system

Abstract

The system according to this embodiment aims to efficiently manage the user's English learning progress and provide an optimal learning plan. [Solution] The system according to the embodiment comprises a level check unit, a learning plan provision unit, a feedback unit, and a progress management unit. The level check unit performs an initial level check. The learning plan provision unit provides the user with an optimal learning plan based on the information obtained by the level check unit. The feedback unit provides feedback each time a stage is cleared. The progress management unit manages progress based on the information obtained by the feedback unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently manage the progress of a user's English learning and provide an optimal learning plan.

[0005] The system according to the embodiment aims to efficiently manage the progress of a user's English learning and provide an optimal learning plan.

Means for Solving the Problems

[0007] The system according to this embodiment can efficiently manage the user's English learning progress and provide an optimal learning plan. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, 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), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Play English Agent according to an embodiment of the present invention is an AI-driven application that allows users to learn English in a game-like manner. First, the user undergoes an initial level check, and the AI ​​provides the user with an optimal learning plan. The learning plan is customized according to the user's learning progress, aiming to reduce the time spent on language learning and improve efficiency. After clearing each stage, the AI ​​provides real-time feedback and manages progress. This maximizes the user's learning efficiency and enables improved proficiency through continuous learning support. The AI ​​agent provides learning materials appropriate to the user's level and provides real-time progress feedback. Furthermore, by integrating gamification elements, it maintains learning motivation and deepens understanding through gradual learning progress. The AI ​​learns the user's responses, automatically generates individualized learning plans, and dynamically adjusts learning materials based on progress. This application targets students and young professionals in their teens to thirties who are interested in learning English, addressing the challenges of difficulty in continuing English learning and the monotonous and easily boring nature of learning methods. By incorporating game elements, it promotes continued learning, and the AI ​​analyzes the user's learning style to optimize it individually. The generative AI uses natural language processing AI to generate individualized learning plans and leverages adaptive AI that learns from user responses. This reduces access barriers to English education and provides an environment where more people can learn in an enjoyable way. As a result, PlayEnglish Agent can maximize user learning efficiency and improve proficiency through continuous learning support.

[0029] The Play English agent according to this embodiment comprises a level check unit, a learning plan provision unit, a feedback unit, and a progress management unit. The level check unit performs an initial level check. For example, when a user uses the app for the first time, the level check unit conducts a simple test to evaluate the user's English level. The level check unit can determine an appropriate level based on the user's answers. For example, the level check unit calculates the accuracy rate of the questions answered by the user and determines the level based on the result. The level check unit can also adjust the level considering the user's response time. For example, if the user answers accurately in a short amount of time, the level can be set higher. The learning plan provision unit provides the user with an optimal learning plan based on the information obtained by the level check unit. For example, the learning plan provision unit automatically generates a curriculum according to the user's level. The learning plan provision unit can adjust the curriculum according to the user's learning progress. For example, when a user clears a particular stage, the content of the next stage is automatically updated. The feedback unit provides feedback to the user each time a stage is cleared. The feedback unit evaluates the accuracy rate and response time based on the user's answers and provides feedback. The feedback unit can provide appropriate advice according to the user's learning progress. For example, if the user is struggling with a particular problem, it provides an explanation for that problem. The progress management unit manages the user's progress based on the information obtained by the feedback unit. The progress management unit records the user's learning history and visualizes the progress status. The progress management unit can adjust the learning plan according to the user's learning pace. For example, if the user continues learning, the progress management unit automatically updates the content of the next stage. As a result, the Play English agent according to the embodiment can maximize the user's learning efficiency and achieve improved proficiency through continuous learning support.

[0030] The Level Check Unit conducts an initial level check. For example, when a user first uses the app, the Level Check Unit administers a simple test to assess the user's English level. Specifically, the Level Check Unit presents multiple questions covering various skills such as grammar, vocabulary, listening, and reading, and collects the user's responses. These questions are designed to comprehensively evaluate the user's English ability, and the difficulty levels range widely from beginner to advanced. Based on the user's responses, the Level Check Unit can determine an appropriate level. For example, the Level Check Unit calculates the accuracy rate of the questions the user answered and determines the level based on the results. The Level Check Unit can also adjust the level considering the user's response time. For example, if a user answers accurately in a short amount of time, the level can be set higher. Furthermore, the Level Check Unit can analyze the user's response patterns and error tendencies to identify weaknesses in specific skills. This allows the Level Check Unit to accurately assess the user's English ability and provide foundational information for offering learning plans tailored to individual needs.

[0031] The Learning Plan Provisioning Unit provides users with the most suitable learning plan based on information obtained by the Level Checking Unit. For example, the Learning Plan Provisioning Unit automatically generates a curriculum tailored to the user's level. Specifically, it creates a curriculum that balances learning content for each skill, such as grammar, vocabulary, listening, reading, and speaking, in order to improve the user's English ability. The Learning Plan Provisioning Unit can adjust the curriculum according to the user's learning progress. For example, if a user clears a certain stage, it automatically updates the content for the next stage. The Learning Plan Provisioning Unit can also provide learning content that focuses on specific skills to reinforce the user's weaknesses. For example, if a user has difficulty with listening, it will provide a curriculum that includes many listening practice exercises. Furthermore, the Learning Plan Provisioning Unit can adjust the difficulty level and pace of the learning content based on the user's learning history and feedback. This allows the Learning Plan Provisioning Unit to maximize the user's learning efficiency and support continuous learning.

[0032] The feedback unit provides feedback to the user after each stage is completed. For example, the feedback unit evaluates the user's accuracy rate and response time based on their answers and provides feedback accordingly. Specifically, the feedback unit calculates the accuracy rate of the questions the user answered and shows in detail which questions were answered correctly and which were answered incorrectly. It also evaluates the user's response time to check whether they were able to answer quickly. The feedback unit can provide appropriate advice according to the user's learning progress. For example, if a user is struggling with a particular question, it will provide an explanation for that question. The explanation will include not only the correct answer to the question, but also a detailed explanation of why that answer is correct and how to think about it. Furthermore, the feedback unit can evaluate the user's learning attitude and effort and provide positive feedback to boost motivation. For example, if a user achieves a certain goal, it will send words of praise and encouragement. In this way, the feedback unit can maintain the user's motivation to learn and support effective learning.

[0033] The progress management unit manages user progress based on information obtained from the feedback unit. For example, the progress management unit records the user's learning history and visualizes their progress. Specifically, the progress management unit meticulously records which stages the user has completed, how much time they spent on each problem, and the degree of progress they have made in each skill. This information is displayed in visual formats such as graphs and charts, allowing users to grasp their learning status at a glance. The progress management unit can adjust the learning plan according to the user's learning pace. For example, if the user is continuing their learning, the progress management unit automatically updates the content of the next stage. Also, if the user is falling behind in their learning, the progress management unit reviews the learning plan and adjusts it so that the user can continue learning without difficulty. Furthermore, the progress management unit can set learning goals for the user and monitor their achievement. This allows the progress management unit to maximize the user's learning efficiency and achieve improved proficiency through continuous learning support.

[0034] The PlayEnglish agent includes a material provision unit that provides learning materials appropriate to the user's level. The material provision unit can, for example, automatically select and provide learning materials appropriate to the user's level. The material provision unit can adjust the content of the learning materials according to the user's learning progress. For example, if the user clears a particular stage, it can automatically update the materials for the next stage. The material provision unit can also select the format of the learning materials according to the user's learning style. For example, if the user prefers visual learning, it can provide visual learning materials. This enhances the effectiveness of learning by providing learning materials appropriate to the user's level. Some or all of the above processes in the material provision unit may be performed using AI, for example, or without AI. For example, the material provision unit can have a generating AI perform the selection of learning materials appropriate to the user's level.

[0035] The PlayEnglish Agent includes a real-time feedback unit that provides real-time progress feedback. The real-time feedback unit provides feedback in real time as the user progresses through their learning. The real-time feedback unit can provide immediate feedback based on the user's answers. For example, it can provide feedback on the accuracy rate and answer time immediately after the user solves a problem. The real-time feedback unit can also provide appropriate advice according to the user's learning progress. For example, if the user is struggling with a particular problem, it can provide an explanation of that problem in real time. This helps maintain learning motivation by providing real-time progress feedback. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's answers into a generating AI and have the generating AI generate the feedback.

[0036] The PlayEnglish agent includes a gamification unit that integrates gamification elements. The gamification unit, for example, incorporates game elements as the user progresses through their learning. The gamification unit can adjust the game elements according to the user's learning progress. For example, if the user clears a certain stage, a new game element can be added in the next stage. The gamification unit can also select the content of the game elements according to the user's learning style. For example, if the user is learning with a competitive spirit, a ranking function can be provided. In this way, incorporating game elements can increase motivation for learning. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can have a generating AI perform the selection of game elements according to the user's learning progress.

[0037] The PlayEnglish agent includes a response learning unit that learns user responses. The response learning unit collects and learns user responses as the user progresses through the learning process. Based on user responses, the response learning unit can adjust the learning plan. For example, if a user shows a positive response to a particular problem, it will reinforce content related to that problem. The response learning unit can also select the content of the learning plan according to the user's learning style. For example, if a user prefers visual learning, it can provide visual learning materials. By learning user responses, it is possible to provide an individually optimized learning plan. Some or all of the above processing in the response learning unit may be performed using AI, for example, or without AI. For example, the response learning unit can input user response data into a generating AI and have the generating AI adjust the learning plan.

[0038] The PlayEnglish Agent includes an automatic generation unit that automatically generates individualized learning plans. The automatic generation unit automatically generates individualized learning plans according to the user's learning progress, for example. The automatic generation unit can adjust the content of the learning plan based on the user's learning history. For example, if the user clears a particular stage, it automatically updates the learning plan for the next stage. The automatic generation unit can also select the format of the learning plan according to the user's learning style. For example, if the user prefers visual learning, it can provide a learning plan that includes visual materials. By automatically generating individualized learning plans, the system can provide the user with the most suitable learning plan. Some or all of the above-described processes in the automatic generation unit may be performed using AI, for example, or without AI. For example, the automatic generation unit can input the user's learning history into a generation AI and have the generation AI perform the generation of the learning plan.

[0039] The PlayEnglish agent includes a dynamic adjustment unit that dynamically adjusts learning materials based on progress. The dynamic adjustment unit dynamically adjusts the content of learning materials according to the user's learning progress. The dynamic adjustment unit can adjust the content of learning materials based on the user's learning history. For example, if a user clears a particular stage, it automatically updates the learning materials for the next stage. The dynamic adjustment unit can also select the format of the learning materials according to the user's learning style. For example, if a user prefers visual learning, it can provide visual learning materials. This maximizes the effectiveness of learning by dynamically adjusting learning materials based on progress. Some or all of the above-described processes in the dynamic adjustment unit may be performed using AI, for example, or without AI. For example, the dynamic adjustment unit can input the user's learning progress data into a generating AI and have the generating AI perform the adjustment of learning materials.

[0040] The level check unit can select the optimal checking method by referring to the user's past learning history during the level check. For example, the level check unit can prioritize presenting questions that the user has struggled with in the past. The level check unit can also present questions in a balanced manner that include questions in areas where the user excels. The level check unit can also select the optimal checking method (multiple choice, written response, etc.) from the user's past learning history. In this way, the optimal checking method can be selected by referring to the user's past learning history. Some or all of the above processing in the level check unit may be performed using AI, for example, or without using AI. For example, the level check unit can input the user's past learning history data into a generating AI and have the generating AI perform the selection of the optimal checking method.

[0041] The level check unit can provide feedback based on the user's current learning environment during a level check. For example, if the user is in a quiet environment, the level check unit can provide detailed feedback. If the user is in a noisy environment, the level check unit can also provide concise feedback. If the user is on the move, the level check unit can also provide voice feedback. This allows for appropriate feedback to be provided based on the user's current learning environment. Some or all of the above processing in the level check unit may be performed using AI, for example, or without AI. For example, the level check unit can input the user's current learning environment data into a generating AI and have the generating AI generate the feedback.

[0042] The level check unit can prioritize presenting highly relevant check items by considering the user's geographical location during the level check. For example, if the user is in an English-speaking country, the level check unit may present check items related to everyday conversation. If the user is in a non-English-speaking country, the level check unit may also present check items related to business English. If the user is traveling, the level check unit may also present check items related to travel English. In this way, by considering the user's geographical location, it is possible to provide highly relevant check items. Some or all of the above processing in the level check unit may be performed using AI, for example, or without AI. For example, the level check unit can input the user's geographical location data into a generating AI and have the generating AI select the check items.

[0043] The level check unit can analyze the user's social media activity during the level check and generate relevant check items. For example, the level check unit can generate check items related to words the user frequently uses on social media. The level check unit can also generate check items related to topics the user is interested in. The level check unit can also generate check items related to the content of accounts the user follows. In this way, relevant check items can be provided by analyzing the user's social media activity. Some or all of the above processing in the level check unit may be performed using AI, for example, or without AI. For example, the level check unit can input the user's social media activity data into a generating AI and have the generating AI select the check items.

[0044] The learning plan provider can select the optimal plan by referring to the user's past learning history when providing a learning plan. For example, the learning plan provider can provide a plan that focuses on areas the user has struggled with in the past. The learning plan provider can also provide a plan that balances learning across areas the user excels at. The learning plan provider can also select the optimal learning method (video, text, etc.) from the user's past learning history. In this way, the learning plan provider can provide the optimal learning plan by referring to the user's past learning history. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or without using AI. For example, the learning plan provider can input the user's past learning history data into a generating AI and have the generating AI select the optimal plan.

[0045] The learning plan provider can customize the plan based on the user's current learning environment when providing the plan. For example, if the user is in a quiet environment, the learning plan provider can provide a plan that allows for focused learning. If the user is in a noisy environment, the learning plan provider can also provide a plan that allows for short-term learning. If the user is on the move, the learning plan provider can also provide an audio learning plan. In this way, by customizing the plan based on the user's current learning environment, an appropriate learning plan can be provided. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or without AI. For example, the learning plan provider can input the user's current learning environment data into a generating AI and have the generating AI perform the plan customization.

[0046] The learning plan provider can prioritize providing highly relevant plans by considering the user's geographical location when providing learning plans. For example, if the user is in an English-speaking country, the learning plan provider can provide plans related to everyday conversation. If the user is in a non-English-speaking country, the learning plan provider can also provide plans related to business English. If the user is traveling, the learning plan provider can also provide plans related to travel English. In this way, by considering the user's geographical location, it is possible to provide highly relevant learning plans. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or not using AI. For example, the learning plan provider can input the user's geographical location data into a generating AI and have the generating AI select a plan.

[0047] The learning plan provider can analyze the user's social media activity and provide relevant plans when providing learning plans. For example, the learning plan provider can provide plans related to words the user frequently uses on social media. The learning plan provider can also provide plans related to topics the user is interested in. The learning plan provider can also provide plans related to the content of accounts the user follows. In this way, relevant learning plans can be provided by analyzing the user's social media activity. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or not using AI. For example, the learning plan provider can input the user's social media activity data into a generating AI and have the generating AI select a plan.

[0048] The feedback unit can select the most appropriate feedback by referring to the user's past learning history when providing feedback. For example, the feedback unit can provide feedback on problems the user has struggled with in the past. The feedback unit can also provide a balanced set of feedback on areas the user excels in. The feedback unit can also select the most appropriate feedback method (text, audio, etc.) from the user's past learning history. This allows the feedback unit to provide optimal feedback by referring to the user's past learning history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past learning history data into a generating AI and have the generating AI select the feedback.

[0049] The feedback unit can customize the feedback based on the user's current learning environment when providing it. For example, if the user is in a quiet environment, the feedback unit can provide detailed feedback. If the user is in a noisy environment, the feedback unit can also provide concise feedback. If the user is on the move, the feedback unit can also provide voice feedback. This allows for the provision of appropriate feedback by customizing it based on the user's current learning environment. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's current learning environment data into a generating AI and have the generating AI perform the customization of the feedback.

[0050] The feedback unit can prioritize providing highly relevant feedback by considering the user's geographical location when providing feedback. For example, if the user is in an English-speaking country, the feedback unit can provide feedback related to everyday conversation. If the user is in a non-English-speaking country, the feedback unit can also provide feedback related to business English. If the user is traveling, the feedback unit can also provide feedback related to travel English. This allows the system to provide highly relevant feedback by considering the user's geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location data into a generating AI and have the generating AI select the feedback.

[0051] The feedback unit can analyze the user's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide feedback related to words the user frequently uses on social media. The feedback unit can also provide feedback related to topics the user is interested in. The feedback unit can also provide feedback related to the content of accounts the user follows. In this way, relevant feedback can be provided by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI select the feedback.

[0052] The progress management unit can select the optimal management method by referring to the user's past learning history when managing progress. For example, the progress management unit can focus on managing progress in areas where the user has struggled in the past. The progress management unit can also manage progress in areas where the user excels in a balanced manner. The progress management unit can also select the optimal progress management method (graph, list, etc.) from the user's past learning history. In this way, the optimal progress management method can be provided by referring to the user's past learning history. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input the user's past learning history data into a generating AI and have the generating AI select the management method.

[0053] The progress management unit can customize the management method based on the user's current learning environment when managing progress. For example, if the user is in a quiet environment, the progress management unit can provide detailed progress management. If the user is in a noisy environment, the progress management unit can also provide concise progress management. If the user is on the move, the progress management unit can also provide voice-based progress management. This allows for appropriate progress management by customizing the management method based on the user's current learning environment. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the user's current learning environment data into a generating AI and have the generating AI perform the customization of the management method.

[0054] The progress management unit can select the optimal management method when managing progress, taking into account the user's geographical location information. For example, if the user is in an English-speaking country, the progress management unit can provide progress management related to everyday conversation. If the user is in a non-English-speaking country, the progress management unit can also provide progress management related to business English. If the user is traveling, the progress management unit can also provide progress management related to travel English. In this way, the optimal progress management method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input the user's geographical location information data into a generating AI and have the generating AI select the management method.

[0055] The progress management unit can analyze the user's social media activity and provide relevant management methods during progress management. For example, the progress management unit can provide progress management related to words that the user frequently uses on social media. The progress management unit can also provide progress management related to topics that the user is interested in. The progress management unit can also provide progress management related to the content of accounts that the user follows. In this way, by analyzing the user's social media activity, it is possible to provide relevant progress management methods. Some or all of the above processing in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input the user's social media activity data into a generating AI and have the generating AI select a management method.

[0056] The learning material provider can select the most suitable learning materials by referring to the user's past learning history when providing materials. For example, the learning material provider can focus on providing materials in areas where the user has previously struggled. The learning material provider can also provide a balanced selection of materials in areas where the user excels. The learning material provider can also select the most suitable learning material format (video, text, etc.) based on the user's past learning history. This allows the system to provide the most suitable learning materials by referring to the user's past learning history. Some or all of the above processes in the learning material provider may be performed using AI, for example, or without AI. For example, the learning material provider can input the user's past learning history data into a generating AI and have the generating AI select the learning materials.

[0057] The material provision unit can prioritize providing highly relevant materials by considering the user's geographical location when providing materials. For example, if the user is in an English-speaking country, the material provision unit can provide materials related to everyday conversation. If the user is in a non-English-speaking country, the material provision unit can also provide materials related to business English. If the user is traveling, the material provision unit can also provide materials related to travel English. In this way, highly relevant materials can be provided by considering the user's geographical location. Some or all of the above processing in the material provision unit may be performed using AI, for example, or without AI. For example, the material provision unit can input the user's geographical location data into a generating AI and have the generating AI select the materials.

[0058] The real-time feedback unit can select the most appropriate feedback by referring to the user's past learning history when providing real-time feedback. For example, the real-time feedback unit can provide feedback on problems the user has struggled with in the past. The real-time feedback unit can also provide a balanced set of feedback on areas the user excels in. The real-time feedback unit can also select the most appropriate feedback method (text, audio, etc.) from the user's past learning history. This allows the unit to provide optimal feedback by referring to the user's past learning history. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's past learning history data into a generating AI and have the generating AI select the feedback.

[0059] The real-time feedback unit can prioritize providing highly relevant feedback by considering the user's geographical location when providing real-time feedback. For example, if the user is in an English-speaking country, the real-time feedback unit can provide feedback related to everyday conversation. If the user is in a non-English-speaking country, the real-time feedback unit can also provide feedback related to business English. If the user is traveling, the real-time feedback unit can also provide feedback related to travel English. This allows for the provision of highly relevant feedback by considering the user's geographical location. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's geographical location data into a generating AI and have the generating AI select the feedback.

[0060] The gamification unit can select the most suitable game elements by referring to the user's past learning history when providing game elements. For example, the gamification unit can focus on providing game elements in areas where the user has previously struggled. The gamification unit can also provide a balanced selection of game elements in areas where the user excels. The gamification unit can also select the most suitable game elements (puzzles, quizzes, etc.) from the user's past learning history. In this way, the gamification unit can provide the most suitable game elements by referring to the user's past learning history. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's past learning history data into a generating AI and have the generating AI perform the selection of game elements.

[0061] The gamification unit can prioritize providing highly relevant game elements by considering the user's geographical location when providing game elements. For example, if the user is in an English-speaking country, the gamification unit can provide game elements related to everyday conversation. If the user is in a non-English-speaking country, the gamification unit can also provide game elements related to business English. If the user is traveling, the gamification unit can also provide game elements related to travel English. In this way, highly relevant game elements can be provided by considering the user's geographical location. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's geographical location data into a generating AI and have the generating AI select game elements.

[0062] The reaction learning unit can select the optimal learning method by referring to the user's past learning history during reaction learning. For example, the reaction learning unit can focus on providing reaction learning in areas where the user has previously struggled. The reaction learning unit can also provide a balanced selection of reaction learning in areas where the user excels. The reaction learning unit can also select the optimal reaction learning method (video, text, etc.) from the user's past learning history. In this way, the optimal reaction learning method can be provided by referring to the user's past learning history. Some or all of the above processing in the reaction learning unit may be performed using AI, for example, or without using AI. For example, the reaction learning unit can input the user's past learning history data into a generating AI and have the generating AI select the learning method.

[0063] The response learning unit can select the optimal learning method during response learning by considering the user's geographical location information. For example, if the user is in an English-speaking country, the response learning unit can provide response learning related to everyday conversation. If the user is in a non-English-speaking country, the response learning unit can also provide response learning related to business English. If the user is traveling, the response learning unit can also provide response learning related to travel English. In this way, the optimal response learning method can be provided by considering the user's geographical location information. Some or all of the above processing in the response learning unit may be performed using AI, for example, or without using AI. For example, the response learning unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the learning method.

[0064] The automatic generation unit can select the optimal generation method by referring to the user's past learning history during automatic generation. For example, the automatic generation unit can automatically generate content that focuses on areas the user has struggled with in the past. The automatic generation unit can also automatically generate content in a balanced manner that includes areas the user excels at. The automatic generation unit can also select the optimal generation method (video, text, etc.) from the user's past learning history. In this way, the system can provide the optimal generation method by referring to the user's past learning history. Some or all of the above-described processes in the automatic generation unit may be performed using AI, for example, or without AI. For example, the automatic generation unit can input the user's past learning history data into a generation AI and have the generation AI select the generation method.

[0065] The automatic generation unit can select the optimal generation method by considering the user's geographical location information during automatic generation. For example, if the user is in an English-speaking country, the automatic generation unit can automatically generate content related to everyday conversation. If the user is in a non-English-speaking country, the automatic generation unit can also automatically generate content related to business English. If the user is traveling, the automatic generation unit can also automatically generate content related to travel English. In this way, the optimal generation method can be provided by considering the user's geographical location information. Some or all of the above processing in the automatic generation unit may be performed using AI, for example, or without using AI. For example, the automatic generation unit can input the user's geographical location information data into a generation AI and have the generation AI select the generation method.

[0066] The dynamic adjustment unit can select the optimal adjustment method by referring to the user's past learning history during dynamic adjustment. For example, the dynamic adjustment unit can dynamically adjust content that the user has struggled with in the past. The dynamic adjustment unit can also dynamically adjust content in a balanced manner that includes areas in which the user excels. The dynamic adjustment unit can also select the optimal adjustment method (video, text, etc.) from the user's past learning history. In this way, the optimal adjustment method can be provided by referring to the user's past learning history. Some or all of the above processing in the dynamic adjustment unit may be performed using AI, for example, or without using AI. For example, the dynamic adjustment unit can input the user's past learning history data into a generating AI and have the generating AI perform the selection of the adjustment method.

[0067] The dynamic adjustment unit can select the optimal adjustment method by considering the user's geographical location information during dynamic adjustment. For example, if the user is in an English-speaking country, the dynamic adjustment unit can dynamically adjust content related to everyday conversation. If the user is in a non-English-speaking country, the dynamic adjustment unit can also dynamically adjust content related to business English. If the user is traveling, the dynamic adjustment unit can also dynamically adjust content related to travel English. In this way, the optimal adjustment method can be provided by considering the user's geographical location information. Some or all of the above processing in the dynamic adjustment unit may be performed using AI, for example, or without using AI. For example, the dynamic adjustment unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the adjustment method.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] PlayEnglish Agent can select the format of the learning plan according to the user's learning style. For example, if the user prefers visual learning, a learning plan centered on visual materials can be provided. If the user prefers auditory learning, a learning plan centered on audio materials can be provided. Furthermore, if the user prefers practical learning, interactive materials can be provided. This allows for the provision of an optimal learning plan tailored to the user's learning style, thereby enhancing the effectiveness of learning.

[0070] PlayEnglish Agent can analyze a user's learning history and provide rewards based on their learning progress. For example, users can earn badges or points when they complete certain stages. It can also offer bonus points for continuous learning. Furthermore, it can provide special rewards when users achieve specific goals. This can increase user motivation and encourage continuous learning.

[0071] PlayEnglish Agent can gradually increase the difficulty of the learning content according to the user's learning progress. For example, once a user completes the beginner level, it can provide intermediate level learning content. Furthermore, once the user completes the intermediate level, it can provide advanced level learning content. And once the user completes the advanced level, it can provide specialized learning content. This allows for the provision of optimal learning content tailored to the user's progress, thereby enhancing the effectiveness of learning.

[0072] PlayEnglish Agent can analyze a user's learning history and adjust the learning plan according to their progress. For example, if a user is struggling at a particular stage, it can provide a learning plan that reinforces the content of that stage. It can also provide a learning plan tailored to a specific area where the user has weaknesses. Furthermore, if a user excels in a particular area, it can provide a learning plan that deepens their understanding of that area. This allows for the provision of an optimal learning plan based on the user's learning history, thereby enhancing the effectiveness of their learning.

[0073] PlayEnglish Agent can customize learning content according to the user's learning progress. For example, if a user clears a particular stage, it can provide a learning plan to review the content of that stage. If a user has weaknesses in a specific area, it can provide a learning plan tailored to that area. Furthermore, if a user has areas of expertise, it can provide a learning plan to deepen their understanding of those areas. This allows for the provision of optimal learning content tailored to the user's learning progress, thereby enhancing the effectiveness of learning.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The level check unit performs an initial level check. For example, when a user uses the app for the first time, a simple test is administered to evaluate the user's English level. The level check unit determines the appropriate level based on the user's answers. For example, it calculates the accuracy rate of the questions the user answers and determines the level based on the results. It can also adjust the level considering the user's response time. Step 2: The learning plan provision unit provides the user with the optimal learning plan based on the information obtained by the level check unit. For example, it automatically generates a curriculum according to the user's level and adjusts the curriculum according to the user's learning progress. When the user clears a specific stage, the content of the next stage is automatically updated. Step 3: The feedback unit provides feedback to the user after each stage is completed. For example, it evaluates the user's accuracy rate and response time based on their answers and provides feedback. It also provides appropriate advice according to the user's learning progress. For example, if the user is struggling with a particular problem, it provides an explanation for that problem. Step 4: The progress management unit manages the user's progress based on the information obtained by the feedback unit. For example, it records the user's learning history and visualizes their progress. It adjusts the learning plan according to the user's learning pace. If the user continues learning, it automatically updates the content for the next stage.

[0076] (Example of form 2) The Play English Agent according to an embodiment of the present invention is an AI-driven application that allows users to learn English in a game-like manner. First, the user undergoes an initial level check, and the AI ​​provides the user with an optimal learning plan. The learning plan is customized according to the user's learning progress, aiming to reduce the time spent on language learning and improve efficiency. After clearing each stage, the AI ​​provides real-time feedback and manages progress. This maximizes the user's learning efficiency and enables improved proficiency through continuous learning support. The AI ​​agent provides learning materials appropriate to the user's level and provides real-time progress feedback. Furthermore, by integrating gamification elements, it maintains learning motivation and deepens understanding through gradual learning progress. The AI ​​learns the user's responses, automatically generates individualized learning plans, and dynamically adjusts learning materials based on progress. This application targets students and young professionals in their teens to thirties who are interested in learning English, addressing the challenges of difficulty in continuing English learning and the monotonous and easily boring nature of learning methods. By incorporating game elements, it promotes continued learning, and the AI ​​analyzes the user's learning style to optimize it individually. The generative AI uses natural language processing AI to generate individualized learning plans and leverages adaptive AI that learns from user responses. This reduces access barriers to English education and provides an environment where more people can learn in an enjoyable way. As a result, PlayEnglish Agent can maximize user learning efficiency and improve proficiency through continuous learning support.

[0077] The Play English agent according to this embodiment comprises a level check unit, a learning plan provision unit, a feedback unit, and a progress management unit. The level check unit performs an initial level check. For example, when a user uses the app for the first time, the level check unit conducts a simple test to evaluate the user's English level. The level check unit can determine an appropriate level based on the user's answers. For example, the level check unit calculates the accuracy rate of the questions answered by the user and determines the level based on the result. The level check unit can also adjust the level considering the user's response time. For example, if the user answers accurately in a short amount of time, the level can be set higher. The learning plan provision unit provides the user with an optimal learning plan based on the information obtained by the level check unit. For example, the learning plan provision unit automatically generates a curriculum according to the user's level. The learning plan provision unit can adjust the curriculum according to the user's learning progress. For example, when a user clears a particular stage, the content of the next stage is automatically updated. The feedback unit provides feedback to the user each time a stage is cleared. The feedback unit evaluates the accuracy rate and response time based on the user's answers and provides feedback. The feedback unit can provide appropriate advice according to the user's learning progress. For example, if the user is struggling with a particular problem, it provides an explanation for that problem. The progress management unit manages the user's progress based on the information obtained by the feedback unit. The progress management unit records the user's learning history and visualizes the progress status. The progress management unit can adjust the learning plan according to the user's learning pace. For example, if the user continues learning, the progress management unit automatically updates the content of the next stage. As a result, the Play English agent according to the embodiment can maximize the user's learning efficiency and achieve improved proficiency through continuous learning support.

[0078] The Level Check Unit conducts an initial level check. For example, when a user first uses the app, the Level Check Unit administers a simple test to assess the user's English level. Specifically, the Level Check Unit presents multiple questions covering various skills such as grammar, vocabulary, listening, and reading, and collects the user's responses. These questions are designed to comprehensively evaluate the user's English ability, and the difficulty levels range widely from beginner to advanced. Based on the user's responses, the Level Check Unit can determine an appropriate level. For example, the Level Check Unit calculates the accuracy rate of the questions the user answered and determines the level based on the results. The Level Check Unit can also adjust the level considering the user's response time. For example, if a user answers accurately in a short amount of time, the level can be set higher. Furthermore, the Level Check Unit can analyze the user's response patterns and error tendencies to identify weaknesses in specific skills. This allows the Level Check Unit to accurately assess the user's English ability and provide foundational information for offering learning plans tailored to individual needs.

[0079] The Learning Plan Provisioning Unit provides users with the most suitable learning plan based on information obtained by the Level Checking Unit. For example, the Learning Plan Provisioning Unit automatically generates a curriculum tailored to the user's level. Specifically, it creates a curriculum that balances learning content for each skill, such as grammar, vocabulary, listening, reading, and speaking, in order to improve the user's English ability. The Learning Plan Provisioning Unit can adjust the curriculum according to the user's learning progress. For example, if a user clears a certain stage, it automatically updates the content for the next stage. The Learning Plan Provisioning Unit can also provide learning content that focuses on specific skills to reinforce the user's weaknesses. For example, if a user has difficulty with listening, it will provide a curriculum that includes many listening practice exercises. Furthermore, the Learning Plan Provisioning Unit can adjust the difficulty level and pace of the learning content based on the user's learning history and feedback. This allows the Learning Plan Provisioning Unit to maximize the user's learning efficiency and support continuous learning.

[0080] The feedback unit provides feedback to the user after each stage is completed. For example, the feedback unit evaluates the user's accuracy rate and response time based on their answers and provides feedback accordingly. Specifically, the feedback unit calculates the accuracy rate of the questions the user answered and shows in detail which questions were answered correctly and which were answered incorrectly. It also evaluates the user's response time to check whether they were able to answer quickly. The feedback unit can provide appropriate advice according to the user's learning progress. For example, if a user is struggling with a particular question, it will provide an explanation for that question. The explanation will include not only the correct answer to the question, but also a detailed explanation of why that answer is correct and how to think about it. Furthermore, the feedback unit can evaluate the user's learning attitude and effort and provide positive feedback to boost motivation. For example, if a user achieves a certain goal, it will send words of praise and encouragement. In this way, the feedback unit can maintain the user's motivation to learn and support effective learning.

[0081] The progress management unit manages user progress based on information obtained from the feedback unit. For example, the progress management unit records the user's learning history and visualizes their progress. Specifically, the progress management unit meticulously records which stages the user has completed, how much time they spent on each problem, and the degree of progress they have made in each skill. This information is displayed in visual formats such as graphs and charts, allowing users to grasp their learning status at a glance. The progress management unit can adjust the learning plan according to the user's learning pace. For example, if the user is continuing their learning, the progress management unit automatically updates the content of the next stage. Also, if the user is falling behind in their learning, the progress management unit reviews the learning plan and adjusts it so that the user can continue learning without difficulty. Furthermore, the progress management unit can set learning goals for the user and monitor their achievement. This allows the progress management unit to maximize the user's learning efficiency and achieve improved proficiency through continuous learning support.

[0082] The PlayEnglish agent includes a material provision unit that provides learning materials appropriate to the user's level. The material provision unit can, for example, automatically select and provide learning materials appropriate to the user's level. The material provision unit can adjust the content of the learning materials according to the user's learning progress. For example, if the user clears a particular stage, it can automatically update the materials for the next stage. The material provision unit can also select the format of the learning materials according to the user's learning style. For example, if the user prefers visual learning, it can provide visual learning materials. This enhances the effectiveness of learning by providing learning materials appropriate to the user's level. Some or all of the above processes in the material provision unit may be performed using AI, for example, or without AI. For example, the material provision unit can have a generating AI perform the selection of learning materials appropriate to the user's level.

[0083] The PlayEnglish Agent includes a real-time feedback unit that provides real-time progress feedback. The real-time feedback unit provides feedback in real time as the user progresses through their learning. The real-time feedback unit can provide immediate feedback based on the user's answers. For example, it can provide feedback on the accuracy rate and answer time immediately after the user solves a problem. The real-time feedback unit can also provide appropriate advice according to the user's learning progress. For example, if the user is struggling with a particular problem, it can provide an explanation of that problem in real time. This helps maintain learning motivation by providing real-time progress feedback. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's answers into a generating AI and have the generating AI generate the feedback.

[0084] The PlayEnglish agent includes a gamification unit that integrates gamification elements. The gamification unit, for example, incorporates game elements as the user progresses through their learning. The gamification unit can adjust the game elements according to the user's learning progress. For example, if the user clears a certain stage, a new game element can be added in the next stage. The gamification unit can also select the content of the game elements according to the user's learning style. For example, if the user is learning with a competitive spirit, a ranking function can be provided. In this way, incorporating game elements can increase motivation for learning. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can have a generating AI perform the selection of game elements according to the user's learning progress.

[0085] The PlayEnglish agent includes a response learning unit that learns user responses. The response learning unit collects and learns user responses as the user progresses through the learning process. Based on user responses, the response learning unit can adjust the learning plan. For example, if a user shows a positive response to a particular problem, it will reinforce content related to that problem. The response learning unit can also select the content of the learning plan according to the user's learning style. For example, if a user prefers visual learning, it can provide visual learning materials. By learning user responses, it is possible to provide an individually optimized learning plan. Some or all of the above processing in the response learning unit may be performed using AI, for example, or without AI. For example, the response learning unit can input user response data into a generating AI and have the generating AI adjust the learning plan.

[0086] The PlayEnglish Agent includes an automatic generation unit that automatically generates individualized learning plans. The automatic generation unit automatically generates individualized learning plans according to the user's learning progress, for example. The automatic generation unit can adjust the content of the learning plan based on the user's learning history. For example, if the user clears a particular stage, it automatically updates the learning plan for the next stage. The automatic generation unit can also select the format of the learning plan according to the user's learning style. For example, if the user prefers visual learning, it can provide a learning plan that includes visual materials. By automatically generating individualized learning plans, the system can provide the user with the most suitable learning plan. Some or all of the above-described processes in the automatic generation unit may be performed using AI, for example, or without AI. For example, the automatic generation unit can input the user's learning history into a generation AI and have the generation AI perform the generation of the learning plan.

[0087] The PlayEnglish agent includes a dynamic adjustment unit that dynamically adjusts learning materials based on progress. The dynamic adjustment unit dynamically adjusts the content of learning materials according to the user's learning progress. The dynamic adjustment unit can adjust the content of learning materials based on the user's learning history. For example, if a user clears a particular stage, it automatically updates the learning materials for the next stage. The dynamic adjustment unit can also select the format of the learning materials according to the user's learning style. For example, if a user prefers visual learning, it can provide visual learning materials. This maximizes the effectiveness of learning by dynamically adjusting learning materials based on progress. Some or all of the above-described processes in the dynamic adjustment unit may be performed using AI, for example, or without AI. For example, the dynamic adjustment unit can input the user's learning progress data into a generating AI and have the generating AI perform the adjustment of learning materials.

[0088] The level check unit can estimate the user's emotions and adjust the difficulty of the level check based on the estimated emotions. For example, if the user is stressed, the level check unit can lower the difficulty of the level check and present easy questions. If the user is relaxed, the level check unit can also raise the difficulty of the level check and present challenging questions. If the user is focused, the level check unit can also appropriately adjust the difficulty of the level check and present appropriate questions. In this way, by adjusting the difficulty of the level check according to the user's emotions, it is possible to provide questions of appropriate difficulty. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the level check unit may be performed using AI, for example, or without using AI. For example, the level check unit can input user emotion data into the generative AI and have the generative AI perform the level check difficulty adjustment.

[0089] The level check unit can select the optimal checking method by referring to the user's past learning history during the level check. For example, the level check unit can prioritize presenting questions that the user has struggled with in the past. The level check unit can also present questions in a balanced manner that include questions in areas where the user excels. The level check unit can also select the optimal checking method (multiple choice, written response, etc.) from the user's past learning history. In this way, the optimal checking method can be selected by referring to the user's past learning history. Some or all of the above processing in the level check unit may be performed using AI, for example, or without using AI. For example, the level check unit can input the user's past learning history data into a generating AI and have the generating AI perform the selection of the optimal checking method.

[0090] The level check unit can provide feedback based on the user's current learning environment during a level check. For example, if the user is in a quiet environment, the level check unit can provide detailed feedback. If the user is in a noisy environment, the level check unit can also provide concise feedback. If the user is on the move, the level check unit can also provide voice feedback. This allows for appropriate feedback to be provided based on the user's current learning environment. Some or all of the above processing in the level check unit may be performed using AI, for example, or without AI. For example, the level check unit can input the user's current learning environment data into a generating AI and have the generating AI generate the feedback.

[0091] The level check unit can estimate the user's emotions and adjust the timing of the level check based on the estimated emotions. For example, if the user is tired, the level check unit may delay the timing of the level check. If the user is focused, the level check unit may also advance the timing of the level check. If the user is relaxed, the level check unit may also perform the level check at an appropriate time. In this way, by adjusting the timing of the level check according to the user's emotions, the level check can be performed at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the level check unit may be performed using AI, for example, or without using AI. For example, the level check unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the level check timing.

[0092] The level check unit can prioritize presenting highly relevant check items by considering the user's geographical location during the level check. For example, if the user is in an English-speaking country, the level check unit may present check items related to everyday conversation. If the user is in a non-English-speaking country, the level check unit may also present check items related to business English. If the user is traveling, the level check unit may also present check items related to travel English. In this way, by considering the user's geographical location, it is possible to provide highly relevant check items. Some or all of the above processing in the level check unit may be performed using AI, for example, or without AI. For example, the level check unit can input the user's geographical location data into a generating AI and have the generating AI select the check items.

[0093] The level check unit can analyze the user's social media activity during the level check and generate relevant check items. For example, the level check unit can generate check items related to words the user frequently uses on social media. The level check unit can also generate check items related to topics the user is interested in. The level check unit can also generate check items related to the content of accounts the user follows. In this way, relevant check items can be provided by analyzing the user's social media activity. Some or all of the above processing in the level check unit may be performed using AI, for example, or without AI. For example, the level check unit can input the user's social media activity data into a generating AI and have the generating AI select the check items.

[0094] The learning plan provider can estimate the user's emotions and adjust the content of the learning plan based on the estimated emotions. For example, if the user is feeling stressed, the learning plan provider can provide a learning plan with relaxing content. If the user is relaxed, the learning plan provider can also provide a learning plan with challenging content. If the user is focused, the learning plan provider can also provide a learning plan with content that allows for efficient learning. In this way, by adjusting the content of the learning plan according to the user's emotions, an appropriate learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or without AI. For example, the learning plan provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the learning plan content.

[0095] The learning plan provider can select the optimal plan by referring to the user's past learning history when providing a learning plan. For example, the learning plan provider can provide a plan that focuses on areas the user has struggled with in the past. The learning plan provider can also provide a plan that balances learning across areas the user excels at. The learning plan provider can also select the optimal learning method (video, text, etc.) from the user's past learning history. In this way, the learning plan provider can provide the optimal learning plan by referring to the user's past learning history. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or without using AI. For example, the learning plan provider can input the user's past learning history data into a generating AI and have the generating AI select the optimal plan.

[0096] The learning plan provider can customize the plan based on the user's current learning environment when providing the plan. For example, if the user is in a quiet environment, the learning plan provider can provide a plan that allows for focused learning. If the user is in a noisy environment, the learning plan provider can also provide a plan that allows for short-term learning. If the user is on the move, the learning plan provider can also provide an audio learning plan. In this way, by customizing the plan based on the user's current learning environment, an appropriate learning plan can be provided. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or without AI. For example, the learning plan provider can input the user's current learning environment data into a generating AI and have the generating AI perform the plan customization.

[0097] The learning plan provider can estimate the user's emotions and determine the priority of the learning plan based on the estimated emotions. For example, if the user is feeling stressed, the learning plan provider may prioritize relaxing content. If the user is relaxed, the learning plan provider may also prioritize challenging content. If the user is focused, the learning plan provider may also prioritize content that allows for efficient learning. In this way, by determining the priority of the learning plan according to the user's emotions, an appropriate learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or not using AI. For example, the learning plan provider can input user emotion data into the generative AI and have the generative AI perform the determination of learning plan priorities.

[0098] The learning plan provider can prioritize providing highly relevant plans by considering the user's geographical location when providing learning plans. For example, if the user is in an English-speaking country, the learning plan provider can provide plans related to everyday conversation. If the user is in a non-English-speaking country, the learning plan provider can also provide plans related to business English. If the user is traveling, the learning plan provider can also provide plans related to travel English. In this way, by considering the user's geographical location, it is possible to provide highly relevant learning plans. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or not using AI. For example, the learning plan provider can input the user's geographical location data into a generating AI and have the generating AI select a plan.

[0099] The learning plan provider can analyze the user's social media activity and provide relevant plans when providing learning plans. For example, the learning plan provider can provide plans related to words the user frequently uses on social media. The learning plan provider can also provide plans related to topics the user is interested in. The learning plan provider can also provide plans related to the content of accounts the user follows. In this way, relevant learning plans can be provided by analyzing the user's social media activity. Some or all of the above processing in the learning plan provider may be performed using AI, for example, or not using AI. For example, the learning plan provider can input the user's social media activity data into a generating AI and have the generating AI select a plan.

[0100] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will provide positive feedback. If the user is relaxed, the feedback unit may also provide detailed feedback. If the user is focused, the feedback unit may also provide efficient feedback. In this way, appropriate feedback can be provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the content of the feedback.

[0101] The feedback unit can select the most appropriate feedback by referring to the user's past learning history when providing feedback. For example, the feedback unit can provide feedback on problems the user has struggled with in the past. The feedback unit can also provide a balanced set of feedback on areas the user excels in. The feedback unit can also select the most appropriate feedback method (text, audio, etc.) from the user's past learning history. This allows the feedback unit to provide optimal feedback by referring to the user's past learning history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past learning history data into a generating AI and have the generating AI select the feedback.

[0102] The feedback unit can customize the feedback based on the user's current learning environment when providing it. For example, if the user is in a quiet environment, the feedback unit can provide detailed feedback. If the user is in a noisy environment, the feedback unit can also provide concise feedback. If the user is on the move, the feedback unit can also provide voice feedback. This allows for the provision of appropriate feedback by customizing it based on the user's current learning environment. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's current learning environment data into a generating AI and have the generating AI perform the customization of the feedback.

[0103] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is tired, the feedback unit may delay the timing of feedback. If the user is focused, the feedback unit may also advance the timing of feedback. If the user is relaxed, the feedback unit may also provide feedback at an appropriate time. In this way, by adjusting the timing of feedback according to the user's emotions, feedback can be provided at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the timing of feedback.

[0104] The feedback unit can prioritize providing highly relevant feedback by considering the user's geographical location when providing feedback. For example, if the user is in an English-speaking country, the feedback unit can provide feedback related to everyday conversation. If the user is in a non-English-speaking country, the feedback unit can also provide feedback related to business English. If the user is traveling, the feedback unit can also provide feedback related to travel English. This allows the system to provide highly relevant feedback by considering the user's geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location data into a generating AI and have the generating AI select the feedback.

[0105] The feedback unit can analyze the user's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide feedback related to words the user frequently uses on social media. The feedback unit can also provide feedback related to topics the user is interested in. The feedback unit can also provide feedback related to the content of accounts the user follows. In this way, relevant feedback can be provided by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI select the feedback.

[0106] The progress management unit can estimate the user's emotions and adjust the progress management method based on the estimated user emotions. For example, if the user is stressed, the progress management unit can reduce the frequency of progress management. If the user is relaxed, the progress management unit can also increase the frequency of progress management. If the user is focused, the progress management unit can also provide an efficient progress management method. In this way, appropriate progress management can be performed by adjusting the progress management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the progress management method.

[0107] The progress management unit can select the optimal management method by referring to the user's past learning history when managing progress. For example, the progress management unit can focus on managing progress in areas where the user has struggled in the past. The progress management unit can also manage progress in areas where the user excels in a balanced manner. The progress management unit can also select the optimal progress management method (graph, list, etc.) from the user's past learning history. In this way, the optimal progress management method can be provided by referring to the user's past learning history. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input the user's past learning history data into a generating AI and have the generating AI select the management method.

[0108] The progress management unit can customize the management method based on the user's current learning environment when managing progress. For example, if the user is in a quiet environment, the progress management unit can provide detailed progress management. If the user is in a noisy environment, the progress management unit can also provide concise progress management. If the user is on the move, the progress management unit can also provide voice-based progress management. This allows for appropriate progress management by customizing the management method based on the user's current learning environment. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the user's current learning environment data into a generating AI and have the generating AI perform the customization of the management method.

[0109] The progress management unit can estimate the user's emotions and determine the priority of progress management based on the estimated emotions. For example, if the user is stressed, the progress management unit will prioritize relaxing content. If the user is relaxed, the progress management unit may also prioritize challenging content. If the user is focused, the progress management unit may also prioritize content that allows for efficient learning. This allows for appropriate progress management by determining the priority of progress management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input user emotion data into a generative AI and have the generative AI perform the determination of progress management priorities.

[0110] The progress management unit can select the optimal management method when managing progress, taking into account the user's geographical location information. For example, if the user is in an English-speaking country, the progress management unit can provide progress management related to everyday conversation. If the user is in a non-English-speaking country, the progress management unit can also provide progress management related to business English. If the user is traveling, the progress management unit can also provide progress management related to travel English. In this way, the optimal progress management method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can input the user's geographical location information data into a generating AI and have the generating AI select the management method.

[0111] The progress management unit can analyze the user's social media activity and provide relevant management methods during progress management. For example, the progress management unit can provide progress management related to words that the user frequently uses on social media. The progress management unit can also provide progress management related to topics that the user is interested in. The progress management unit can also provide progress management related to the content of accounts that the user follows. In this way, by analyzing the user's social media activity, it is possible to provide relevant progress management methods. Some or all of the above processing in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input the user's social media activity data into a generating AI and have the generating AI select a management method.

[0112] The learning material provider can estimate the user's emotions and adjust the content of the learning materials based on the estimated emotions. For example, if the user is feeling stressed, the learning material provider can provide relaxing learning materials. If the user is relaxed, the learning material provider can also provide challenging learning materials. If the user is focused, the learning material provider can also provide learning materials that allow for efficient learning. In this way, appropriate learning materials can be provided by adjusting the content of the learning materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the learning material provider may be performed using AI, for example, or without using AI. For example, the learning material provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the learning material content.

[0113] The learning material provider can select the most suitable learning materials by referring to the user's past learning history when providing materials. For example, the learning material provider can focus on providing materials in areas where the user has previously struggled. The learning material provider can also provide a balanced selection of materials in areas where the user excels. The learning material provider can also select the most suitable learning material format (video, text, etc.) based on the user's past learning history. This allows the system to provide the most suitable learning materials by referring to the user's past learning history. Some or all of the above processes in the learning material provider may be performed using AI, for example, or without AI. For example, the learning material provider can input the user's past learning history data into a generating AI and have the generating AI select the learning materials.

[0114] The learning material provider can estimate the user's emotions and prioritize learning materials based on those emotions. For example, if the user is stressed, the learning material provider will prioritize relaxing content. If the user is relaxed, the learning material provider may also prioritize challenging content. If the user is focused, the learning material provider may also prioritize content that allows for efficient learning. This allows for the provision of appropriate learning materials by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning material provider may be performed using AI or not. For example, the learning material provider can input user emotion data into a generative AI and have the generative AI perform the task of prioritizing learning materials.

[0115] The material provision unit can prioritize providing highly relevant materials by considering the user's geographical location when providing materials. For example, if the user is in an English-speaking country, the material provision unit can provide materials related to everyday conversation. If the user is in a non-English-speaking country, the material provision unit can also provide materials related to business English. If the user is traveling, the material provision unit can also provide materials related to travel English. In this way, highly relevant materials can be provided by considering the user's geographical location. Some or all of the above processing in the material provision unit may be performed using AI, for example, or without AI. For example, the material provision unit can input the user's geographical location data into a generating AI and have the generating AI select the materials.

[0116] The real-time feedback unit can estimate the user's emotions and adjust the content of the real-time feedback based on the estimated emotions. For example, if the user is stressed, the real-time feedback unit can provide positive feedback. If the user is relaxed, the real-time feedback unit can also provide detailed feedback. If the user is focused, the real-time feedback unit can also provide efficient feedback. In this way, appropriate feedback can be provided by adjusting the content of the real-time feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the feedback content.

[0117] The real-time feedback unit can select the most appropriate feedback by referring to the user's past learning history when providing real-time feedback. For example, the real-time feedback unit can provide feedback on problems the user has struggled with in the past. The real-time feedback unit can also provide a balanced set of feedback on areas the user excels in. The real-time feedback unit can also select the most appropriate feedback method (text, audio, etc.) from the user's past learning history. This allows the unit to provide optimal feedback by referring to the user's past learning history. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's past learning history data into a generating AI and have the generating AI select the feedback.

[0118] The real-time feedback unit can estimate the user's emotions and adjust the timing of real-time feedback based on the estimated emotions. For example, if the user is tired, the real-time feedback unit may delay the timing of feedback. If the user is focused, the real-time feedback unit may also speed up the timing of feedback. If the user is relaxed, the real-time feedback unit may also provide feedback at an appropriate time. In this way, by adjusting the timing of real-time feedback according to the user's emotions, feedback can be provided at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or not using AI. For example, the real-time feedback unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the feedback timing.

[0119] The real-time feedback unit can prioritize providing highly relevant feedback by considering the user's geographical location when providing real-time feedback. For example, if the user is in an English-speaking country, the real-time feedback unit can provide feedback related to everyday conversation. If the user is in a non-English-speaking country, the real-time feedback unit can also provide feedback related to business English. If the user is traveling, the real-time feedback unit can also provide feedback related to travel English. This allows for the provision of highly relevant feedback by considering the user's geographical location. Some or all of the above processing in the real-time feedback unit may be performed using AI, for example, or without AI. For example, the real-time feedback unit can input the user's geographical location data into a generating AI and have the generating AI select the feedback.

[0120] The gamification unit can estimate the user's emotions and adjust the content of game elements based on the estimated user emotions. For example, if the user is stressed, the gamification unit can provide relaxing game elements. If the user is relaxed, the gamification unit can also provide challenging game elements. If the user is focused, the gamification unit can also provide game elements that allow for efficient learning. In this way, appropriate game elements can be provided by adjusting the content of game elements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of game element content.

[0121] The gamification unit can select the most suitable game elements by referring to the user's past learning history when providing game elements. For example, the gamification unit can focus on providing game elements in areas where the user has previously struggled. The gamification unit can also provide a balanced selection of game elements in areas where the user excels. The gamification unit can also select the most suitable game elements (puzzles, quizzes, etc.) from the user's past learning history. In this way, the gamification unit can provide the most suitable game elements by referring to the user's past learning history. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's past learning history data into a generating AI and have the generating AI perform the selection of game elements.

[0122] The gamification unit can estimate the user's emotions and determine the priority of game elements based on the estimated emotions. For example, if the user is stressed, the gamification unit will prioritize relaxing content. If the user is relaxed, the gamification unit may also prioritize challenging content. If the user is focused, the gamification unit may also prioritize content that allows for efficient learning. In this way, by determining the priority of game elements according to the user's emotions, appropriate game elements can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the gamification unit may be performed using AI, for example, or not using AI. For example, the gamification unit can input user emotion data into a generative AI and have the generative AI perform the determination of game element priorities.

[0123] The gamification unit can prioritize providing highly relevant game elements by considering the user's geographical location when providing game elements. For example, if the user is in an English-speaking country, the gamification unit can provide game elements related to everyday conversation. If the user is in a non-English-speaking country, the gamification unit can also provide game elements related to business English. If the user is traveling, the gamification unit can also provide game elements related to travel English. In this way, highly relevant game elements can be provided by considering the user's geographical location. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's geographical location data into a generating AI and have the generating AI select game elements.

[0124] The response learning unit can estimate the user's emotions and adjust the content of the response learning based on the estimated user emotions. For example, if the user is feeling stressed, the response learning unit can provide response learning content that promotes relaxation. If the user is relaxed, the response learning unit can also provide response learning content that is challenging. If the user is focused, the response learning unit can also provide response learning content that allows for efficient learning. In this way, appropriate response learning can be provided by adjusting the content of the response learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the response learning unit may be performed using AI, for example, or without using AI. For example, the response learning unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the response learning content.

[0125] The reaction learning unit can select the optimal learning method by referring to the user's past learning history during reaction learning. For example, the reaction learning unit can focus on providing reaction learning in areas where the user has previously struggled. The reaction learning unit can also provide a balanced selection of reaction learning in areas where the user excels. The reaction learning unit can also select the optimal reaction learning method (video, text, etc.) from the user's past learning history. In this way, the optimal reaction learning method can be provided by referring to the user's past learning history. Some or all of the above processing in the reaction learning unit may be performed using AI, for example, or without using AI. For example, the reaction learning unit can input the user's past learning history data into a generating AI and have the generating AI select the learning method.

[0126] The response learning unit can estimate the user's emotions and determine the priority of response learning based on the estimated user emotions. For example, if the user is stressed, the response learning unit will prioritize relaxing content. If the user is relaxed, the response learning unit may also prioritize challenging content. If the user is focused, the response learning unit may also prioritize content that allows for efficient learning. In this way, appropriate response learning can be provided by determining the priority of response learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response learning unit may be performed using AI, for example, or without AI. For example, the response learning unit can input user emotion data into the generative AI and have the generative AI perform the determination of response learning priorities.

[0127] The response learning unit can select the optimal learning method during response learning by considering the user's geographical location information. For example, if the user is in an English-speaking country, the response learning unit can provide response learning related to everyday conversation. If the user is in a non-English-speaking country, the response learning unit can also provide response learning related to business English. If the user is traveling, the response learning unit can also provide response learning related to travel English. In this way, the optimal response learning method can be provided by considering the user's geographical location information. Some or all of the above processing in the response learning unit may be performed using AI, for example, or without using AI. For example, the response learning unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the learning method.

[0128] The automatic generation unit can estimate the user's emotions and adjust the automatically generated content based on the estimated emotions. For example, if the user is stressed, the automatic generation unit can generate relaxing content. If the user is relaxed, the automatic generation unit can also generate challenging content. If the user is focused, the automatic generation unit can also generate content that allows for efficient learning. In this way, by adjusting the automatically generated content according to the user's emotions, appropriate automatic generation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the automatic generation unit may be performed using AI, for example, or without AI. For example, the automatic generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the automatically generated content.

[0129] The automatic generation unit can select the optimal generation method by referring to the user's past learning history during automatic generation. For example, the automatic generation unit can automatically generate content that focuses on areas the user has struggled with in the past. The automatic generation unit can also automatically generate content in a balanced manner that includes areas the user excels at. The automatic generation unit can also select the optimal generation method (video, text, etc.) from the user's past learning history. In this way, the system can provide the optimal generation method by referring to the user's past learning history. Some or all of the above-described processes in the automatic generation unit may be performed using AI, for example, or without AI. For example, the automatic generation unit can input the user's past learning history data into a generation AI and have the generation AI select the generation method.

[0130] The automatic generation unit can estimate the user's emotions and determine the priority of automatic generation based on the estimated user emotions. For example, if the user is stressed, the automatic generation unit will prioritize relaxing content. If the user is relaxed, the automatic generation unit may also prioritize challenging content. If the user is focused, the automatic generation unit may also prioritize content that allows for efficient learning. In this way, appropriate automatic generation can be provided by determining the priority of automatic generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the automatic generation unit may be performed using AI, for example, or without AI. For example, the automatic generation unit can input user emotion data into the generation AI and have the generation AI perform the determination of automatic generation priorities.

[0131] The automatic generation unit can select the optimal generation method by considering the user's geographical location information during automatic generation. For example, if the user is in an English-speaking country, the automatic generation unit can automatically generate content related to everyday conversation. If the user is in a non-English-speaking country, the automatic generation unit can also automatically generate content related to business English. If the user is traveling, the automatic generation unit can also automatically generate content related to travel English. In this way, the optimal generation method can be provided by considering the user's geographical location information. Some or all of the above processing in the automatic generation unit may be performed using AI, for example, or without using AI. For example, the automatic generation unit can input the user's geographical location information data into a generation AI and have the generation AI select the generation method.

[0132] The dynamic adjustment unit can estimate the user's emotions and adjust the content of the dynamic adjustments based on the estimated emotions. For example, if the user is feeling stressed, the dynamic adjustment unit can perform dynamic adjustments that promote relaxation. If the user is relaxed, the dynamic adjustment unit can also perform dynamic adjustments that are challenging. If the user is focused, the dynamic adjustment unit can also perform dynamic adjustments that facilitate efficient learning. In this way, by adjusting the content of the dynamic adjustments according to the user's emotions, appropriate dynamic adjustments can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the dynamic adjustment unit may be performed using AI, for example, or without using AI. For example, the dynamic adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the dynamic adjustment content.

[0133] The dynamic adjustment unit can select the optimal adjustment method by referring to the user's past learning history during dynamic adjustment. For example, the dynamic adjustment unit can dynamically adjust content that the user has struggled with in the past. The dynamic adjustment unit can also dynamically adjust content in a balanced manner that includes areas in which the user excels. The dynamic adjustment unit can also select the optimal adjustment method (video, text, etc.) from the user's past learning history. In this way, the optimal adjustment method can be provided by referring to the user's past learning history. Some or all of the above processing in the dynamic adjustment unit may be performed using AI, for example, or without using AI. For example, the dynamic adjustment unit can input the user's past learning history data into a generating AI and have the generating AI perform the selection of the adjustment method.

[0134] The dynamic adjustment unit can estimate the user's emotions and determine the priority of dynamic adjustments based on the estimated user emotions. For example, if the user is stressed, the dynamic adjustment unit will prioritize relaxing content. If the user is relaxed, the dynamic adjustment unit may also prioritize challenging content. If the user is focused, the dynamic adjustment unit may also prioritize content that allows for efficient learning. In this way, appropriate dynamic adjustments can be provided by determining the priority of dynamic adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dynamic adjustment unit may be performed using AI, for example, or without AI. For example, the dynamic adjustment unit can input user emotion data into the generative AI and have the generative AI perform the determination of dynamic adjustment priorities.

[0135] The dynamic adjustment unit can select the optimal adjustment method by considering the user's geographical location information during dynamic adjustment. For example, if the user is in an English-speaking country, the dynamic adjustment unit can dynamically adjust content related to everyday conversation. If the user is in a non-English-speaking country, the dynamic adjustment unit can also dynamically adjust content related to business English. If the user is traveling, the dynamic adjustment unit can also dynamically adjust content related to travel English. In this way, the optimal adjustment method can be provided by considering the user's geographical location information. Some or all of the above processing in the dynamic adjustment unit may be performed using AI, for example, or without using AI. For example, the dynamic adjustment unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the adjustment method.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] PlayEnglish Agent can select the format of the learning plan according to the user's learning style. For example, if the user prefers visual learning, a learning plan centered on visual materials can be provided. If the user prefers auditory learning, a learning plan centered on audio materials can be provided. Furthermore, if the user prefers practical learning, interactive materials can be provided. This allows for the provision of an optimal learning plan tailored to the user's learning style, thereby enhancing the effectiveness of learning.

[0138] PlayEnglish Agent can analyze a user's learning history and provide rewards based on their learning progress. For example, users can earn badges or points when they complete certain stages. It can also offer bonus points for continuous learning. Furthermore, it can provide special rewards when users achieve specific goals. This can increase user motivation and encourage continuous learning.

[0139] PlayEnglish Agent can gradually increase the difficulty of the learning content according to the user's learning progress. For example, once a user completes the beginner level, it can provide intermediate level learning content. Furthermore, once the user completes the intermediate level, it can provide advanced level learning content. And once the user completes the advanced level, it can provide specialized learning content. This allows for the provision of optimal learning content tailored to the user's progress, thereby enhancing the effectiveness of learning.

[0140] PlayEnglish Agent can analyze a user's learning history and adjust the learning plan according to their progress. For example, if a user is struggling at a particular stage, it can provide a learning plan that reinforces the content of that stage. It can also provide a learning plan tailored to a specific area where the user has weaknesses. Furthermore, if a user excels in a particular area, it can provide a learning plan that deepens their understanding of that area. This allows for the provision of an optimal learning plan based on the user's learning history, thereby enhancing the effectiveness of their learning.

[0141] PlayEnglish Agent can customize learning content according to the user's learning progress. For example, if a user clears a particular stage, it can provide a learning plan to review the content of that stage. If a user has weaknesses in a specific area, it can provide a learning plan tailored to that area. Furthermore, if a user has areas of expertise, it can provide a learning plan to deepen their understanding of those areas. This allows for the provision of optimal learning content tailored to the user's learning progress, thereby enhancing the effectiveness of learning.

[0142] The PlayEnglish agent can estimate the user's emotions and adjust the learning plan based on those emotions. For example, if the user is feeling stressed, it can provide a learning plan with relaxing content. If the user is relaxed, it can provide a learning plan with challenging content. Furthermore, if the user is focused, it can provide a learning plan with content that allows for efficient learning. In this way, by providing an optimal learning plan tailored to the user's emotions, the effectiveness of learning can be enhanced.

[0143] The PlayEnglish agent can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it can provide positive feedback. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is focused, it can provide efficient feedback. This allows for improved learning effectiveness by providing optimal feedback tailored to the user's emotions.

[0144] The PlayEnglish agent can estimate the user's emotions and adjust the learning timing based on those emotions. For example, if the user is tired, the learning timing can be delayed. Conversely, if the user is focused, the learning timing can be accelerated. Furthermore, if the user is relaxed, learning can be conducted at an appropriate time. This allows for improved learning effectiveness by providing optimal learning timing tailored to the user's emotions.

[0145] The PlayEnglish agent can estimate the user's emotions and adjust the learning content based on those emotions. For example, if the user is stressed, it can provide relaxing learning content. If the user is relaxed, it can provide challenging learning content. Furthermore, if the user is focused, it can provide learning content that allows for efficient learning. In this way, by providing optimal learning content tailored to the user's emotions, the effectiveness of learning can be enhanced.

[0146] The PlayEnglish agent can estimate the user's emotions and prioritize learning based on those emotions. For example, if the user is stressed, it can prioritize relaxing content. If the user is relaxed, it can prioritize challenging content. Furthermore, if the user is focused, it can prioritize content that allows for efficient learning. This provides optimal learning priorities tailored to the user's emotions, thereby enhancing learning effectiveness.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The level check unit performs an initial level check. For example, when a user uses the app for the first time, a simple test is administered to evaluate the user's English level. The level check unit determines the appropriate level based on the user's answers. For example, it calculates the accuracy rate of the questions the user answers and determines the level based on the results. It can also adjust the level considering the user's response time. Step 2: The learning plan provision unit provides the user with the optimal learning plan based on the information obtained by the level check unit. For example, it automatically generates a curriculum according to the user's level and adjusts the curriculum according to the user's learning progress. When the user clears a specific stage, the content of the next stage is automatically updated. Step 3: The feedback unit provides feedback to the user after each stage is completed. For example, it evaluates the user's accuracy rate and response time based on their answers and provides feedback. It also provides appropriate advice according to the user's learning progress. For example, if the user is struggling with a particular problem, it provides an explanation for that problem. Step 4: The progress management unit manages the user's progress based on the information obtained by the feedback unit. For example, it records the user's learning history and visualizes their progress. It adjusts the learning plan according to the user's learning pace. If the user continues learning, it automatically updates the content for the next stage.

[0149] 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.

[0150] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0151] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the level check unit, learning plan provision unit, feedback unit, progress management unit, teaching material provision unit, real-time feedback unit, gamification unit, reaction learning unit, automatic generation unit, and dynamic adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the level check unit is implemented by the control unit 46A of the smart device 14 and performs an initial level check of the user. The learning plan provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with an optimal learning plan. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides feedback each time a stage is cleared. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's progress. The teaching material provision unit is implemented by the control unit 46A of the smart device 14 and provides teaching materials according to the user's level. The real-time feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback in real time. The gamification unit is implemented by the control unit 46A of the smart device 14 and integrates game elements into learning. The reaction learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The automatic generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates individual learning plans. The dynamic adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and performs dynamic adjustment of learning materials based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0156] 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.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0158] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] 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.

[0160] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the level check unit, learning plan provision unit, feedback unit, progress management unit, teaching material provision unit, real-time feedback unit, gamification unit, reaction learning unit, automatic generation unit, and dynamic adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the level check unit is implemented by the control unit 46A of the smart glasses 214 and performs an initial level check of the user. The learning plan provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with an optimal learning plan. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback each time a stage is cleared. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's progress. The teaching material provision unit is implemented by the control unit 46A of the smart glasses 214 and provides teaching materials according to the user's level. The real-time feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback in real time. The gamification unit is implemented by the control unit 46A of the smart glasses 214 and integrates game elements into learning. The reaction learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The automatic generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates individual learning plans. The dynamic adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and performs dynamic adjustment of learning materials based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0172] 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.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0174] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] 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.

[0176] 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.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the level check unit, learning plan provision unit, feedback unit, progress management unit, teaching material provision unit, real-time feedback unit, gamification unit, reaction learning unit, automatic generation unit, and dynamic adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the level check unit is implemented by the control unit 46A of the headset terminal 314 and performs an initial level check of the user. The learning plan provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with an optimal learning plan. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback each time a stage is cleared. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's progress. The teaching material provision unit is implemented by the control unit 46A of the headset terminal 314 and provides teaching materials according to the user's level. The real-time feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback in real time. The gamification unit is implemented by the control unit 46A of the headset terminal 314 and integrates game elements into learning. The reaction learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The automatic generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates individual learning plans. The dynamic adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and performs dynamic adjustment of learning materials based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0188] 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.

[0189] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0190] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0191] 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.

[0192] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0193] 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.

[0194] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0195] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0196] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0197] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0198] 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.

[0199] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0201] Each of the multiple elements described above, including the level check unit, learning plan provision unit, feedback unit, progress management unit, teaching material provision unit, real-time feedback unit, gamification unit, reaction learning unit, automatic generation unit, and dynamic adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the level check unit is implemented by the control unit 46A of the robot 414 and performs an initial level check of the user. The learning plan provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with an optimal learning plan. The feedback unit is implemented by the control unit 46A of the robot 414 and provides feedback each time a stage is cleared. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's progress. The teaching material provision unit is implemented by the control unit 46A of the robot 414 and provides teaching materials according to the user's level. The real-time feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback in real time. The gamification unit is implemented by the control unit 46A of the robot 414 and integrates game elements into learning. The reaction learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The automatic generation unit is implemented by the control unit 46A of the robot 414 and automatically generates individual learning plans. The dynamic adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and performs dynamic adjustment of learning materials based on progress. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0202] 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.

[0203] Figure 9 shows the 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.

[0204] 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.

[0205] 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.

[0206] 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, and motorcycles, 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 based, for example, 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.

[0207] 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."

[0208] 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.

[0209] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0218] 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 other things 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.

[0219] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0220] (Note 1) The level check unit performs an initial level check, A learning plan provision unit provides the user with an optimal learning plan based on the information obtained by the level check unit, A feedback unit that provides feedback after clearing each stage, A progress management unit that performs progress management based on the information obtained by the aforementioned feedback unit, Equipped with A system characterized by the following features. (Note 2) The facility includes a curriculum sourcing department that provides teaching materials tailored to different skill levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a real-time feedback unit that provides real-time progress feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a gamification section that integrates gamification elements. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a response learning unit that learns user responses. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes an automatic generation unit that automatically generates individual learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) It is equipped with a dynamic adjustment unit that performs dynamic adjustment of teaching materials based on progress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The level check unit is, The system estimates the user's emotions and adjusts the difficulty level of the level check based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The level check unit is, During level checks, the system selects the optimal checking method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The level check unit is, During level checks, provide feedback based on the user's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The level check unit is, The system estimates the user's emotions and adjusts the timing of level checks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The level check unit is, During the level check, the system prioritizes presenting questions that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The level check unit is, During the level check, the system analyzes the user's social media activity and presents relevant check items. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning plan provision unit, It estimates the user's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning plan provision unit, When providing a learning plan, the system selects the most suitable plan by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning plan provision unit, When providing a learning plan, customize the plan based on the user's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning plan provision unit, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning plan provision unit, When providing learning plans, we prioritize providing plans that are highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning plan provision unit, When providing learning plans, we analyze the user's social media activity and provide relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, the system selects the most appropriate feedback by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, customize the feedback based on the user's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we prioritize providing highly relevant feedback by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned progress management unit, We estimate the user's emotions and adjust the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned progress management unit, When managing progress, the system selects the optimal management method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned progress management unit, When managing progress, customize the management method based on the user's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned progress management unit, Estimate user emotions and prioritize progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned progress management unit, When managing progress, select the optimal management method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned progress management unit, During progress management, we analyze users' social media activity and provide relevant management methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned teaching materials provision department, The system estimates the user's emotions and adjusts the content of the learning materials based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned teaching materials provision department, When providing learning materials, the system selects the most suitable materials by referring to the user's past learning history. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned teaching materials provision department, It estimates the user's emotions and determines the priority of learning materials based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned teaching materials provision department, When providing educational materials, we prioritize providing highly relevant materials by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The real-time feedback unit described above is: It estimates the user's emotions and adjusts the content of real-time feedback based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The real-time feedback unit described above is: When providing real-time feedback, the system selects the most appropriate feedback by referring to the user's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The real-time feedback unit described above is: It estimates the user's emotions and adjusts the timing of real-time feedback based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The real-time feedback unit described above is: When providing real-time feedback, the system prioritizes providing highly relevant feedback by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 40) The gamification unit is, The system estimates the user's emotions and adjusts the game elements based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The gamification unit is, When providing game elements, the system selects the most suitable game elements by referring to the user's past learning history. The system described in Appendix 4, characterized by the features described herein. (Note 42) The gamification unit is, The system estimates the user's emotions and prioritizes game elements based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 43) The gamification unit is, When providing game elements, the system prioritizes providing highly relevant game elements by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 44) The reaction learning unit is It estimates the user's emotions and adjusts the response learning content based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 45) The reaction learning unit is During reaction learning, the optimal learning method is selected by referring to the user's past learning history. The system described in Appendix 5, characterized by the features described herein. (Note 46) The reaction learning unit is It estimates the user's emotions and determines the priority of response learning based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 47) The reaction learning unit is During response learning, the optimal learning method is selected by considering the user's geographical location information. The system according to appended claim 5, characterized in that... (Appended claim 48) The automatic generation unit estimates the user's emotion and adjusts the content of automatic generation based on the estimated user's emotion The system according to appended claim 6, characterized in that... (Appended claim 49) The automatic generation unit selects an optimal generation method by referring to the user's past learning history during automatic generation The system according to appended claim 6, characterized in that... (Appended claim 50) The automatic generation unit estimates the user's emotion and determines the priority order of automatic generation based on the estimated user's emotion The system according to appended claim 6, characterized in that... (Appended claim 51) The automatic generation unit selects an optimal generation method by considering the user's geographical location information during automatic generation The system according to appended claim 6, characterized in that... (Appended claim 52) The dynamic adjustment unit estimates the user's emotion and adjusts the content of dynamic adjustment based on the estimated user's emotion The system according to appended claim 7, characterized in that... (Appended claim 53) The dynamic adjustment unit selects an optimal adjustment method by referring to the user's past learning history during dynamic adjustment The system according to appended claim 7, characterized in that... (Appended claim 54) The dynamic adjustment unit estimates the user's emotion and determines the priority order of dynamic adjustment based on the estimated user's emotion The system according to appended claim 7, characterized in that... (Appended claim 55) The dynamic adjustment unit selects an optimal adjustment method by considering the user's geographical location information during dynamic adjustment The system described in Appendix 7, characterized by the features described herein. [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The level check unit performs an initial level check, A learning plan provision unit provides the user with an optimal learning plan based on the information obtained by the level check unit, A feedback unit that provides feedback after clearing each stage, A progress management unit that performs progress management based on the information obtained by the aforementioned feedback unit, Equipped with A system characterized by the following features.

2. The facility includes a curriculum sourcing department that provides teaching materials tailored to different skill levels. The system according to feature 1.

3. It features a real-time feedback unit that provides real-time progress feedback. The system according to feature 1.

4. It includes a gamification section that integrates gamification elements. The system according to feature 1.

5. It includes a response learning unit that learns user responses. The system according to feature 1.

6. It includes an automatic generation unit that automatically generates individual learning plans. The system according to feature 1.

7. It is equipped with a dynamic adjustment unit that performs dynamic adjustment of teaching materials based on progress. The system according to feature 1.

8. The level check unit is, The system estimates the user's emotions and adjusts the difficulty level of the level check based on those emotions. The system according to feature 1.

9. The level check unit is, During level checks, the system selects the optimal checking method by referring to the user's past learning history. The system according to feature 1.

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