server analyzes the received emotional data and uses a generative AI model to classify the learner's emotional state. Based on this analysis, the server dynamically adjusts the learning plan. For example, it might suggest a more challenging task if the user is focused, or a more relaxed approach if they are stressed. The input is emotional data and the current learning plan, and the output is the adjusted learning content.

[0194] Step 5:

[0195] The server sends a tailored learning plan and feedback to the device, which then displays it to the user. The feedback includes advice based on the learner's progress and emotions. Users can review the feedback as they progress, maintaining motivation and continuing their learning. The output consists of visualized feedback information and a tailored learning plan.

[0196] (Application Example 2)

[0197] 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".

[0198] In online learning, providing individually optimized learning support tailored to each learner's state and emotions is difficult. Therefore, there is a need to recognize learners' emotional states in real time and flexibly adjust learning plans based on those emotions. However, conventional systems lack this kind of dynamic response. This results in a challenge in effectively alleviating learners' stress and decreased motivation.

[0199] 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.

[0200] In this invention, the server includes emotion recognition means for recognizing and analyzing the learner's emotional state, adjustment means for dynamically adjusting the learning content and method based on the learner's emotional information, and progress management means for collecting the learner's progress information. This makes it possible to provide a customized learning experience that is tailored to the learner's emotions and learning progress.

[0201] A "plan generation means" is an element that has the function of creating an optimal learning plan based on goal and schedule information received from the learner.

[0202] A "progress management tool" is an element that has the function of continuously collecting the learner's progress in the learning process, which proceeds according to the generated learning plan.

[0203] "Evaluation methods" refer to elements used to assess learners' understanding through periodic tests and to adjust learning content based on the results.

[0204] A "display means" is an element that has the function of generating feedback to maintain learning motivation by visually showing the learner's learning outcomes.

[0205] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the learner's emotional state using sensors such as cameras and microphones.

[0206] A "modification mechanism" is an element that has the function of flexibly changing the learning content and approach according to the learner's current situation, based on emotional information.

[0207] This system consists of a server and user terminals. The server provides emotion recognition capabilities to recognize the learner's emotional state in real time. The user terminals are equipped with cameras and microphones, and an emotion recognition algorithm is executed based on the image and audio data collected from these sensors. Specifically, facial expression features are extracted using OpenCV, and emotions are classified using a TENSORFLOW® model.

[0208] The server has an adjustment mechanism that dynamically adjusts learning content to suit the learner's current state based on information obtained through emotion recognition. Learners judged to be highly motivated are presented with more advanced content, while those experiencing stress are provided with relaxing content. This dynamic adjustment is performed by a Python program implemented on the server.

[0209] Furthermore, the server continuously collects and analyzes learner progress information using progress management tools. This makes it possible to continuously generate optimal learning plans tailored to the learner's growth.

[0210] As a concrete example, when a child is learning a new language, an emotion recognition system analyzes the child's facial expressions and collects data on their level of concentration. If the results indicate that the child is clearly experiencing stress, the server instructs the system to switch to a learning method that involves simple games.

[0211] An example of a prompt to input into a generative AI model is, "Please suggest relaxing learning content for a child who is stressed by a complex problem." Based on this prompt, the AI ​​will suggest appropriate learning support.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The device uses its camera and microphone to collect the user's facial expression and voice data. This data serves as input for emotion recognition. The device stores this data locally and prepares it for transmission to the server in real time.

[0215] Step 2:

[0216] The server receives facial expression and audio data sent from the terminal and uses OpenCV to extract facial expression features from the image data. The extracted features become input to the emotion recognition algorithm. This allows the server to obtain the user's basic emotional state (e.g., joy, anger, sadness, surprise).

[0217] Step 3:

[0218] The server uses a TensorFlow emotion recognition model to classify emotions by linking extracted facial features with voice tone data. The classified emotional state is then used as input for subsequent processing. This allows for a more accurate understanding of the user's emotional state.

[0219] Step 4:

[0220] Based on the emotion recognition results, the server uses a generative AI model to suggest learning content that is best suited to the user's current emotional state. Here, the AI ​​model is input with a prompt such as, "Please suggest learning content that will help a child relax when they are stressed by a complex problem," and the optimal learning content is obtained as output.

[0221] Step 5:

[0222] The server dynamically adjusts the learning plan based on the output of the AI ​​model and sends the learning content to the terminal. The terminal presents this content to the user visually and audibly. This allows the user to receive real-time, emotion-responsive learning support.

[0223] Step 6:

[0224] The device continuously collects the user's learning progress and sends it to the server. The server analyzes this progress information and uses it to dynamically update the next learning plan. This allows for adjustments to maximize long-term learning effectiveness.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention aims to improve learning efficiency by providing an optimal learning environment tailored to the individual learning needs of learners using an online learning support system. Specific embodiments are shown below.

[0242] First, the server receives goal and schedule information from the learner and stores it in a database. Next, it uses a plan generation system to create an optimal learning plan tailored to the learner's goals. This plan includes learning content, learning sequence, and progress checkpoints.

[0243] The terminal presents the learning plan, sent from the server, to the learner through a user interface. The learner can progress through the learning process using this interface, and the terminal further records the learner's progress using a progress management system. This information is sent to the server, and an assessment of comprehension is performed based on the progress of the plan.

[0244] The server periodically provides quizzes and tests to assess learners' understanding. Upon receiving the results from learners, the server analyzes them using comprehension assessment tools and suggests the next learning steps. The learning plan is dynamically adjusted as needed during this process.

[0245] The device also displays feedback and learning outcomes sent from the server. This allows learners to visualize their learning progress and achievements, helping them maintain their motivation.

[0246] Furthermore, the means of supporting communication allow learners to share information with other learners and post and answer questions. This feature contributes to reducing feelings of isolation and deepening the understanding of the learning material.

[0247] For example, if a learner sets the goal of "passing an English proficiency test," the server analyzes the learner's available schedule and assigns weekly learning content (vocabulary, grammar, listening, etc.). Based on this plan, the learner proceeds with their daily studies, and the server incorporates feedback into the next learning content based on the results of regularly administered mini-tests. As a result, the system provides support for achieving goals efficiently and effectively.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user logs into the learning support system and enters their goals and planned study time. The device then sends this information to the server.

[0251] Step 2:

[0252] The server stores the received goal and schedule information in a database. Based on this information, it activates the plan generation mechanism to create a learning plan.

[0253] Step 3:

[0254] The server uses machine learning algorithms to analyze past learning history and learning trends, and generates an optimal learning plan for the learner.

[0255] Step 4:

[0256] The generated learning plan is sent from the server to the terminal, which then displays it in the user interface. The user reviews the presented learning plan and proceeds with their learning accordingly.

[0257] Step 5:

[0258] As users progress through their learning, they use a progress management system to input their learning progress into their device.

[0259] Step 6:

[0260] The terminal sends progress information to the server. The server stores the progress information in a database and evaluates the learner's level of understanding using a comprehension evaluation tool.

[0261] Step 7:

[0262] The server periodically creates and provides tests to users to assess their understanding. Based on these results, the learning progress and level of understanding are re-evaluated.

[0263] Step 8:

[0264] The server adjusts the learning plan based on test results and progress information, and updates the next learning content. The updated plan is then sent to the terminal.

[0265] Step 9:

[0266] The device displays feedback and analysis results from the server to the user. This allows the user to understand their learning progress and comprehension level, and use that information to guide their next learning steps.

[0267] Step 10:

[0268] If necessary, users can share information with other learners through their devices and use communication tools to ask questions and exchange opinions.

[0269] (Example 1)

[0270] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] There is a growing need to improve learners' learning efficiency by providing an optimal learning environment tailored to their individual learning needs. However, conventional systems have challenges in adequately creating optimized plans for each learner and efficiently collecting and analyzing progress information. This can lead to delays in achieving learning goals and a decline in motivation.

[0272] 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.

[0273] In this invention, the server includes a plan generation means for creating an optimal learning plan based on goal and schedule information obtained from the user, a progress management means for collecting user progress information as the user progresses through the learning according to the constructed learning plan, and an evaluation means for providing periodic tests to assess the user's understanding and improving the learning content based on the results. This makes it possible to provide an efficient and effective learning environment that meets the individual learning needs of learners.

[0274] A "plan generation means" is a component for creating a learning plan optimized for individual learning needs based on goal and schedule information obtained from the user.

[0275] A "progress management system" is a system component equipped with functions to efficiently collect and record the progress of a user as they proceed with their learning according to their learning plan.

[0276] "Evaluation methods" refer to methods and mechanisms for providing periodic tests to assess users' understanding, analyzing the results, and improving the learning content.

[0277] A "display mechanism" is a system that visualizes the user's learning outcomes and presents them efficiently to the user in order to maintain and promote their motivation to learn.

[0278] "Means of interaction" refer to elements that have the function of forming learning groups and supporting information sharing and communication among users.

[0279] "Interface means" refers to a user interface that visually presents the generated learning plan and progress, enabling users to operate it intuitively.

[0280] This invention is a system for supporting online learning environments and aims to provide an optimal learning plan according to the individual learning needs of learners. The following describes embodiments for carrying out this invention.

[0281] The server collects learning objectives and schedule information provided by users and stores this information in a database. Relational database management systems are often used as the database, specifically MySQL or PostgreSQL. On the server, programs written in Python or Java process the objectives and schedule information and function as a plan generation mechanism. This generates a learning plan that includes individual learning content and sequence.

[0282] The terminal presents the learning plan sent from the server to the user through a dedicated application. This application operates on iOS and Android, which are commonly used mobile operating systems. The user interface displayed on the terminal is built using React Native and is designed to be intuitive for the user to operate. The user uses this interface to proceed with daily learning, and progress information is recorded.

[0283] The user's progress information is sent from the terminal to the server and is regularly analyzed by the comprehension evaluation means. Regular evaluations are conducted using online test platforms such as Google Forms and Quizlet. Upon receiving the evaluation results based on the progress, the server dynamically modifies the next learning steps and content to provide an optimal learning environment. This feedback maintains the user's learning motivation and supports effective attainment of the goals to be achieved.

[0284] As a specific example, when a certain user has the goal of "improving English vocabulary", the server analyzes from the past learning history and proposes a vocabulary list and related quizzes to be done this week. The user checks the daily progress through the terminal and continues learning while receiving feedback from the server as needed.

[0285] An example of the prompt text input to the generative AI model is shown below:

[0286] "Please propose an optimal schedule for next week's English vocabulary learning."

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The server receives goal and schedule information from the user through an input form. This input data is processed by the server as an HTTP request and stored in a database. This process executes algorithms to verify the validity of the data and ensure that no invalid data is present. The output is normalized learning goals and schedule information stored in the database.

[0290] Step 2:

[0291] The server creates a learning plan using a plan generation mechanism based on stored goal and schedule information. Based on the goal data received as input, it determines the priority of learning content and selects practice problems and reference materials. An algorithm written in Python processes learning content accessible from the database and generates a plan as output, which includes specific learning steps and progress checkpoints.

[0292] Step 3:

[0293] The terminal displays the learning plan sent from the server in a user interface. The input here is the plan data received from the server, and the plan is visualized using a UI built with React Native. In this process, design templates are used to make it easy for the user to intuitively check their learning progress. The output is a display of the learning plan in a format that the user can review.

[0294] Step 4:

[0295] Users progress through their daily studies according to the learning plan presented via their device. As they progress, users record their progress. Inputs include the completion status of daily learning content and the time spent studying, which are stored on the device as progress management data. Output is a progress report synchronized with the server.

[0296] Step 5:

[0297] The server operates by evaluating understanding based on progress information. For example, it utilizes learning management methods to analyze test results obtained as input. Online platforms such as Google Forms are used for tests, and the results are sent back to the server as digital data. Based on this output, the server suggests adjustments to be applied to the next learning plan and designs a more appropriate and effective learning path.

[0298] Step 6:

[0299] The terminal displays feedback and learning outcome information obtained from the server in a user interface. The input is feedback data sent from the server, and the terminal visually presents the results. Based on this, the user can check their own learning performance and reset their goals. The output is a screen display visually showing the feedback.

[0300] (Application Example 1)

[0301] 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."

[0302] Conventional learning support systems have struggled to provide dynamically optimized learning plans based on individual learning goals and progress for each learner. Furthermore, they lacked mechanisms to efficiently support information sharing and interaction among learners, posing challenges to maintaining learner motivation and improving comprehension. Additionally, the use of AI to propose personalized learning processes has been insufficient, highlighting the need for improved learning efficiency.

[0303] 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.

[0304] In this invention, the server includes a plan generation means for generating an optimal learning plan based on the goals and activity plan information received from the learner, a progress management means for collecting the progress information of the learner, an evaluation means for providing problems to evaluate the degree of understanding, and a recommendation means for optimizing the learning plan using a generation AI model. Thereby, dynamic and optimized learning support according to individual learning needs becomes possible, and the learner can efficiently achieve the goals.

[0305] The "learner" refers to an individual who attempts to improve their knowledge and skills using a learning support system.

[0306] The "goal" is a specific outcome or skill acquisition objective that the learner wants to achieve.

[0307] The "activity plan information" is information regarding the schedule and action plan necessary to achieve the goals set by the learner.

[0308] The "plan generation means" is a function that executes a process for creating an optimal learning plan based on the goals and activity plan information of the learner.

[0309] The "progress management means" is a function that sequentially measures and records the progress and achievement level of the learner's learning.

[0310] The "evaluation means" is a function that quantitatively or qualitatively measures the degree of understanding based on the problems and test results provided to the learner.

[0311] "Feedback" is a process of presenting information to reflect the learning achievements of the learner and improve motivation.

[0312] The "communication means" is a function that provides a venue or mechanism for learners to share information and ask questions.

[0313] A "generative AI model" is an algorithm or model that uses artificial intelligence to analyze data and dynamically optimize the learning plan.

[0314] A "prompt statement" is an input statement used to prompt a generative AI model to produce a specific output or perform a particular action.

[0315] "Recommendation methods" refers to a feature that uses a generative AI model to suggest the optimal learning plan and next learning steps based on the learner's data.

[0316] The system implementing this invention consists of a server and a terminal, providing a learning environment optimized for the learner. The server receives goal and activity plan information from the learner and generates a learning plan using a generative AI model based on this information. Python and the Django framework are used as the plan generation means to perform data management and calculations.

[0317] The device visually presents the learning plan to the learner through a user interface and collects data based on user input. It uses a web browser or a dedicated application and employs React Native. For progress management, it tracks the learner's progress in real time and sends the data to the server.

[0318] The server analyzes progress information and learning history, and conducts periodic tests to assess comprehension. Based on the evaluation results, the evaluation system utilizes a generative AI model to dynamically optimize the learning plan by adjusting the next learning content. Furthermore, the generative AI model is trained on learning content using prompt sentences, and provides feedback and recommendations tailored to the learner.

[0319] For example, if a learner wants to learn a new language, the AI ​​will recommend a daily learning plan based on the user's set goals and present quizzes according to their progress. This measures their proficiency, and the next learning steps are suggested. Through this process, learners can efficiently achieve their goals.

[0320] As an example of a prompt to input into the generative AI model, we will use the text: "Generate an appropriate learning plan and tasks daily based on the goals set by the user. Analyze progress and suggest the next steps." Based on this prompt, the generative AI model will recommend appropriate learning content.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] Users input learning objectives and activity plan information using a terminal. This information is sent to the server as structured data by the terminal. Input includes details such as the user's goals and available schedule, while output is structured data sent to the server. The terminal provides a screen that allows users to input this information in an easy-to-understand format.

[0324] Step 2:

[0325] The server inputs the received data into a generating AI model and generates an optimized learning plan. This process uses prompts to instruct the AI ​​model to generate daily learning tasks and content. The input is formatted user data, and the output is a specific learning plan. The server saves the generated plan to a database.

[0326] Step 3:

[0327] The server sends the generated learning plan to the terminal. The terminal configures a display screen so that the learner can visually confirm the plan. The plan is organized in a way that facilitates progress. The input is the learning plan data received from the server, and the output is the display on the user interface.

[0328] Step 4:

[0329] Users progress through their learning on their device according to their learning plan. The device records the user's progress in real time and periodically sends it to the server. Input is the user's learning activity, and output is progress data. The device provides an easy-to-use interface to support learning habits.

[0330] Step 5:

[0331] The server uses evaluation tools to determine the level of understanding based on progress data and proposes the next learning step using a generative AI model. In this process, the AI ​​determines the optimal learning content using prompts. The input is progress data, and the output is the next learning suggestion. The AI ​​model has the ability to flexibly adapt to the user's characteristics.

[0332] Step 6:

[0333] The server sends the next learning plan to the terminal, which displays it in the user interface. The user receives feedback and obtains information that helps them continue learning. The input is information about the next learning step, and the output is the displayed feedback. The terminal presents information that includes elements to enhance the learner's motivation.

[0334] 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.

[0335] This invention provides more individually optimized learning support by combining an emotion engine that recognizes and analyzes learners' emotions with an online learning system. A specific embodiment is shown below.

[0336] First, learners log into the system and enter their goals and schedules. The terminal sends this information to the server, where it is stored in a database. The server uses this data to generate an optimal learning plan and activates the plan generation mechanism. Up to this point, it is the same as conventional systems, but the addition of an emotion engine provides more advanced learning support.

[0337] The emotion engine analyzes the learner's facial expressions and voice tone through sensors such as cameras and microphones built into the user's device, collecting emotion data in real time. The server receives this data and uses an emotion recognition algorithm to classify the learner's emotional state (for example, whether they are focused or stressed).

[0338] Based on this emotional information, the server suggests learning content and methods that are best suited to the learner's current state. For example, if a learner is feeling stressed, it can recommend more relaxing content and approaches. Conversely, if a high level of concentration is detected, it can suggest tasks with increased difficulty.

[0339] Furthermore, the server dynamically adjusts the learning plan and generates feedback using data analyzed by the emotion engine. This allows learners to visualize their learning progress and emotional changes, providing customized support to boost their motivation.

[0340] As a concrete example, suppose a learner has the goal of "learning a new language." The server creates a standard learning plan and displays it on the device, while simultaneously monitoring the learner's emotional state during learning using an emotion engine. If the server detects that the learner is feeling stressed while tackling complex grammatical points, it suggests switching to learning with simpler example sentences. This reduces the learner's stress and allows them to continue learning while maintaining motivation.

[0341] Thus, a learning support system that incorporates an emotional engine provides a learning environment tailored to the individual needs of learners, offering a means to achieve efficient and effective learning.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user logs into the online learning system and enters their learning goals and schedule into their device. The device then sends this information to the server.

[0345] Step 2:

[0346] The server stores the received goal and schedule information in a database. A plan generation mechanism is then activated to create a learning plan.

[0347] Step 3:

[0348] The plan generation system formulates an optimal learning plan based on the learner's goals, taking into account the content of the previous lesson. This plan is then transmitted to the terminal.

[0349] Step 4:

[0350] The device displays the received learning plan on the user interface. The user reviews the plan and begins their learning activities.

[0351] Step 5:

[0352] During learning, the device uses its built-in emotion engine to analyze the user's facial expressions and voice in real time. The results of this analysis are sent to the server as emotion data.

[0353] Step 6:

[0354] The server receives emotional data and uses an emotion recognition algorithm to classify the user's emotional state. For example, it determines states such as "concentrated," "stressed," and "relaxed."

[0355] Step 7:

[0356] The server adjusts the learning content based on the user's emotional state. If the user's emotions indicate stress, the learning plan is dynamically changed, for example, by switching to content more suitable for relaxation.

[0357] Step 8:

[0358] The adjusted learning plan is sent back to the device, and the user is presented with the new plan. This allows the user to continue with optimal learning activities tailored to their emotional state.

[0359] Step 9:

[0360] The server generates feedback based on the user's progress and sentiment data. The feedback is displayed on the device, allowing the user to track their learning progress and emotional changes.

[0361] Step 10:

[0362] If necessary, users can exchange opinions with other learners regarding sentiment data and progress, and communication features are supported. This allows users to gain new perspectives on their learning.

[0363] (Example 2)

[0364] 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".

[0365] In online learning systems, it has been difficult to appropriately recognize the emotional state of individual learners and provide learning support accordingly. Furthermore, there has been a lack of effective feedback to maintain learners' motivation and methods to visualize learning progress. This invention aims to solve these problems by providing a highly individualized learning environment that incorporates emotional data.

[0366] 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.

[0367] In this invention, the server includes a plan generation means, a progress management means, an emotion recognition means, a suggestion means, a display means, and an information transmission means. This makes it possible to provide an individually optimized learning plan according to the learner's emotional state and to support the effective progress of learning while maintaining the learner's motivation.

[0368] A "plan generation means" is a method or apparatus equipped with the function of generating an optimal learning plan based on goal and schedule information received from the learner.

[0369] "Progress management means" refers to a method or device for collecting and managing learner progress information during the learning process in accordance with the generated learning plan.

[0370] "Emotion recognition means" refers to a method or apparatus for collecting learner's emotional data via voice and image input devices, and for recognizing and classifying that emotional state.

[0371] "Proposed means" refers to a method or apparatus for providing learning content suitable for learners based on emotional information and for dynamically adjusting the learning plan.

[0372] "Display means" refers to a method or device for visualizing learners' learning outcomes and generating and displaying feedback to maintain motivation.

[0373] "Information transmission means" refers to methods or devices for forming learning communities and supporting information sharing among learners.

[0374] This specification provides an individually optimized online learning support system that takes learners' emotions into account. This system uses emotion recognition technology to analyze the learner's real-time emotional state and dynamically adjust the learning experience. The hardware and software used, data processing methods, and specific examples are described below.

[0375] Hardware and software

[0376] The device is equipped with a camera and microphone to collect the learner's facial expressions and voice. The device has emotion recognition software installed to analyze the collected audio and image data in real time.

[0377] The server is equipped with a database management system that stores goals, schedules, and sentiment data received from learners. The server also implements algorithms for classifying sentiment data and generative AI models.

[0378] Data processing and calculations

[0379] When a user enters their goals and schedule, the device sends this information to the server, where it is registered in the database.

[0380] Emotional data collected by the device is sent to a server and analyzed by an emotion recognition algorithm. This classifies the learner's emotional state into categories such as concentration, stress, and relaxation.

[0381] Based on emotional information, the server suggests learning content suitable for the learner and automatically adjusts the learning plan to provide an optimal learning environment.

[0382] Specific example

[0383] For example, suppose a learner has the goal of "learning a new language." While the learner is working on a difficult grammatical section, the device's camera and microphone detect signs of stress from the user's facial expressions and voice. The server then suggests switching to practicing simpler phrases and displays this information on the device.

[0384] Example of a prompt

[0385] When using a generative AI model, the prompt message used is "Please suggest ways to adjust the learning plan when the user is feeling stressed."

[0386] This system enables personalized learning support through real-time analysis of learners' emotions.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The user logs into the online learning platform and enters their goals and schedule information into their device. The device then sends the entered goals and schedule information to the server. The server records this information in a database and uses it as basic data for planning future learning. The input is the goals and schedule information, and the output is registration to the database.

[0390] Step 2:

[0391] The server executes a plan generation algorithm based on the database to generate the optimal learning plan for the learner. The generated learning plan takes the learner's goals and schedule into account, setting sequential learning steps. Here, learner information from the database is used as input data, and the output is a specific learning plan. The generated learning plan is sent to the terminal and presented to the user.

[0392] Step 3:

[0393] The user begins learning using a device equipped with a camera and microphone. The device's emotion recognition software collects the user's facial expressions and voice via the camera and microphone, and analyzes the emotion data in real time. The device software receives the collected audio and image data as input, uses an emotion recognition algorithm to determine a specific emotional state, and sends the result to the server. The output is the learner's emotional state.

[0394] Step 4:

[0395] The server analyzes the received emotional data and uses a generative AI model to classify the learner's emotional state. Based on this analysis, the server dynamically adjusts the learning plan. For example, it might suggest a more challenging task if the user is focused, or a more relaxed approach if they are stressed. The input is emotional data and the current learning plan, and the output is the adjusted learning content.

[0396] Step 5:

[0397] The server sends a tailored learning plan and feedback to the device, which then displays it to the user. The feedback includes advice based on the learner's progress and emotions. Users can review the feedback as they progress, maintaining motivation and continuing their learning. The output consists of visualized feedback information and a tailored learning plan.

[0398] (Application Example 2)

[0399] 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 as the "terminal".

[0400] In online learning, providing individually optimized learning support tailored to each learner's state and emotions is difficult. Therefore, there is a need to recognize learners' emotional states in real time and flexibly adjust learning plans based on those emotions. However, conventional systems lack this kind of dynamic response. This results in a challenge in effectively alleviating learners' stress and decreased motivation.

[0401] 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.

[0402] In this invention, the server includes emotion recognition means for recognizing and analyzing the learner's emotional state, adjustment means for dynamically adjusting the learning content and method based on the learner's emotional information, and progress management means for collecting the learner's progress information. This makes it possible to provide a customized learning experience that is tailored to the learner's emotions and learning progress.

[0403] A "plan generation means" is an element that has the function of creating an optimal learning plan based on goal and schedule information received from the learner.

[0404] A "progress management tool" is an element that has the function of continuously collecting the learner's progress in the learning process, which proceeds according to the generated learning plan.

[0405] "Evaluation methods" refer to elements used to assess learners' understanding through periodic tests and to adjust learning content based on the results.

[0406] A "display means" is an element that has the function of generating feedback to maintain learning motivation by visually showing the learner's learning outcomes.

[0407] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the learner's emotional state using sensors such as cameras and microphones.

[0408] "Adjustment mechanisms" are elements that have the function of flexibly changing the learning content and approach according to the learner's current situation, based on emotional information.

[0409] This system consists of a server and user terminals. The server provides emotion recognition capabilities to recognize the learner's emotional state in real time. The user terminals are equipped with cameras and microphones, and they execute emotion recognition algorithms based on image and audio data collected from these sensors. Specifically, they use OpenCV to extract facial expression features and TensorFlow models to classify emotions.

[0410] The server has an adjustment mechanism that dynamically adjusts learning content to suit the learner's current state based on information obtained through emotion recognition. Learners judged to be highly motivated are presented with more advanced content, while those experiencing stress are provided with relaxing content. This dynamic adjustment is performed by a Python program implemented on the server.

[0411] Furthermore, the server continuously collects and analyzes learner progress information using progress management tools. This makes it possible to continuously generate optimal learning plans tailored to the learner's growth.

[0412] As a concrete example, when a child is learning a new language, an emotion recognition system analyzes the child's facial expressions and collects data on their level of concentration. If the results indicate that the child is clearly experiencing stress, the server instructs the system to switch to a learning method that involves simple games.

[0413] An example of a prompt to input into a generative AI model is, "Please suggest relaxing learning content for a child who is stressed by a complex problem." Based on this prompt, the AI ​​will suggest appropriate learning support.

[0414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0415] Step 1:

[0416] The device uses its camera and microphone to collect the user's facial expression and voice data. This data serves as input for emotion recognition. The device stores this data locally and prepares it for transmission to the server in real time.

[0417] Step 2:

[0418] The server receives facial expression and audio data sent from the terminal and uses OpenCV to extract facial expression features from the image data. The extracted features become input to the emotion recognition algorithm. This allows the server to obtain the user's basic emotional state (e.g., joy, anger, sadness, surprise).

[0419] Step 3:

[0420] The server uses a TensorFlow emotion recognition model to classify emotions by linking extracted facial features with voice tone data. The classified emotional state is then used as input for subsequent processing. This allows for a more accurate understanding of the user's emotional state.

[0421] Step 4:

[0422] Based on the emotion recognition results, the server uses a generative AI model to suggest learning content that is best suited to the user's current emotional state. Here, the AI ​​model is input with a prompt such as, "Please suggest learning content that will help a child relax when they are stressed by a complex problem," and the optimal learning content is obtained as output.

[0423] Step 5:

[0424] The server dynamically adjusts the learning plan based on the output of the AI ​​model and sends the learning content to the terminal. The terminal presents this content to the user visually and audibly. This allows the user to receive real-time, emotion-responsive learning support.

[0425] Step 6:

[0426] The device continuously collects the user's learning progress and sends it to the server. The server analyzes this progress information and uses it to dynamically update the next learning plan. This allows for adjustments to maximize long-term learning effectiveness.

[0427] 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.

[0428] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention aims to improve learning efficiency by providing an optimal learning environment tailored to the individual learning needs of learners using an online learning support system. Specific embodiments are shown below.

[0444] First, the server receives goal and schedule information from the learner and stores it in a database. Next, it uses a plan generation system to create an optimal learning plan tailored to the learner's goals. This plan includes learning content, learning sequence, and progress checkpoints.

[0445] The terminal presents the learning plan, sent from the server, to the learner through a user interface. The learner can progress through the learning process using this interface, and the terminal further records the learner's progress using a progress management system. This information is sent to the server, and an assessment of comprehension is performed based on the progress of the plan.

[0446] The server periodically provides quizzes and tests to assess learners' understanding. Upon receiving the results from learners, the server analyzes them using comprehension assessment tools and suggests the next learning steps. The learning plan is dynamically adjusted as needed during this process.

[0447] The device also displays feedback and learning outcomes sent from the server. This allows learners to visualize their learning progress and achievements, helping them maintain their motivation.

[0448] Furthermore, the means of supporting communication allow learners to share information with other learners and post and answer questions. This feature contributes to reducing feelings of isolation and deepening the understanding of the learning material.

[0449] For example, if a learner sets the goal of "passing an English proficiency test," the server analyzes the learner's available schedule and assigns weekly learning content (vocabulary, grammar, listening, etc.). Based on this plan, the learner proceeds with their daily studies, and the server incorporates feedback into the next learning content based on the results of regularly administered mini-tests. As a result, the system provides support for achieving goals efficiently and effectively.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The user logs into the learning support system and enters their goals and planned study time. The device then sends this information to the server.

[0453] Step 2:

[0454] The server stores the received goal and schedule information in a database. Based on this information, it activates the plan generation mechanism to create a learning plan.

[0455] Step 3:

[0456] The server uses machine learning algorithms to analyze past learning history and learning trends, and generates an optimal learning plan for the learner.

[0457] Step 4:

[0458] The generated learning plan is sent from the server to the terminal, which then displays it in the user interface. The user reviews the presented learning plan and proceeds with their learning accordingly.

[0459] Step 5:

[0460] As users progress through their learning, they use a progress management system to input their learning progress into their device.

[0461] Step 6:

[0462] The terminal sends progress information to the server. The server stores the progress information in a database and evaluates the learner's level of understanding using a comprehension evaluation tool.

[0463] Step 7:

[0464] The server periodically creates and provides tests to users to assess their understanding. Based on these results, the learning progress and level of understanding are re-evaluated.

[0465] Step 8:

[0466] The server adjusts the learning plan based on test results and progress information, and updates the next learning content. The updated plan is then sent to the terminal.

[0467] Step 9:

[0468] The device displays feedback and analysis results from the server to the user. This allows the user to understand their learning progress and comprehension level, and use that information to guide their next learning steps.

[0469] Step 10:

[0470] If necessary, users can share information with other learners through their devices and use communication tools to ask questions and exchange opinions.

[0471] (Example 1)

[0472] 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."

[0473] There is a growing need to improve learners' learning efficiency by providing an optimal learning environment tailored to their individual learning needs. However, conventional systems have challenges in adequately creating optimized plans for each learner and efficiently collecting and analyzing progress information. This can lead to delays in achieving learning goals and a decline in motivation.

[0474] 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.

[0475] In this invention, the server includes a plan generation means for creating an optimal learning plan based on goal and schedule information obtained from the user, a progress management means for collecting user progress information as the user progresses through the learning according to the constructed learning plan, and an evaluation means for providing periodic tests to assess the user's understanding and improving the learning content based on the results. This makes it possible to provide an efficient and effective learning environment that meets the individual learning needs of learners.

[0476] A "plan generation means" is a component for creating a learning plan optimized for individual learning needs based on goal and schedule information obtained from the user.

[0477] A "progress management system" is a system component equipped with functions to efficiently collect and record the progress of a user as they proceed with their learning according to their learning plan.

[0478] "Evaluation methods" refer to methods and mechanisms for providing periodic tests to assess users' understanding, analyzing the results, and improving the learning content.

[0479] A "display mechanism" is a system that visualizes the user's learning outcomes and presents them efficiently to the user in order to maintain and promote their motivation to learn.

[0480] "Means of interaction" refer to elements that have the function of forming learning groups and supporting information sharing and communication among users.

[0481] "Interface means" refers to a user interface that visually presents the generated learning plan and progress, enabling users to operate it intuitively.

[0482] This invention is a system for supporting online learning environments and aims to provide an optimal learning plan according to the individual learning needs of learners. The following describes embodiments for carrying out this invention.

[0483] The server collects learning objectives and schedule information provided by users and stores this information in a database. Relational database management systems are often used as the database, specifically MySQL or PostgreSQL. On the server, programs written in Python or Java process the objectives and schedule information and function as a plan generation mechanism. This generates a learning plan that includes individual learning content and sequence.

[0484] The device presents the user with a learning plan sent from the server via a dedicated application. This application runs on commonly used mobile operating systems such as iOS and Android. The user interface displayed on the device is built using React Native and designed for intuitive operation. Users use this interface to progress through their daily studies, and their progress is recorded.

[0485] User progress information is sent from the device to the server and periodically analyzed by comprehension assessment tools. Regular assessments are conducted using online testing platforms such as Google Forms and Quizlet. Based on the assessment results, the server dynamically adjusts the next learning steps and content to provide an optimal learning environment. This feedback helps maintain user motivation and effectively achieve learning goals.

[0486] For example, if a user has the goal of "improving their English vocabulary," the server analyzes their past learning history and suggests a vocabulary list and related quizzes to complete this week. The user can check their daily progress through their device and continue learning while receiving feedback from the server as needed.

[0487] An example of a prompt message to input into a generative AI model is shown below:

[0488] "Please suggest the best schedule for English vocabulary learning next week."

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The server receives goal and schedule information from the user through an input form. This input data is processed by the server as an HTTP request and stored in a database. This process executes algorithms to verify the validity of the data and ensure that no invalid data is present. The output is normalized learning goals and schedule information stored in the database.

[0492] Step 2:

[0493] The server creates a learning plan using a plan generation mechanism based on stored goal and schedule information. Based on the goal data received as input, it determines the priority of learning content and selects practice problems and reference materials. An algorithm written in Python processes learning content accessible from the database and generates a plan as output, which includes specific learning steps and progress checkpoints.

[0494] Step 3:

[0495] The terminal displays the learning plan sent from the server in a user interface. The input here is the plan data received from the server, and the plan is visualized using a UI built with React Native. In this process, design templates are used to make it easy for the user to intuitively check their learning progress. The output is a display of the learning plan in a format that the user can review.

[0496] Step 4:

[0497] Users progress through their daily studies according to the learning plan presented via their device. As they progress, users record their progress. Inputs include the completion status of daily learning content and the time spent studying, which are stored on the device as progress management data. Output is a progress report synchronized with the server.

[0498] Step 5:

[0499] The server operates by evaluating understanding based on progress information. For example, it utilizes learning management methods to analyze test results obtained as input. Online platforms such as Google Forms are used for tests, and the results are sent back to the server as digital data. Based on this output, the server suggests adjustments to be applied to the next learning plan and designs a more appropriate and effective learning path.

[0500] Step 6:

[0501] The terminal displays feedback and learning outcome information obtained from the server in a user interface. The input is feedback data sent from the server, and the terminal visually presents the results. Based on this, the user can check their own learning performance and reset their goals. The output is a screen display visually showing the feedback.

[0502] (Application Example 1)

[0503] 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."

[0504] Conventional learning support systems have struggled to provide dynamically optimized learning plans based on individual learning goals and progress for each learner. Furthermore, they lacked mechanisms to efficiently support information sharing and interaction among learners, posing challenges to maintaining learner motivation and improving comprehension. Additionally, the use of AI to propose personalized learning processes has been insufficient, highlighting the need for improved learning efficiency.

[0505] 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.

[0506] In this invention, the server includes a plan generation means for generating an optimal learning plan based on goal and activity plan information received from the learner, a progress management means for collecting learner progress information, an evaluation means for providing problems to assess comprehension, and a recommendation means for optimizing the learning plan using a generated AI model. This enables dynamic and optimized learning support tailored to individual learning needs, allowing learners to efficiently achieve their goals.

[0507] A "learner" refers to an individual who uses a learning support system to improve their own knowledge and skills.

[0508] A "goal" is the specific outcome or skill acquisition objective that a learner wishes to achieve.

[0509] "Activity plan information" refers to information about the schedule and action plan necessary to achieve the goals set by the learner.

[0510] A "plan generation means" is a function that executes a process to create an optimal learning plan based on the learner's goals and activity plan information.

[0511] A "progress management system" is a function that continuously measures and records the learner's learning progress and achievement level.

[0512] "Evaluation methods" refer to functions that quantitatively or qualitatively measure the level of understanding based on the results of problems or tests provided to learners.

[0513] "Feedback" is the process of providing information that reflects learners' learning outcomes and improves their motivation.

[0514] "Communication tools" refer to functions that provide a platform or system for learners to share information and ask questions.

[0515] A "generative AI model" is an algorithm or model that uses artificial intelligence to analyze data and dynamically optimize the learning plan.

[0516] A "prompt statement" is an input statement used to prompt a generative AI model to produce a specific output or perform a particular action.

[0517] "Recommendation methods" refers to a feature that uses a generative AI model to suggest the optimal learning plan and next learning steps based on the learner's data.

[0518] The system implementing this invention consists of a server and a terminal, providing a learning environment optimized for the learner. The server receives goal and activity plan information from the learner and generates a learning plan using a generative AI model based on this information. Python and the Django framework are used as the plan generation means to perform data management and calculations.

[0519] The device visually presents the learning plan to the learner through a user interface and collects data based on user input. It uses a web browser or a dedicated application and employs React Native. For progress management, it tracks the learner's progress in real time and sends the data to the server.

[0520] The server analyzes progress information and learning history, and conducts periodic tests to assess comprehension. Based on the evaluation results, the evaluation system utilizes a generative AI model to dynamically optimize the learning plan by adjusting the next learning content. Furthermore, the generative AI model is trained on learning content using prompt sentences, and provides feedback and recommendations tailored to the learner.

[0521] For example, if a learner wants to learn a new language, the AI ​​will recommend a daily learning plan based on the user's set goals and present quizzes according to their progress. This measures their proficiency, and the next learning steps are suggested. Through this process, learners can efficiently achieve their goals.

[0522] As an example of a prompt to input into the generative AI model, we will use the text: "Generate an appropriate learning plan and tasks daily based on the goals set by the user. Analyze progress and suggest the next steps." Based on this prompt, the generative AI model will recommend appropriate learning content.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] Users input learning objectives and activity plan information using a terminal. This information is sent to the server as structured data by the terminal. Input includes details such as the user's goals and available schedule, while output is structured data sent to the server. The terminal provides a screen that allows users to input this information in an easy-to-understand format.

[0526] Step 2:

[0527] The server inputs the received data into a generating AI model and generates an optimized learning plan. This process uses prompts to instruct the AI ​​model to generate daily learning tasks and content. The input is formatted user data, and the output is a specific learning plan. The server saves the generated plan to a database.

[0528] Step 3:

[0529] The server sends the generated learning plan to the terminal. The terminal configures a display screen so that the learner can visually confirm the plan. The plan is organized in a way that facilitates progress. The input is the learning plan data received from the server, and the output is the display on the user interface.

[0530] Step 4:

[0531] Users progress through their learning on their device according to their learning plan. The device records the user's progress in real time and periodically sends it to the server. Input is the user's learning activity, and output is progress data. The device provides an easy-to-use interface to support learning habits.

[0532] Step 5:

[0533] The server uses evaluation tools to determine the level of understanding based on progress data and proposes the next learning step using a generative AI model. In this process, the AI ​​determines the optimal learning content using prompts. The input is progress data, and the output is the next learning suggestion. The AI ​​model has the ability to flexibly adapt to the user's characteristics.

[0534] Step 6:

[0535] The server sends the next learning plan to the terminal, which displays it in the user interface. The user receives feedback and obtains information that helps them continue learning. The input is information about the next learning step, and the output is the displayed feedback. The terminal presents information that includes elements to enhance the learner's motivation.

[0536] 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.

[0537] This invention provides more individually optimized learning support by combining an emotion engine that recognizes and analyzes learners' emotions with an online learning system. A specific embodiment is shown below.

[0538] First, learners log into the system and enter their goals and schedules. The terminal sends this information to the server, where it is stored in a database. The server uses this data to generate an optimal learning plan and activates the plan generation mechanism. Up to this point, it is the same as conventional systems, but the addition of an emotion engine provides more advanced learning support.

[0539] The emotion engine analyzes the learner's facial expressions and voice tone through sensors such as cameras and microphones built into the user's device, collecting emotion data in real time. The server receives this data and uses an emotion recognition algorithm to classify the learner's emotional state (for example, whether they are focused or stressed).

[0540] Based on this emotional information, the server suggests learning content and methods that are best suited to the learner's current state. For example, if a learner is feeling stressed, it can recommend more relaxing content and approaches. Conversely, if a high level of concentration is detected, it can suggest tasks with increased difficulty.

[0541] Furthermore, the server dynamically adjusts the learning plan and generates feedback using data analyzed by the emotion engine. This allows learners to visualize their learning progress and emotional changes, providing customized support to boost their motivation.

[0542] As a concrete example, suppose a learner has the goal of "learning a new language." The server creates a standard learning plan and displays it on the device, while simultaneously monitoring the learner's emotional state during learning using an emotion engine. If the server detects that the learner is feeling stressed while tackling complex grammatical points, it suggests switching to learning with simpler example sentences. This reduces the learner's stress and allows them to continue learning while maintaining motivation.

[0543] Thus, a learning support system that incorporates an emotional engine provides a learning environment tailored to the individual needs of learners, offering a means to achieve efficient and effective learning.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The user logs into the online learning system and enters their learning goals and schedule into their device. The device then sends this information to the server.

[0547] Step 2:

[0548] The server stores the received goal and schedule information in a database. A plan generation mechanism is then activated to create a learning plan.

[0549] Step 3:

[0550] The plan generation system formulates an optimal learning plan based on the learner's goals, taking into account the content of the previous lesson. This plan is then transmitted to the terminal.

[0551] Step 4:

[0552] The device displays the received learning plan on the user interface. The user reviews the plan and begins their learning activities.

[0553] Step 5:

[0554] During learning, the device uses its built-in emotion engine to analyze the user's facial expressions and voice in real time. The results of this analysis are sent to the server as emotion data.

[0555] Step 6:

[0556] The server receives emotional data and uses an emotion recognition algorithm to classify the user's emotional state. For example, it determines states such as "concentrated," "stressed," and "relaxed."

[0557] Step 7:

[0558] The server adjusts the learning content based on the user's emotional state. If the user's emotions indicate stress, the learning plan is dynamically changed, for example, by switching to content more suitable for relaxation.

[0559] Step 8:

[0560] The adjusted learning plan is sent back to the device, and the user is presented with the new plan. This allows the user to continue with optimal learning activities tailored to their emotional state.

[0561] Step 9:

[0562] The server generates feedback based on the user's progress and sentiment data. The feedback is displayed on the device, allowing the user to track their learning progress and emotional changes.

[0563] Step 10:

[0564] If necessary, users can exchange opinions with other learners regarding sentiment data and progress, and communication features are supported. This allows users to gain new perspectives on their learning.

[0565] (Example 2)

[0566] 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."

[0567] In online learning systems, it has been difficult to appropriately recognize the emotional state of individual learners and provide learning support accordingly. Furthermore, there has been a lack of effective feedback to maintain learners' motivation and methods to visualize learning progress. This invention aims to solve these problems by providing a highly individualized learning environment that incorporates emotional data.

[0568] 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.

[0569] In this invention, the server includes a plan generation means, a progress management means, an emotion recognition means, a suggestion means, a display means, and an information transmission means. This makes it possible to provide an individually optimized learning plan according to the learner's emotional state and to support the effective progress of learning while maintaining the learner's motivation.

[0570] A "plan generation means" is a method or apparatus equipped with the function of generating an optimal learning plan based on goal and schedule information received from the learner.

[0571] "Progress management means" refers to a method or device for collecting and managing learner progress information during the process of learning according to a generated learning plan.

[0572] "Emotion recognition means" refers to a method or apparatus for collecting learner's emotional data via voice and image input devices, and for recognizing and classifying that emotional state.

[0573] "Proposed means" refers to a method or apparatus for providing learning content suitable for learners based on emotional information and for dynamically adjusting the learning plan.

[0574] "Display means" refers to a method or device for visualizing learners' learning outcomes and generating and displaying feedback to maintain motivation.

[0575] "Information transmission means" refers to methods or devices for forming learning communities and supporting information sharing among learners.

[0576] This specification provides an individually optimized online learning support system that takes learners' emotions into account. This system uses emotion recognition technology to analyze the learner's real-time emotional state and dynamically adjust the learning experience. The hardware and software used, data processing methods, and specific examples are described below.

[0577] Hardware and software

[0578] The device is equipped with a camera and microphone to collect the learner's facial expressions and voice. The device has emotion recognition software installed to analyze the collected audio and image data in real time.

[0579] The server is equipped with a database management system that stores goals, schedules, and sentiment data received from learners. The server also implements algorithms for classifying sentiment data and generative AI models.

[0580] Data processing and calculations

[0581] When a user enters their goals and schedule, the device sends this information to the server, where it is registered in the database.

[0582] Emotional data collected by the device is sent to a server and analyzed by an emotion recognition algorithm. This classifies the learner's emotional state into categories such as concentration, stress, and relaxation.

[0583] Based on emotional information, the server suggests learning content suitable for the learner and automatically adjusts the learning plan to provide an optimal learning environment.

[0584] Specific example

[0585] For example, suppose a learner has the goal of "learning a new language." While the learner is working on a difficult grammatical section, the device's camera and microphone detect signs of stress from the user's facial expressions and voice. The server then suggests switching to practicing simpler phrases and displays this information on the device.

[0586] Example of a prompt

[0587] When using a generative AI model, the prompt message used is "Please suggest ways to adjust the learning plan when the user is feeling stressed."

[0588] This system enables personalized learning support through real-time analysis of learners' emotions.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The user logs into the online learning platform and enters their goals and schedule information into their device. The device then sends the entered goals and schedule information to the server. The server records this information in a database and uses it as basic data for planning future learning. The input is the goals and schedule information, and the output is registration to the database.

[0592] Step 2:

[0593] The server executes a plan generation algorithm based on the database to generate the optimal learning plan for the learner. The generated learning plan takes the learner's goals and schedule into account, setting sequential learning steps. Here, learner information from the database is used as input data, and the output is a specific learning plan. The generated learning plan is sent to the terminal and presented to the user.

[0594] Step 3:

[0595] The user begins learning using a device equipped with a camera and microphone. The device's emotion recognition software collects the user's facial expressions and voice via the camera and microphone, and analyzes the emotion data in real time. The device software receives the collected audio and image data as input, uses an emotion recognition algorithm to determine a specific emotional state, and sends the result to the server. The output is the learner's emotional state.

[0596] Step 4:

[0597] The server analyzes the received emotional data and uses a generative AI model to classify the learner's emotional state. Based on this analysis, the server dynamically adjusts the learning plan. For example, it might suggest a more challenging task if the user is focused, or a more relaxed approach if they are stressed. The input is emotional data and the current learning plan, and the output is the adjusted learning content.

[0598] Step 5:

[0599] The server sends a tailored learning plan and feedback to the device, which then displays it to the user. The feedback includes advice based on the learner's progress and emotions. Users can review the feedback as they progress, maintaining motivation and continuing their learning. The output consists of visualized feedback information and a tailored learning plan.

[0600] (Application Example 2)

[0601] 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."

[0602] In online learning, providing individually optimized learning support tailored to each learner's state and emotions is difficult. Therefore, there is a need to recognize learners' emotional states in real time and flexibly adjust learning plans based on those emotions. However, conventional systems lack this kind of dynamic response. This results in a challenge in effectively alleviating learners' stress and decreased motivation.

[0603] 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.

[0604] In this invention, the server includes emotion recognition means for recognizing and analyzing the learner's emotional state, adjustment means for dynamically adjusting the learning content and method based on the learner's emotional information, and progress management means for collecting the learner's progress information. This makes it possible to provide a customized learning experience that is tailored to the learner's emotions and learning progress.

[0605] A "plan generation means" is an element that has the function of creating an optimal learning plan based on goal and schedule information received from the learner.

[0606] A "progress management tool" is an element that has the function of continuously collecting the learner's progress in the learning process, which proceeds according to the generated learning plan.

[0607] "Evaluation methods" refer to elements used to assess learners' understanding through periodic tests and to adjust learning content based on the results.

[0608] A "display means" is an element that has the function of generating feedback to maintain learning motivation by visually showing the learner's learning outcomes.

[0609] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the learner's emotional state using sensors such as cameras and microphones.

[0610] A "modification mechanism" is an element that has the function of flexibly changing the learning content and approach according to the learner's current situation, based on emotional information.

[0611] This system consists of a server and user terminals. The server provides emotion recognition capabilities to recognize the learner's emotional state in real time. The user terminals are equipped with cameras and microphones, and they execute emotion recognition algorithms based on image and audio data collected from these sensors. Specifically, they use OpenCV to extract facial expression features and TensorFlow models to classify emotions.

[0612] The server has an adjustment mechanism that dynamically adjusts learning content to suit the learner's current state based on information obtained through emotion recognition. Learners judged to be highly motivated are presented with more advanced content, while those experiencing stress are provided with relaxing content. This dynamic adjustment is performed by a Python program implemented on the server.

[0613] Furthermore, the server continuously collects and analyzes learner progress information using progress management tools. This makes it possible to continuously generate optimal learning plans tailored to the learner's growth.

[0614] As a concrete example, when a child is learning a new language, an emotion recognition system analyzes the child's facial expressions and collects data on their level of concentration. If the results indicate that the child is clearly experiencing stress, the server instructs the system to switch to a learning method that involves simple games.

[0615] An example of a prompt to input into a generative AI model is, "Please suggest relaxing learning content for a child who is stressed by a complex problem." Based on this prompt, the AI ​​will suggest appropriate learning support.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The device uses its camera and microphone to collect the user's facial expression and voice data. This data serves as input for emotion recognition. The device stores this data locally and prepares to send it to the server in real time.

[0619] Step 2:

[0620] The server receives facial expression and audio data sent from the terminal and uses OpenCV to extract facial expression features from the image data. The extracted features become input to the emotion recognition algorithm. This allows the server to obtain the user's basic emotional state (e.g., joy, anger, sadness, surprise).

[0621] Step 3:

[0622] The server uses a TensorFlow emotion recognition model to classify emotions by linking extracted facial features with voice tone data. The classified emotional state is then used as input for subsequent processing. This allows for a more accurate understanding of the user's emotional state.

[0623] Step 4:

[0624] Based on the emotion recognition results, the server uses a generative AI model to suggest learning content that is best suited to the user's current emotional state. Here, the AI ​​model is input with a prompt such as, "Please suggest learning content that will help a child relax when they are stressed by a complex problem," and the optimal learning content is obtained as output.

[0625] Step 5:

[0626] The server dynamically adjusts the learning plan based on the output of the AI ​​model and sends the learning content to the terminal. The terminal presents this content to the user visually and audibly. This allows the user to receive real-time, emotion-responsive learning support.

[0627] Step 6:

[0628] The device continuously collects the user's learning progress and sends it to the server. The server analyzes this progress information and uses it to dynamically update the next learning plan. This allows for adjustments to maximize long-term learning effectiveness.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] This invention aims to improve learning efficiency by providing an optimal learning environment tailored to the individual learning needs of learners using an online learning support system. Specific embodiments are shown below.

[0647] First, the server receives goal and schedule information from the learner and stores it in a database. Next, it uses a plan generation system to create an optimal learning plan tailored to the learner's goals. This plan includes learning content, learning sequence, and progress checkpoints.

[0648] The terminal presents the learning plan, sent from the server, to the learner through a user interface. The learner can progress through the learning process using this interface, and the terminal further records the learner's progress using a progress management system. This information is sent to the server, and an assessment of comprehension is performed based on the progress of the plan.

[0649] The server periodically provides quizzes and tests to assess learners' understanding. Upon receiving the results from learners, the server analyzes them using comprehension assessment tools and suggests the next learning steps. The learning plan is dynamically adjusted as needed during this process.

[0650] The device also displays feedback and learning outcomes sent from the server. This allows learners to visualize their learning progress and achievements, helping them maintain their motivation.

[0651] Furthermore, the means of supporting communication allow learners to share information with other learners and post and answer questions. This feature contributes to reducing feelings of isolation and deepening the understanding of the learning material.

[0652] For example, if a learner sets the goal of "passing an English proficiency test," the server analyzes the learner's available schedule and assigns weekly learning content (vocabulary, grammar, listening, etc.). Based on this plan, the learner proceeds with their daily studies, and the server incorporates feedback into the next learning content based on the results of regularly administered mini-tests. As a result, the system provides support for achieving goals efficiently and effectively.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] The user logs into the learning support system and enters their goals and planned study time. The device then sends this information to the server.

[0656] Step 2:

[0657] The server stores the received goal and schedule information in a database. Based on this information, it activates the plan generation mechanism to create a learning plan.

[0658] Step 3:

[0659] The server uses machine learning algorithms to analyze past learning history and learning trends, and generates an optimal learning plan for the learner.

[0660] Step 4:

[0661] The generated learning plan is sent from the server to the terminal, which then displays it in the user interface. The user reviews the presented learning plan and proceeds with their learning accordingly.

[0662] Step 5:

[0663] As users progress through their learning, they use a progress management system to input their learning progress into their device.

[0664] Step 6:

[0665] The terminal sends progress information to the server. The server stores the progress information in a database and evaluates the learner's level of understanding using a comprehension evaluation tool.

[0666] Step 7:

[0667] The server periodically creates and provides tests to users to assess their understanding. Based on these results, the learning progress and level of understanding are re-evaluated.

[0668] Step 8:

[0669] The server adjusts the learning plan based on test results and progress information, and updates the next learning content. The updated plan is then sent to the terminal.

[0670] Step 9:

[0671] The device displays feedback and analysis results from the server to the user. This allows the user to understand their learning progress and comprehension level, and use that information to guide their next learning steps.

[0672] Step 10:

[0673] If necessary, users can share information with other learners through their devices and use communication tools to ask questions and exchange opinions.

[0674] (Example 1)

[0675] 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".

[0676] There is a growing need to improve learners' learning efficiency by providing an optimal learning environment tailored to their individual learning needs. However, conventional systems have challenges in adequately creating optimized plans for each learner and efficiently collecting and analyzing progress information. This can lead to delays in achieving learning goals and a decline in motivation.

[0677] 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.

[0678] In this invention, the server includes a plan generation means for creating an optimal learning plan based on goal and schedule information obtained from the user, a progress management means for collecting user progress information as the user progresses through the learning according to the constructed learning plan, and an evaluation means for providing periodic tests to assess the user's understanding and improving the learning content based on the results. This makes it possible to provide an efficient and effective learning environment that meets the individual learning needs of learners.

[0679] A "plan generation means" is a component for creating a learning plan optimized for individual learning needs based on goal and schedule information obtained from the user.

[0680] A "progress management system" is a system component equipped with functions to efficiently collect and record the progress of a user as they proceed with their learning according to their learning plan.

[0681] "Evaluation methods" refer to methods and mechanisms for providing periodic tests to assess users' understanding, analyzing the results, and improving the learning content.

[0682] A "display mechanism" is a system that visualizes the user's learning outcomes and presents them efficiently to the user in order to maintain and promote their motivation to learn.

[0683] "Means of interaction" refer to elements that have the function of forming learning groups and supporting information sharing and communication among users.

[0684] "Interface means" refers to a user interface that visually presents the generated learning plan and progress, enabling users to operate it intuitively.

[0685] This invention is a system for supporting online learning environments and aims to provide an optimal learning plan according to the individual learning needs of learners. The following describes embodiments for carrying out this invention.

[0686] The server collects learning objectives and schedule information provided by users and stores this information in a database. Relational database management systems are often used as the database, specifically MySQL or PostgreSQL. On the server, programs written in Python or Java process the objectives and schedule information and function as a plan generation mechanism. This generates a learning plan that includes individual learning content and sequence.

[0687] The device presents the user with a learning plan sent from the server via a dedicated application. This application runs on commonly used mobile operating systems such as iOS and Android. The user interface displayed on the device is built using React Native and designed for intuitive operation. Users use this interface to progress through their daily studies, and their progress is recorded.

[0688] User progress information is sent from the device to the server and periodically analyzed by comprehension assessment tools. Regular assessments are conducted using online testing platforms such as Google Forms and Quizlet. Based on the assessment results, the server dynamically adjusts the next learning steps and content to provide an optimal learning environment. This feedback helps maintain user motivation and effectively achieve learning goals.

[0689] For example, if a user has the goal of "improving their English vocabulary," the server analyzes their past learning history and suggests a vocabulary list and related quizzes to complete this week. The user can check their daily progress through their device and continue learning while receiving feedback from the server as needed.

[0690] An example of a prompt message to input into a generative AI model is shown below:

[0691] "Please suggest the best schedule for English vocabulary learning next week."

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The server receives goal and schedule information from the user through an input form. This input data is processed by the server as an HTTP request and stored in a database. This process executes algorithms to verify the validity of the data and ensure that no invalid data is present. The output is normalized learning goals and schedule information stored in the database.

[0695] Step 2:

[0696] The server creates a learning plan using a plan generation mechanism based on stored goal and schedule information. Based on the goal data received as input, it determines the priority of learning content and selects practice problems and reference materials. An algorithm written in Python processes learning content accessible from the database and generates a plan as output, which includes specific learning steps and progress checkpoints.

[0697] Step 3:

[0698] The terminal displays the learning plan sent from the server in a user interface. The input here is the plan data received from the server, and the plan is visualized using a UI built with React Native. In this process, design templates are used to make it easy for the user to intuitively check their learning progress. The output is a display of the learning plan in a format that the user can review.

[0699] Step 4:

[0700] Users progress through their daily studies according to the learning plan presented via their device. As they progress, users record their progress. Inputs include the completion status of daily learning content and the time spent studying, which are stored on the device as progress management data. Output is a progress report synchronized with the server.

[0701] Step 5:

[0702] The server operates by evaluating understanding based on progress information. For example, it utilizes learning management methods to analyze test results obtained as input. Online platforms such as Google Forms are used for tests, and the results are sent back to the server as digital data. Based on this output, the server suggests adjustments to be applied to the next learning plan and designs a more appropriate and effective learning path.

[0703] Step 6:

[0704] The terminal displays feedback and learning outcome information obtained from the server in a user interface. The input is feedback data sent from the server, and the terminal visually presents the results. Based on this, the user can check their own learning performance and reset their goals. The output is a screen display visually showing the feedback.

[0705] (Application Example 1)

[0706] 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".

[0707] Conventional learning support systems have struggled to provide dynamically optimized learning plans based on individual learning goals and progress for each learner. Furthermore, they lacked mechanisms to efficiently support information sharing and interaction among learners, posing challenges to maintaining learner motivation and improving comprehension. Additionally, the use of AI to propose personalized learning processes has been insufficient, highlighting the need for improved learning efficiency.

[0708] 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.

[0709] In this invention, the server includes a plan generation means for generating an optimal learning plan based on goal and activity plan information received from the learner, a progress management means for collecting learner progress information, an evaluation means for providing problems to assess comprehension, and a recommendation means for optimizing the learning plan using a generated AI model. This enables dynamic and optimized learning support tailored to individual learning needs, allowing learners to efficiently achieve their goals.

[0710] A "learner" refers to an individual who uses a learning support system to improve their own knowledge and skills.

[0711] A "goal" is the specific outcome or skill acquisition objective that a learner wishes to achieve.

[0712] "Activity plan information" refers to information about the schedule and action plan necessary to achieve the goals set by the learner.

[0713] A "plan generation means" is a function that executes a process to create an optimal learning plan based on the learner's goals and activity plan information.

[0714] A "progress management system" is a function that continuously measures and records the learner's learning progress and achievement level.

[0715] "Evaluation methods" refer to functions that quantitatively or qualitatively measure the level of understanding based on the results of problems or tests provided to learners.

[0716] "Feedback" is the process of providing information that reflects learners' learning outcomes and improves their motivation.

[0717] "Communication tools" refer to functions that provide a platform or system for learners to share information and ask questions.

[0718] A "generative AI model" is an algorithm or model that uses artificial intelligence to analyze data and dynamically optimize the learning plan.

[0719] A "prompt statement" is an input statement used to prompt a generative AI model to produce a specific output or perform a particular action.

[0720] "Recommendation methods" refers to a feature that uses a generative AI model to suggest the optimal learning plan and next learning steps based on the learner's data.

[0721] The system implementing this invention consists of a server and a terminal, providing a learning environment optimized for the learner. The server receives goal and activity plan information from the learner and generates a learning plan using a generative AI model based on this information. Python and the Django framework are used as the plan generation means to perform data management and calculations.

[0722] The device visually presents the learning plan to the learner through a user interface and collects data based on user input. It uses a web browser or a dedicated application and employs React Native. For progress management, it tracks the learner's progress in real time and sends the data to the server.

[0723] The server analyzes progress information and learning history, and conducts periodic tests to assess comprehension. Based on the evaluation results, the evaluation system utilizes a generative AI model to dynamically optimize the learning plan by adjusting the next learning content. Furthermore, the generative AI model is trained on learning content using prompt sentences, and provides feedback and recommendations tailored to the learner.

[0724] For example, if a learner wants to learn a new language, the AI ​​will recommend a daily learning plan based on the user's set goals and present quizzes according to their progress. This measures their proficiency, and the next learning steps are suggested. Through this process, learners can efficiently achieve their goals.

[0725] As an example of a prompt to input into the generative AI model, we will use the text: "Generate an appropriate learning plan and tasks daily based on the goals set by the user. Analyze progress and suggest the next steps." Based on this prompt, the generative AI model will recommend appropriate learning content.

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] Users input learning objectives and activity plan information using a terminal. This information is sent to the server as structured data by the terminal. Input includes details such as the user's goals and available schedule, while output is structured data sent to the server. The terminal provides a screen that allows users to input this information in an easy-to-understand format.

[0729] Step 2:

[0730] The server inputs the received data into a generating AI model and generates an optimized learning plan. This process uses prompts to instruct the AI ​​model to generate daily learning tasks and content. The input is formatted user data, and the output is a specific learning plan. The server saves the generated plan to a database.

[0731] Step 3:

[0732] The server sends the generated learning plan to the terminal. The terminal configures a display screen so that the learner can visually confirm the plan. The plan is organized in a way that facilitates progress. The input is the learning plan data received from the server, and the output is the display on the user interface.

[0733] Step 4:

[0734] Users progress through their learning on their device according to their learning plan. The device records the user's progress in real time and periodically sends it to the server. Input is the user's learning activity, and output is progress data. The device provides an easy-to-use interface to support learning habits.

[0735] Step 5:

[0736] The server uses evaluation tools to determine the level of understanding based on progress data and proposes the next learning step using a generative AI model. In this process, the AI ​​determines the optimal learning content using prompts. The input is progress data, and the output is the next learning suggestion. The AI ​​model has the ability to flexibly adapt to the user's characteristics.

[0737] Step 6:

[0738] The server sends the next learning plan to the terminal, which displays it in the user interface. The user receives feedback and obtains information that helps them continue learning. The input is information about the next learning step, and the output is the displayed feedback. The terminal presents information that includes elements to enhance the learner's motivation.

[0739] 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.

[0740] This invention provides more individually optimized learning support by combining an emotion engine that recognizes and analyzes learners' emotions with an online learning system. A specific embodiment is shown below.

[0741] First, learners log into the system and enter their goals and schedules. The terminal sends this information to the server, where it is stored in a database. The server uses this data to generate an optimal learning plan and activates the plan generation mechanism. Up to this point, it is the same as conventional systems, but the addition of an emotion engine provides more advanced learning support.

[0742] The emotion engine analyzes the learner's facial expressions and voice tone through sensors such as cameras and microphones built into the user's device, collecting emotion data in real time. The server receives this data and uses an emotion recognition algorithm to classify the learner's emotional state (for example, whether they are focused or stressed).

[0743] Based on this emotional information, the server suggests learning content and methods that are best suited to the learner's current state. For example, if a learner is feeling stressed, it can recommend more relaxing content and approaches. Conversely, if a high level of concentration is detected, it can suggest tasks with increased difficulty.

[0744] Furthermore, the server dynamically adjusts the learning plan and generates feedback using data analyzed by the emotion engine. This allows learners to visualize their learning progress and emotional changes, providing customized support to boost their motivation.

[0745] As a concrete example, suppose a learner has the goal of "learning a new language." The server creates a standard learning plan and displays it on the device, while simultaneously monitoring the learner's emotional state during learning using an emotion engine. If the server detects that the learner is feeling stressed while tackling complex grammatical points, it suggests switching to learning with simpler example sentences. This reduces the learner's stress and allows them to continue learning while maintaining motivation.

[0746] Thus, a learning support system that incorporates an emotional engine provides a learning environment tailored to the individual needs of learners, offering a means to achieve efficient and effective learning.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] The user logs into the online learning system and enters their learning goals and schedule into their device. The device then sends this information to the server.

[0750] Step 2:

[0751] The server stores the received goal and schedule information in a database. A plan generation mechanism is then activated to create a learning plan.

[0752] Step 3:

[0753] The plan generation system formulates an optimal learning plan based on the learner's goals, taking into account the content of the previous lesson. This plan is then transmitted to the terminal.

[0754] Step 4:

[0755] The device displays the received learning plan on the user interface. The user reviews the plan and begins their learning activities.

[0756] Step 5:

[0757] During learning, the device uses its built-in emotion engine to analyze the user's facial expressions and voice in real time. The results of this analysis are sent to the server as emotion data.

[0758] Step 6:

[0759] The server receives emotional data and uses an emotion recognition algorithm to classify the user's emotional state. For example, it determines states such as "concentrated," "stressed," and "relaxed."

[0760] Step 7:

[0761] The server adjusts the learning content based on the user's emotional state. If the user's emotions indicate stress, the learning plan is dynamically changed, for example, by switching to content more suitable for relaxation.

[0762] Step 8:

[0763] The adjusted learning plan is sent back to the device, and the user is presented with the new plan. This allows the user to continue with optimal learning activities tailored to their emotional state.

[0764] Step 9:

[0765] The server generates feedback based on the user's progress and sentiment data. The feedback is displayed on the device, allowing the user to track their learning progress and emotional changes.

[0766] Step 10:

[0767] If necessary, users can exchange opinions with other learners regarding sentiment data and progress, and communication features are supported. This allows users to gain new perspectives on their learning.

[0768] (Example 2)

[0769] 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".

[0770] In online learning systems, it has been difficult to appropriately recognize the emotional state of individual learners and provide learning support accordingly. Furthermore, there has been a lack of effective feedback to maintain learners' motivation and methods to visualize learning progress. This invention aims to solve these problems by providing a highly individualized learning environment that incorporates emotional data.

[0771] 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.

[0772] In this invention, the server includes a plan generation means, a progress management means, an emotion recognition means, a suggestion means, a display means, and an information transmission means. This makes it possible to provide an individually optimized learning plan according to the learner's emotional state and to support the effective progress of learning while maintaining the learner's motivation.

[0773] A "plan generation means" is a method or apparatus equipped with the function of generating an optimal learning plan based on goal and schedule information received from the learner.

[0774] "Progress management means" refers to a method or device for collecting and managing learner progress information during the process of learning according to a generated learning plan.

[0775] "Emotion recognition means" refers to a method or apparatus for collecting learner's emotional data via voice and image input devices, and for recognizing and classifying that emotional state.

[0776] "Proposed means" refers to a method or apparatus for providing learning content suitable for learners based on emotional information and for dynamically adjusting the learning plan.

[0777] "Display means" refers to a method or device for visualizing learners' learning outcomes and generating and displaying feedback to maintain motivation.

[0778] "Information transmission means" refers to methods or devices for forming learning communities and supporting information sharing among learners.

[0779] This specification provides an individually optimized online learning support system that takes learners' emotions into account. This system uses emotion recognition technology to analyze the learner's real-time emotional state and dynamically adjust the learning experience. The hardware and software used, data processing methods, and specific examples are described below.

[0780] Hardware and software

[0781] The device is equipped with a camera and microphone to collect the learner's facial expressions and voice. The device has emotion recognition software installed to analyze the collected audio and image data in real time.

[0782] The server is equipped with a database management system that stores goals, schedules, and sentiment data received from learners. The server also implements algorithms for classifying sentiment data and generative AI models.

[0783] Data processing and calculations

[0784] When a user enters their goals and schedule, the device sends this information to the server, where it is registered in the database.

[0785] Emotional data collected by the device is sent to a server and analyzed by an emotion recognition algorithm. This classifies the learner's emotional state into categories such as concentration, stress, and relaxation.

[0786] Based on emotional information, the server suggests learning content suitable for the learner and automatically adjusts the learning plan to provide an optimal learning environment.

[0787] Specific example

[0788] For example, suppose a learner has the goal of "learning a new language." While the learner is working on a difficult grammatical section, the device's camera and microphone detect signs of stress from the user's facial expressions and voice. The server then suggests switching to practicing simpler phrases and displays this information on the device.

[0789] Example of a prompt

[0790] When using a generative AI model, the prompt message used is "Please suggest ways to adjust the learning plan when the user is feeling stressed."

[0791] This system enables personalized learning support through real-time analysis of learners' emotions.

[0792] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0793] Step 1:

[0794] The user logs into the online learning platform and enters their goals and schedule information into their device. The device then sends the entered goals and schedule information to the server. The server records this information in a database and uses it as basic data for planning future learning. The input is the goals and schedule information, and the output is registration to the database.

[0795] Step 2:

[0796] The server executes a plan generation algorithm based on the database to generate the optimal learning plan for the learner. The generated learning plan takes the learner's goals and schedule into account, setting sequential learning steps. Here, learner information from the database is used as input data, and the output is a specific learning plan. The generated learning plan is sent to the terminal and presented to the user.

[0797] Step 3:

[0798] The user begins learning using a device equipped with a camera and microphone. The device's emotion recognition software collects the user's facial expressions and voice via the camera and microphone, and analyzes the emotion data in real time. The device software receives the collected audio and image data as input, uses an emotion recognition algorithm to determine a specific emotional state, and sends the result to the server. The output is the learner's emotional state.

[0799] Step 4:

[0800] The server analyzes the received emotional data and uses a generative AI model to classify the learner's emotional state. Based on this analysis, the server dynamically adjusts the learning plan. For example, it might suggest a more challenging task if the user is focused, or a more relaxed approach if they are stressed. The input is emotional data and the current learning plan, and the output is the adjusted learning content.

[0801] Step 5:

[0802] The server sends a tailored learning plan and feedback to the device, which then displays it to the user. The feedback includes advice based on the learner's progress and emotions. Users can review the feedback as they progress, maintaining motivation and continuing their learning. The output consists of visualized feedback information and a tailored learning plan.

[0803] (Application Example 2)

[0804] 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".

[0805] In online learning, providing individually optimized learning support tailored to each learner's state and emotions is difficult. Therefore, there is a need to recognize learners' emotional states in real time and flexibly adjust learning plans based on those emotions. However, conventional systems lack this kind of dynamic response. This results in a challenge in effectively alleviating learners' stress and decreased motivation.

[0806] 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.

[0807] In this invention, the server includes emotion recognition means for recognizing and analyzing the learner's emotional state, adjustment means for dynamically adjusting the learning content and method based on the learner's emotional information, and progress management means for collecting the learner's progress information. This makes it possible to provide a customized learning experience that is tailored to the learner's emotions and learning progress.

[0808] A "plan generation means" is an element that has the function of creating an optimal learning plan based on goal and schedule information received from the learner.

[0809] A "progress management tool" is an element that has the function of continuously collecting the learner's progress in the learning process, which proceeds according to the generated learning plan.

[0810] "Evaluation methods" refer to elements used to assess learners' understanding through periodic tests and to adjust learning content based on the results.

[0811] A "display means" is an element that has the function of generating feedback to maintain learning motivation by visually showing the learner's learning outcomes.

[0812] An "emotion recognition tool" is an element that has the function of recognizing and analyzing the learner's emotional state using sensors such as cameras and microphones.

[0813] A "modification mechanism" is an element that has the function of flexibly changing the learning content and approach according to the learner's current situation, based on emotional information.

[0814] This system consists of a server and user terminals. The server provides emotion recognition capabilities to recognize the learner's emotional state in real time. The user terminals are equipped with cameras and microphones, and they execute emotion recognition algorithms based on image and audio data collected from these sensors. Specifically, they use OpenCV to extract facial expression features and TensorFlow models to classify emotions.

[0815] The server has an adjustment mechanism that dynamically adjusts learning content to suit the learner's current state based on information obtained through emotion recognition. Learners judged to be highly motivated are presented with more advanced content, while those experiencing stress are provided with relaxing content. This dynamic adjustment is performed by a Python program implemented on the server.

[0816] Furthermore, the server continuously collects and analyzes learner progress information using progress management tools. This makes it possible to continuously generate optimal learning plans tailored to the learner's growth.

[0817] As a concrete example, when a child is learning a new language, an emotion recognition system analyzes the child's facial expressions and collects data on their level of concentration. If the results indicate that the child is clearly experiencing stress, the server instructs the system to switch to a learning method that involves simple games.

[0818] An example of a prompt to input into a generative AI model is, "Please suggest relaxing learning content for a child who is stressed by a complex problem." Based on this prompt, the AI ​​will suggest appropriate learning support.

[0819] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0820] Step 1:

[0821] The device uses its camera and microphone to collect the user's facial expression and voice data. This data serves as input for emotion recognition. The device stores this data locally and prepares to send it to the server in real time.

[0822] Step 2:

[0823] The server receives facial expression and audio data sent from the terminal and uses OpenCV to extract facial expression features from the image data. The extracted features become input to the emotion recognition algorithm. This allows the server to obtain the user's basic emotional state (e.g., joy, anger, sadness, surprise).

[0824] Step 3:

[0825] The server uses a TensorFlow emotion recognition model to classify emotions by linking extracted facial features with voice tone data. The classified emotional state is then used as input for subsequent processing. This allows for a more accurate understanding of the user's emotional state.

[0826] Step 4:

[0827] Based on the emotion recognition results, the server uses a generative AI model to suggest learning content that is best suited to the user's current emotional state. Here, the AI ​​model is input with a prompt such as, "Please suggest learning content that will help a child relax when they are stressed by a complex problem," and the optimal learning content is obtained as output.

[0828] Step 5:

[0829] The server dynamically adjusts the learning plan based on the output of the AI ​​model and sends the learning content to the terminal. The terminal presents this content to the user visually and audibly. This allows the user to receive real-time, emotion-responsive learning support.

[0830] Step 6:

[0831] The device continuously collects the user's learning progress and sends it to the server. The server analyzes this progress information and uses it to dynamically update the next learning plan. This allows for adjustments to maximize long-term learning effectiveness.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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."

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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 as being incorporated by reference.

[0853] The following is further disclosed regarding the embodiments described above.

[0854] (Claim 1)

[0855] A plan generation means that generates an optimal learning plan based on goal and schedule information received from learners,

[0856] In the process of learning according to the generated learning plan, a progress management means is used to collect learner progress information,

[0857] An assessment method that provides periodic tests to evaluate learners' understanding and adjusts the learning content based on the results,

[0858] A display means for visualizing learners' learning outcomes and generating feedback to maintain motivation,

[0859] A means of communication that forms learning communities and supports information sharing with other learners,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising an analysis means for analyzing the learner's learning history and trends based on the learner's input data and dynamically updating the next learning plan.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising interface means for providing a user interface to visually present the generated learning plan and learning progress.

[0865] "Example 1"

[0866] (Claim 1)

[0867] A plan generation means that creates an optimal learning plan based on goal and schedule information obtained from the user,

[0868] As users progress through their learning according to the established learning plan, a progress management system is used to collect information on their progress.

[0869] An evaluation method that provides periodic tests to assess users' understanding and improves learning content based on the results,

[0870] A display means that visualizes the user's learning outcomes and generates feedback to maintain learning motivation,

[0871] A means of interaction that forms learning groups and supports information sharing with other users,

[0872] A means of human-machine interaction for visually presenting plan-based learning,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising an analysis means for analyzing the user's learning history and trends based on the user's input information and dynamically updating the next learning plan.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising human-machine interaction means for visually presenting the generated learning plan and learning progress.

[0878] "Application Example 1"

[0879] (Claim 1)

[0880] A plan generation means that generates an optimal learning plan based on goal and activity plan information received from learners,

[0881] In the process of learning according to the generated learning plan, a progress management means is used to collect learner progress information,

[0882] An assessment method that periodically provides questions to evaluate the learner's level of understanding and adjusts the learning content based on the results,

[0883] A display means for displaying learners' learning outcomes and generating feedback to maintain motivation,

[0884] A means of communication that creates a learning environment and supports information sharing with other learners,

[0885] A recommendation method for optimizing learning plans based on learner progress information using a generative AI model,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, comprising an analysis means that analyzes the learner's learning history and trends based on the learner's input information and dynamically updates the next learning plan, and training the learning content using prompt sentences generated by a generative AI model.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising an interface means for visually presenting the generated learning plan and learning progress, and for forming an online forum with other learners.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] A plan generation means that generates an optimal learning plan based on goal and schedule information received from learners,

[0894] In the process of learning according to the generated learning plan, a progress management means is used to collect learner progress information,

[0895] An emotion recognition means that collects learner emotion data via voice and image input devices, recognizes and classifies the emotional state,

[0896] A suggestion mechanism that proposes learning content suitable for learners based on emotional information and dynamically adjusts the learning plan,

[0897] A display means for visualizing learners' learning outcomes and generating feedback to maintain motivation,

[0898] A means of communication that forms learning communities and supports information sharing with other learners,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising an analysis means for analyzing the learner's learning history and trends based on the learner's input data and dynamically updating the next learning plan.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising interface means for providing a user interface to visually present the generated learning plan and learning progress.

[0904] "Application example 2 when combining with an emotional engine"

[0905] (Claim 1)

[0906] A plan generation means that generates an optimal learning plan based on goal and schedule information received from learners,

[0907] In the process of learning according to the generated learning plan, a progress management means is used to collect learner progress information,

[0908] An evaluation method that provides periodic tests to assess learners' understanding and adjusts the learning content based on the results,

[0909] A display means for visualizing learners' learning outcomes and generating feedback to maintain motivation,

[0910] A means for recognizing and analyzing the emotional state of learners,

[0911] An adjustment mechanism that dynamically adjusts learning content and methods based on learner emotional information,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, comprising an analysis means for analyzing the learner's learning history and trends based on the learner's input data and dynamically updating the next learning plan.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising interface means for providing a user interface to visually present the generated learning plan and learning progress. [Explanation of Symbols]

[0917] 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 plan generation means that generates an optimal learning plan based on goal and activity plan information received from learners, In the process of learning according to the generated learning plan, a progress management means is used to collect learner progress information, An assessment method that periodically provides questions to evaluate the learner's level of understanding and adjusts the learning content based on the results, A display means for displaying learners' learning outcomes and generating feedback to maintain motivation, A means of communication that creates a learning environment and supports information sharing with other learners, A recommendation method for optimizing learning plans based on learner progress information using a generative AI model, A system that includes this.

2. The system according to claim 1, comprising an analysis means that analyzes the learner's learning history and trends based on the learner's input information and dynamically updates the next learning plan, and training the learning content using prompt sentences generated by a generative AI model.

3. The system according to claim 1, comprising an interface means for visually presenting the generated learning plan and learning progress, and for forming an online forum with other learners.

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