system

Abstract

The system according to this embodiment aims to provide individually customized learning plans based on the learner's progress and level of understanding. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a content unit, an assistant unit, and a reporting unit. The analysis unit analyzes the learner's progress and level of understanding. The provision unit provides an individually customized learning plan based on the data analyzed by the analysis unit. The content unit provides interactive content. The assistant unit provides an AI teaching assistant. The reporting unit visualizes the learning progress and provides a report.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, a learning plan customized individually based on the progress and understanding level of a learner has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a learning plan customized individually based on the progress and understanding level of a learner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a content unit, an assistant unit, and a reporting unit. The analysis unit analyzes the learner's progress and level of understanding. The provision unit provides individually customized learning plans based on the data analyzed by the analysis unit. The content unit provides interactive content. The assistant unit provides an AI teaching assistant. The reporting unit visualizes learning progress and provides reports. [Effects of the Invention]

[0007] The system according to this embodiment can provide individually customized learning plans based on the learner's progress and level of understanding. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 receiving 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 receiving 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) An online platform according to an embodiment of the present invention is a system that utilizes generative AI to provide customized educational content tailored to individual learning styles. This system targets elementary, junior high, and high school students, university students, and even working adults seeking reskilling. The online platform uses AI to analyze learners' progress and understanding, and provides individually customized learning plans. For example, if a student is struggling with a particular area of ​​mathematics, the AI ​​recommends supplementary materials and practice problems suitable for that student. It also recommends content according to learning style and interests. For example, it provides video content for students who prefer visual learning and simulations for students who prefer practical learning. Next, it provides interactive content. A variety of content formats, such as videos, quizzes, and simulations, are available to allow learners to learn at their own pace. Furthermore, it provides real-time feedback and advice, allowing learners to check their understanding as they progress. It also incorporates an AI teaching assistant. An AI chatbot is available 24 hours a day to answer learners' questions and support their learning. For example, if a student has a question about something in history class, they can get an immediate answer by asking the AI ​​chatbot. Furthermore, it visualizes learning progress and provides reports to parents and teachers. This allows for tracking learners' progress and providing support as needed. For example, parents can check their child's learning progress and provide necessary support. In this way, online platforms utilizing generative AI can provide customized educational content tailored to individual learning styles, improving learners' understanding. Furthermore, by utilizing interactive content and AI teaching assistants, they provide an environment where learners can learn at their own pace. In addition, they enhance learner support by visualizing learning progress and providing reports to parents and teachers. As a result, online platforms can provide customized educational content based on learners' progress and understanding, improving learners' comprehension.

[0029] The online platform according to this embodiment comprises an analysis unit, a provision unit, a content unit, an assistant unit, and a reporting unit. The analysis unit analyzes the learner's progress and level of understanding. For example, the analysis unit analyzes the learner's test results and learning history to evaluate the learner's level of understanding. The analysis unit can also analyze the learner's learning style and interests. For example, the analysis unit analyzes whether the learner prefers visual or auditory learning. The provision unit provides individually customized learning plans based on the data analyzed by the analysis unit. For example, the provision unit provides real-time feedback according to the learner's progress. The provision unit can also recommend content according to the learner's learning style and interests. For example, the provision unit provides video content to learners who prefer visual learning and simulations to learners who prefer practical learning. The content unit provides interactive content. For example, the content unit provides content in various formats such as videos, quizzes, and simulations, allowing learners to learn at their own pace. The content unit can also update the content according to the learner's progress. The Assistant section provides an AI teaching assistant. For example, the Assistant section provides a chatbot that is available 24 hours a day to answer learners' questions. The Assistant section can also provide advice according to the learner's progress. The Reporting section visualizes learning progress and provides reports to parents and teachers. For example, the Reporting section displays the learner's progress in graphs and charts so that parents and teachers can understand the learner's situation. The Reporting section can also evaluate the learner's level of understanding and suggest the next steps. As a result, the online platform according to the embodiment can provide customized educational content based on the learner's progress and level of understanding, thereby improving the learner's comprehension.

[0030] The analytics unit analyzes learners' progress and understanding. For example, it analyzes learners' test results and learning history to evaluate their comprehension. Specifically, the analytics unit analyzes in detail the learner's scores, response time, and the types of questions they answered incorrectly on past tests. This allows for a clear understanding of the learner's strengths and weaknesses. The analytics unit can also analyze learners' learning styles and interests. For example, it analyzes whether learners prefer visual or auditory learning. This involves analyzing what types of content learners frequently use and which formats of content they perform best with. Furthermore, the analytics unit can use AI to analyze learners' behavioral patterns and identify when learners can learn most effectively and in what environments their concentration is highest. This allows the analytics unit to collect data to provide the optimal learning environment for each individual learner and build a foundation for maximizing learning efficiency.

[0031] The service provider offers individually customized learning plans based on data analyzed by the analysis unit. For example, the service provider provides real-time feedback based on the learner's progress. Specifically, immediately after a test, the service provider provides detailed explanations of correct and incorrect answers, clearly indicating where mistakes were made and how to improve. The service provider can also recommend content tailored to the learner's learning style and interests. For instance, it provides video content for learners who prefer visual learning and simulations for those who prefer hands-on learning. Furthermore, the service provider dynamically adjusts the learning plan based on the learner's progress, ensuring they always have access to the most relevant learning content. For example, if a learner struggles with a particular area, the service provider provides supplementary materials and additional practice problems related to that area. The service provider also implements achievement goals and reward systems to maintain learner motivation and encourage them to strive towards their goals. This allows the service provider to provide an optimal learning experience for each individual learner, maximizing learning effectiveness.

[0032] The Content Department provides interactive content. For example, it offers a variety of content formats, such as videos, quizzes, and simulations, allowing learners to learn at their own pace. Specifically, the Content Department selects themes and topics that are likely to interest learners and creates content based on them. For instance, in science classes, it allows students to experience theories firsthand through experimental simulations, and in history classes, it provides videos recreating historical events. The Content Department can also update content according to learners' progress. For example, once a learner completes a particular unit, it provides more advanced content as the next step. Furthermore, the Content Department enhances learner engagement by incorporating interactive elements. For instance, quiz-style content allows learners to check their understanding as they progress, and simulation-style content allows learners to learn by actually operating the system. This enables the Content Department to provide a fun and effective learning environment, improving the quality of learning.

[0033] The Assistant Department provides an AI teaching assistant. For example, it offers a 24 / 7 chatbot to answer learners' questions. Specifically, the Assistant Department uses natural language processing technology to understand learners' questions and provide appropriate answers. For instance, if a learner asks a question about a math problem, the chatbot will explain the solution step-by-step. The Assistant Department can also provide advice based on the learner's progress. For example, if a learner is struggling with a particular area, it will recommend supplementary materials and practice problems related to that area. Furthermore, based on the learner's learning history, the Assistant Department can suggest what to learn next and effective learning methods. This allows the Assistant Department to support learners in continuing to learn at their own pace and improve learning efficiency. Additionally, the Assistant Department can collect learner feedback and continuously improve the accuracy of the chatbot's answers and the quality of its advice. This allows the Assistant Department to always provide learners with optimal support and enhance their learning experience.

[0034] The reporting department visualizes learning progress and provides reports to parents and teachers. For example, the reporting department displays learners' progress using graphs and charts, allowing parents and teachers to understand the learners' situation. Specifically, the reporting department details which units learners understand to what extent and which areas they struggle with. The reporting department can also evaluate learners' understanding and suggest the next steps. For example, if a learner completes a particular unit, it can suggest the next content to learn and supplementary materials. Furthermore, the reporting department can provide information for developing long-term learning plans based on learners' progress data. This allows parents and teachers to understand learners' learning status in real time and provide appropriate support. In addition, the reporting department can introduce achievement goals and reward systems to maintain learners' motivation and increase their willingness to work towards their goals. In this way, the reporting department can effectively manage learners' progress and improve the quality of learning.

[0035] The analysis unit can analyze learners' learning styles and interests. For example, it can analyze whether learners prefer visual or auditory learning. The analysis unit can also analyze survey results and past learning history to analyze learners' interests. This allows for the provision of more effective learning plans by performing analysis based on learners' learning styles and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input learners' survey results into AI and have the AI ​​perform the analysis of learning styles and interests.

[0036] The service provider can provide real-time feedback based on the learner's progress. For example, it can send immediate comments or notifications when a learner is working on a particular task. It can also suggest what the learner should learn next based on their progress. For instance, if a learner is struggling in a particular area, the service provider can recommend supplementary materials or practice problems related to that area. This real-time feedback tailored to the learner's progress can enhance learning effectiveness. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input learner progress data into an AI and have the AI ​​provide real-time feedback.

[0037] The content unit can provide content in various formats, such as videos, quizzes, and simulations. For example, if a learner prefers visual learning, the content unit can provide video content. Alternatively, if a learner prefers practical learning, the content unit can provide simulations. Furthermore, the content unit updates the content according to the learner's progress, allowing learners to learn at their own pace. This diverse content format can engage learners and enhance learning effectiveness. Some or all of the above processes in the content unit may be performed using AI, for example, or without AI. For instance, the content unit can input learner learning style data into AI, allowing the AI ​​to select the most suitable content.

[0038] The assistant unit can provide a chatbot that is available 24 hours a day. For example, the assistant unit can provide immediate answers when a learner asks a question. The assistant unit can also provide advice according to the learner's progress. For example, if a learner is struggling in a particular area, the assistant unit can provide supplementary information related to that area. This allows the 24 / 7 chatbot to provide an environment where learners can ask questions at any time. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input learner question data into AI and have the AI ​​generate the optimal answer.

[0039] The reporting department can provide reports to parents and teachers. For example, the reporting department can display learners' progress using graphs and charts, allowing parents and teachers to understand the learners' situation. The reporting department can also evaluate the learners' level of understanding and suggest the next steps. For example, if a learner is struggling in a particular area, the reporting department can suggest supplementary materials related to that area. By providing reports to parents and teachers, they can understand the learners' progress and provide appropriate support. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input learner progress data into AI and have the AI ​​generate an optimal report.

[0040] The analysis unit can analyze a learner's past learning history and select the optimal analysis method. For example, the analysis unit can focus on analyzing areas where the learner has struggled in the past to deepen their understanding. It can also analyze areas where the learner excels and provide more advanced learning content. Furthermore, the analysis unit can analyze the learner's past learning patterns and set the optimal analysis timing. This allows for improved learning effectiveness by selecting the optimal analysis method based on past learning history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's past learning data into AI and have the AI ​​select the optimal analysis method.

[0041] The analysis unit can improve the accuracy of its analysis based on the learner's current learning environment and areas of interest. For example, if the learner is learning in a quiet environment, the analysis unit will perform a more detailed analysis. The analysis unit can also prioritize the analysis of data related to the learner's areas of interest. Furthermore, the analysis unit can adjust the accuracy of its analysis according to the performance of the device the learner is using. This allows for more effective learning support by improving the accuracy of the analysis based on the current learning environment and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's current learning environment data into the AI ​​and have the AI ​​perform the improvement of the analysis accuracy.

[0042] The analysis unit can perform analysis while considering the learner's geographical location information, thereby reflecting region-specific learning trends. For example, the analysis unit can perform analysis based on the educational curriculum of the area where the learner lives. It can also incorporate popular learning resources in the learner's area into the analysis. Furthermore, the analysis unit can include data related to the culture and history specific to the learner's area in the analysis. This allows for the reflection of region-specific learning trends by considering geographical location information during the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's geographical location data into AI and have the AI ​​perform the analysis of region-specific learning trends.

[0043] The analysis unit can analyze learners' social media activities and acquire relevant learning data, which can then be incorporated into the analysis. For example, the analysis unit can incorporate learning resources shared by learners on social media into the analysis. The analysis unit can also include information on educational accounts that learners follow in the analysis. Furthermore, the analysis unit can incorporate topics that learners have shown interest in on social media into the analysis. In this way, by analyzing social media activities, relevant learning data can be acquired and incorporated into the analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input learners' social media data into an AI and have the AI ​​acquire and analyze relevant learning data.

[0044] The service provider can adjust the level of detail in the learning plan based on the learner's progress. For example, if a learner is behind schedule, the service provider can provide a detailed, step-by-step learning plan. If the learner is progressing well, the service provider can also provide an overview plan. Furthermore, if the learner is progressing quickly, the service provider can provide a learning plan that includes more advanced content. By adjusting the level of detail in the learning plan based on the learner's progress, more effective learning support becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input learner progress data into AI and have the AI ​​adjust the level of detail in the learning plan.

[0045] The service provider can apply different learning plan provision algorithms depending on the learner's category. For example, the service provider can provide a visual and interactive learning plan for elementary school students. It can also provide a learning plan for high school students that includes detailed explanations and practice problems. Furthermore, it can provide a learning plan for working adults that includes content directly related to their work. By applying different learning plan provision algorithms depending on the learner's category, more effective learning support becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input learner category data into AI and have the AI ​​apply the optimal learning plan provision algorithm.

[0046] The service provider can prioritize learning plans based on learners' submission timings. For example, it can prioritize assignments with approaching deadlines in the learning plan. It can also postpone assignments with longer deadlines and prioritize other important assignments. Furthermore, it can adjust the progress schedule of the learning plan according to the submission timing. This allows for more effective learning support by prioritizing learning plans based on submission timing. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input learner submission timing data into AI and have the AI ​​perform the task of prioritizing learning plans.

[0047] The service provider can adjust the order of the learning plan based on the learner's relevance. For example, the service provider can prioritize incorporating areas of interest to the learner into the learning plan. It can also prioritize highly relevant content based on the learner's past learning history. Furthermore, the service provider can provide the learning plan in the optimal order according to the learner's current learning situation. This allows for more effective learning support by adjusting the order of the learning plan based on relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input learner relevance data into AI and have the AI ​​adjust the order of the learning plan.

[0048] The content unit can select the most suitable content by referring to the learner's past learning history. For example, the content unit may prioritize displaying content related to areas the learner has struggled with in the past. It can also display advanced content related to areas the learner excels at. Furthermore, the content unit can select the most suitable content based on the learner's past learning patterns. This allows for more effective learning support by selecting the most suitable content based on past learning history. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For example, the content unit can input the learner's past learning data into AI and have the AI ​​select the most suitable content.

[0049] The content unit can customize the content format based on the learner's current learning environment. For example, if the learner is studying in a quiet environment, the content unit can provide content with detailed explanations. Alternatively, if the learner is studying while on the go, the content unit can provide content that can be completed in a short amount of time. Furthermore, the content unit can provide the optimal content format depending on the capabilities of the device the learner is using. This allows for more effective learning support by customizing the content format based on the current learning environment. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For instance, the content unit can input data on the learner's current learning environment into the AI ​​and have the AI ​​customize the content format.

[0050] The content unit can provide optimal content by taking into account the learner's geographical location. For example, the content unit can provide content based on the educational curriculum of the area where the learner lives. It can also reflect popular learning resources in the learner's area in its content. Furthermore, the content unit can provide content related to the culture and history specific to the learner's area. In this way, by providing optimal content that takes geographical location into account, it can address region-specific learning needs. Some or all of the above processing in the content unit may be performed using AI, for example, or not using AI. For example, the content unit can input the learner's geographical location data into AI and have the AI ​​perform the task of providing optimal content.

[0051] The content department can analyze learners' social media activity and provide relevant content. For example, the content department can incorporate learning resources shared by learners on social media into the content. It can also include information about educational accounts that learners follow in the content. Furthermore, the content department can incorporate topics that learners have shown interest in on social media into the content. This allows for the provision of relevant content and improved learning effectiveness by analyzing social media activity. Some or all of the above processing in the content department may be performed using AI, for example, or not. For example, the content department can input learners' social media data into an AI and have the AI ​​provide relevant content.

[0052] The assistant unit can provide the most appropriate answers by referring to the learner's past question history. For example, the assistant unit can provide relevant answers based on the content of questions the learner has asked in the past. The assistant unit can also predict common questions from the learner's past question history and prepare answers in advance. Furthermore, the assistant unit can provide more detailed explanations for questions that the learner did not understand in the past. This enhances learning effectiveness by providing the most appropriate answers based on past question history. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not. For example, the assistant unit can input the learner's past question data into an AI and have the AI ​​provide the most appropriate answers.

[0053] The assistant unit can customize its responses based on the learner's current learning status. For example, the assistant unit can provide information related to the task the learner is currently working on. It can also suggest what the learner should learn next, depending on their progress. Furthermore, the assistant unit can provide additional support in areas where the learner is finding it difficult. This allows for more effective learning support by customizing the assistant's responses based on the learner's current learning status. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not. For example, the assistant unit can input the learner's current learning status data into the AI ​​and have the AI ​​customize the responses.

[0054] The assistant unit can provide optimal responses by taking into account the learner's geographical location. For example, the assistant unit can provide responses based on the educational curriculum of the area where the learner lives. The assistant unit can also reflect popular learning resources in the learner's area in its responses. Furthermore, the assistant unit can provide information related to the culture and history specific to the learner's area. In this way, by providing optimal assistant responses that take geographical location into account, it can address region-specific learning needs. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input the learner's geographical location data into AI and have the AI ​​perform the task of providing optimal responses.

[0055] The assistant unit can analyze the learner's social media activity and provide relevant assistant responses. For example, the assistant unit can reflect learning resources shared by the learner on social media in its responses. It can also include information about educational accounts the learner follows in its responses. Furthermore, the assistant unit can provide information related to topics the learner has shown interest in on social media. This allows for the provision of relevant assistant responses by analyzing social media activity, thereby enhancing learning effectiveness. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not. For example, the assistant unit can input the learner's social media data into an AI and have the AI ​​provide relevant responses.

[0056] The reporting unit can provide optimal reports by referring to the learner's past learning history. For example, the reporting unit may prioritize displaying reports related to areas the learner has struggled with in the past. It can also display advanced reports related to areas the learner excels at. Furthermore, the reporting unit can provide optimal reports based on the learner's past learning patterns. This enhances learning effectiveness by providing optimal reports based on past learning history. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the learner's past learning data into AI and have the AI ​​perform the task of providing optimal reports.

[0057] The reporting function can customize the content of reports based on the learner's current learning status. For example, the reporting function can provide reports related to the assignments the learner is currently working on. It can also include in the report what the learner should learn next, depending on their progress. Furthermore, the reporting function can create reports that provide additional support for areas where the learner is finding difficult. This allows for more effective learning support by customizing the content of reports based on the current learning status. Some or all of the above processes in the reporting function may be performed using AI, for example, or not. For example, the reporting function can input the learner's current learning status data into an AI and have the AI ​​customize the content of the reports.

[0058] The reporting function can provide optimal reports by taking into account the learner's geographical location. For example, the reporting function can provide reports based on the educational curriculum of the area where the learner lives. It can also reflect popular learning resources in the learner's area in the report. Furthermore, the reporting function can provide reports related to the culture and history specific to the learner's area. In this way, by providing optimal reports that take geographical location into account, it can address region-specific learning needs. Some or all of the above processing in the reporting function may be performed using AI, for example, or not using AI. For example, the reporting function can input the learner's geographical location data into AI and have the AI ​​perform the task of providing optimal reports.

[0059] The reporting unit can analyze learners' social media activity and provide relevant reports. For example, the reporting unit can reflect learning resources shared by learners on social media in the report. It can also include information about educational accounts that learners follow in the report. Furthermore, the reporting unit can provide reports related to topics that learners have shown interest in on social media. In this way, analyzing social media activity can provide relevant reports and improve learning effectiveness. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input learners' social media data into an AI and have the AI ​​perform the task of providing relevant reports.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can acquire the learner's physiological data and estimate the optimal timing for learning. For example, it can use data such as heart rate and skin electrical activity to identify the time of day when the learner is most focused. The analysis unit can also analyze the learner's sleep patterns and suggest the optimal learning time. Furthermore, the analysis unit can consider the learner's diet and exercise data to estimate the timing that maximizes learning effectiveness. In this way, learning effectiveness can be enhanced by estimating the optimal timing for learning based on physiological data.

[0062] The content section can select the most suitable content by referring to the learner's past learning history. For example, it can prioritize displaying content related to areas the learner has struggled with in the past. It can also display advanced content related to areas the learner excels at. Furthermore, it can select the most suitable content based on the learner's past learning patterns. This allows for more effective learning support by selecting the most suitable content based on past learning history.

[0063] The analysis unit can perform analyses while considering the learner's geographical location, thereby reflecting region-specific learning trends. For example, it can perform analyses based on the educational curriculum of the area where the learner lives. It can also incorporate popular learning resources in the learner's area into the analysis. Furthermore, it can include data related to the culture and history specific to the learner's area in the analysis. In this way, by performing analyses while considering geographical location, it can reflect region-specific learning trends.

[0064] The learning department can prioritize learning plans based on the learner's submission schedule. For example, assignments with approaching deadlines can be prioritized in the learning plan. Alternatively, assignments with longer deadlines can be postponed, allowing other important assignments to take priority. Furthermore, the learning plan's progress schedule can be adjusted according to submission timing. This allows for more effective learning support by prioritizing learning plans based on submission timing.

[0065] The content section can customize the content format based on the learner's current learning environment. For example, if a learner is studying in a quiet environment, it can provide content with detailed explanations. Alternatively, if a learner is studying on the go, it can provide content that can be completed in a short amount of time. Furthermore, it can provide the optimal content format depending on the capabilities of the device the learner is using. This allows for more effective learning support by customizing the content format based on the current learning environment.

[0066] The assistant unit can analyze learners' social media activity and provide relevant assistant responses. For example, it can reflect learning resources shared by learners on social media in its responses. It can also include information about educational accounts that learners follow. Furthermore, it can provide information related to topics that learners have shown interest in on social media. In this way, by analyzing social media activity, relevant assistant responses can be provided, thereby enhancing learning effectiveness.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis unit analyzes the learner's progress and understanding. For example, it analyzes the learner's test results and learning history to evaluate their comprehension. It can also analyze the learner's learning style and interests. For example, it can analyze whether the learner prefers visual or auditory learning. Step 2: The provisioning unit provides individually customized learning plans based on the data analyzed by the analysis unit. For example, it provides real-time feedback according to the learner's progress and recommends content that matches their learning style and interests. It provides video content for learners who prefer visual learning and simulations for learners who prefer practical learning. Step 3: The content department provides interactive content. For example, it provides diverse content formats such as videos, quizzes, and simulations, allowing learners to learn at their own pace. It can also update the content according to the learners' progress. Step 4: The Assistant Department provides an AI teaching assistant. For example, it provides a chatbot that is available 24 / 7 to answer learners' questions. It can also provide advice based on the learner's progress. Step 5: The reporting department visualizes learning progress and provides reports to parents and teachers. For example, it displays learners' progress using graphs and charts so that parents and teachers can understand the learners' situation. It can also evaluate the learners' level of understanding and suggest the next steps.

[0069] (Example of form 2) An online platform according to an embodiment of the present invention is a system that utilizes generative AI to provide customized educational content tailored to individual learning styles. This system targets elementary, junior high, and high school students, university students, and even working adults seeking reskilling. The online platform uses AI to analyze learners' progress and understanding, and provides individually customized learning plans. For example, if a student is struggling with a particular area of ​​mathematics, the AI ​​recommends supplementary materials and practice problems suitable for that student. It also recommends content according to learning style and interests. For example, it provides video content for students who prefer visual learning and simulations for students who prefer practical learning. Next, it provides interactive content. A variety of content formats, such as videos, quizzes, and simulations, are available to allow learners to learn at their own pace. Furthermore, it provides real-time feedback and advice, allowing learners to check their understanding as they progress. It also incorporates an AI teaching assistant. An AI chatbot is available 24 hours a day to answer learners' questions and support their learning. For example, if a student has a question about something in history class, they can get an immediate answer by asking the AI ​​chatbot. Furthermore, it visualizes learning progress and provides reports to parents and teachers. This allows for tracking learners' progress and providing support as needed. For example, parents can check their child's learning progress and provide necessary support. In this way, online platforms utilizing generative AI can provide customized educational content tailored to individual learning styles, improving learners' understanding. Furthermore, by utilizing interactive content and AI teaching assistants, they provide an environment where learners can learn at their own pace. In addition, they enhance learner support by visualizing learning progress and providing reports to parents and teachers. As a result, online platforms can provide customized educational content based on learners' progress and understanding, improving learners' comprehension.

[0070] The online platform according to this embodiment comprises an analysis unit, a provision unit, a content unit, an assistant unit, and a reporting unit. The analysis unit analyzes the learner's progress and level of understanding. For example, the analysis unit analyzes the learner's test results and learning history to evaluate the learner's level of understanding. The analysis unit can also analyze the learner's learning style and interests. For example, the analysis unit analyzes whether the learner prefers visual or auditory learning. The provision unit provides individually customized learning plans based on the data analyzed by the analysis unit. For example, the provision unit provides real-time feedback according to the learner's progress. The provision unit can also recommend content according to the learner's learning style and interests. For example, the provision unit provides video content to learners who prefer visual learning and simulations to learners who prefer practical learning. The content unit provides interactive content. For example, the content unit provides content in various formats such as videos, quizzes, and simulations, allowing learners to learn at their own pace. The content unit can also update the content according to the learner's progress. The Assistant section provides an AI teaching assistant. For example, the Assistant section provides a chatbot that is available 24 hours a day to answer learners' questions. The Assistant section can also provide advice according to the learner's progress. The Reporting section visualizes learning progress and provides reports to parents and teachers. For example, the Reporting section displays the learner's progress in graphs and charts so that parents and teachers can understand the learner's situation. The Reporting section can also evaluate the learner's level of understanding and suggest the next steps. As a result, the online platform according to the embodiment can provide customized educational content based on the learner's progress and level of understanding, thereby improving the learner's comprehension.

[0071] The analytics unit analyzes learners' progress and understanding. For example, it analyzes learners' test results and learning history to evaluate their comprehension. Specifically, the analytics unit analyzes in detail the learner's scores, response time, and the types of questions they answered incorrectly on past tests. This allows for a clear understanding of the learner's strengths and weaknesses. The analytics unit can also analyze learners' learning styles and interests. For example, it analyzes whether learners prefer visual or auditory learning. This involves analyzing what types of content learners frequently use and which formats of content they perform best with. Furthermore, the analytics unit can use AI to analyze learners' behavioral patterns and identify when learners can learn most effectively and in what environments their concentration is highest. This allows the analytics unit to collect data to provide the optimal learning environment for each individual learner and build a foundation for maximizing learning efficiency.

[0072] The service provider offers individually customized learning plans based on data analyzed by the analysis unit. For example, the service provider provides real-time feedback based on the learner's progress. Specifically, immediately after a test, the service provider provides detailed explanations of correct and incorrect answers, clearly indicating where mistakes were made and how to improve. The service provider can also recommend content tailored to the learner's learning style and interests. For instance, it provides video content for learners who prefer visual learning and simulations for those who prefer hands-on learning. Furthermore, the service provider dynamically adjusts the learning plan based on the learner's progress, ensuring they always have access to the most relevant learning content. For example, if a learner struggles with a particular area, the service provider provides supplementary materials and additional practice problems related to that area. The service provider also implements achievement goals and reward systems to maintain learner motivation and encourage them to strive towards their goals. This allows the service provider to provide an optimal learning experience for each individual learner, maximizing learning effectiveness.

[0073] The Content Department provides interactive content. For example, it offers a variety of content formats, such as videos, quizzes, and simulations, allowing learners to learn at their own pace. Specifically, the Content Department selects themes and topics that are likely to interest learners and creates content based on them. For instance, in science classes, it allows students to experience theories firsthand through experimental simulations, and in history classes, it provides videos recreating historical events. The Content Department can also update content according to learners' progress. For example, once a learner completes a particular unit, it provides more advanced content as the next step. Furthermore, the Content Department enhances learner engagement by incorporating interactive elements. For instance, quiz-style content allows learners to check their understanding as they progress, and simulation-style content allows learners to learn by actually operating the system. This enables the Content Department to provide a fun and effective learning environment, improving the quality of learning.

[0074] The Assistant Department provides an AI teaching assistant. For example, it offers a 24 / 7 chatbot to answer learners' questions. Specifically, the Assistant Department uses natural language processing technology to understand learners' questions and provide appropriate answers. For instance, if a learner asks a question about a math problem, the chatbot will explain the solution step-by-step. The Assistant Department can also provide advice based on the learner's progress. For example, if a learner is struggling with a particular area, it will recommend supplementary materials and practice problems related to that area. Furthermore, based on the learner's learning history, the Assistant Department can suggest what to learn next and effective learning methods. This allows the Assistant Department to support learners in continuing to learn at their own pace and improve learning efficiency. Additionally, the Assistant Department can collect learner feedback and continuously improve the accuracy of the chatbot's answers and the quality of its advice. This allows the Assistant Department to always provide learners with optimal support and enhance their learning experience.

[0075] The reporting department visualizes learning progress and provides reports to parents and teachers. For example, the reporting department displays learners' progress using graphs and charts, allowing parents and teachers to understand the learners' situation. Specifically, the reporting department details which units learners understand to what extent and which areas they struggle with. The reporting department can also evaluate learners' understanding and suggest the next steps. For example, if a learner completes a particular unit, it can suggest the next content to learn and supplementary materials. Furthermore, the reporting department can provide information for developing long-term learning plans based on learners' progress data. This allows parents and teachers to understand learners' learning status in real time and provide appropriate support. In addition, the reporting department can introduce achievement goals and reward systems to maintain learners' motivation and increase their willingness to work towards their goals. In this way, the reporting department can effectively manage learners' progress and improve the quality of learning.

[0076] The analysis unit can analyze learners' learning styles and interests. For example, it can analyze whether learners prefer visual or auditory learning. The analysis unit can also analyze survey results and past learning history to analyze learners' interests. This allows for the provision of more effective learning plans by performing analysis based on learners' learning styles and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input learners' survey results into AI and have the AI ​​perform the analysis of learning styles and interests.

[0077] The service provider can provide real-time feedback based on the learner's progress. For example, it can send immediate comments or notifications when a learner is working on a particular task. It can also suggest what the learner should learn next based on their progress. For instance, if a learner is struggling in a particular area, the service provider can recommend supplementary materials or practice problems related to that area. This real-time feedback tailored to the learner's progress can enhance learning effectiveness. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input learner progress data into an AI and have the AI ​​provide real-time feedback.

[0078] The content unit can provide content in various formats, such as videos, quizzes, and simulations. For example, if a learner prefers visual learning, the content unit can provide video content. Alternatively, if a learner prefers practical learning, the content unit can provide simulations. Furthermore, the content unit updates the content according to the learner's progress, allowing learners to learn at their own pace. This diverse content format can engage learners and enhance learning effectiveness. Some or all of the above processes in the content unit may be performed using AI, for example, or without AI. For instance, the content unit can input learner learning style data into AI, allowing the AI ​​to select the most suitable content.

[0079] The assistant unit can provide a chatbot that is available 24 hours a day. For example, the assistant unit can provide immediate answers when a learner asks a question. The assistant unit can also provide advice according to the learner's progress. For example, if a learner is struggling in a particular area, the assistant unit can provide supplementary information related to that area. This allows the 24 / 7 chatbot to provide an environment where learners can ask questions at any time. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input learner question data into AI and have the AI ​​generate the optimal answer.

[0080] The reporting department can provide reports to parents and teachers. For example, the reporting department can display learners' progress using graphs and charts, allowing parents and teachers to understand the learners' situation. The reporting department can also evaluate the learners' level of understanding and suggest the next steps. For example, if a learner is struggling in a particular area, the reporting department can suggest supplementary materials related to that area. By providing reports to parents and teachers, they can understand the learners' progress and provide appropriate support. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input learner progress data into AI and have the AI ​​generate an optimal report.

[0081] The analysis unit can estimate the learner's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the learner is stressed, the analysis unit can reduce the frequency of analysis and perform it again when the learner is relaxed. The analysis unit can also increase the frequency of analysis when the learner is focused and monitor their progress in real time. Furthermore, if the learner is tired, the analysis unit can temporarily stop the analysis and resume it after a break. This allows for more effective learning support by adjusting the timing of analysis according to the learner'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-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input learner emotion data into an AI and have the AI ​​adjust the timing of the analysis.

[0082] The analysis unit can analyze a learner's past learning history and select the optimal analysis method. For example, the analysis unit can focus on analyzing areas where the learner has struggled in the past to deepen their understanding. It can also analyze areas where the learner excels and provide more advanced learning content. Furthermore, the analysis unit can analyze the learner's past learning patterns and set the optimal analysis timing. This allows for improved learning effectiveness by selecting the optimal analysis method based on past learning history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's past learning data into AI and have the AI ​​select the optimal analysis method.

[0083] The analysis unit can improve the accuracy of its analysis based on the learner's current learning environment and areas of interest. For example, if the learner is learning in a quiet environment, the analysis unit will perform a more detailed analysis. The analysis unit can also prioritize the analysis of data related to the learner's areas of interest. Furthermore, the analysis unit can adjust the accuracy of its analysis according to the performance of the device the learner is using. This allows for more effective learning support by improving the accuracy of the analysis based on the current learning environment and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's current learning environment data into the AI ​​and have the AI ​​perform the improvement of the analysis accuracy.

[0084] The analysis unit can estimate the learner's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the learner is feeling anxious, the analysis unit can prioritize displaying analysis results for areas where the learner has a low level of understanding. Furthermore, if the learner is confident, the analysis unit can prioritize displaying analysis results for more advanced content. In addition, the analysis unit can prioritize displaying analysis results for areas of interest to the learner. This allows for more effective learning support by prioritizing analysis results according to the learner'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-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input learner emotion data into an AI and have the AI ​​determine the priority of analysis results.

[0085] The analysis unit can perform analysis while considering the learner's geographical location information, thereby reflecting region-specific learning trends. For example, the analysis unit can perform analysis based on the educational curriculum of the area where the learner lives. It can also incorporate popular learning resources in the learner's area into the analysis. Furthermore, the analysis unit can include data related to the culture and history specific to the learner's area in the analysis. This allows for the reflection of region-specific learning trends by considering geographical location information during the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the learner's geographical location data into AI and have the AI ​​perform the analysis of region-specific learning trends.

[0086] The analysis unit can analyze learners' social media activities and acquire relevant learning data, which can then be incorporated into the analysis. For example, the analysis unit can incorporate learning resources shared by learners on social media into the analysis. The analysis unit can also include information on educational accounts that learners follow in the analysis. Furthermore, the analysis unit can incorporate topics that learners have shown interest in on social media into the analysis. In this way, by analyzing social media activities, relevant learning data can be acquired and incorporated into the analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input learners' social media data into an AI and have the AI ​​acquire and analyze relevant learning data.

[0087] The service provider can estimate the learner's emotions and adjust the presentation of the learning plan based on the estimated emotions. For example, if the learner is stressed, the service provider can provide a simple and visually easy-to-understand learning plan. If the learner is relaxed, the service provider can also provide a learning plan with detailed explanations. Furthermore, if the learner is excited, the service provider can provide a learning plan with interactive elements. By adjusting the presentation of the learning plan according to the learner's emotions, more effective learning support becomes possible. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input learner emotion data into AI and have the AI ​​adjust the presentation of the learning plan.

[0088] The service provider can adjust the level of detail in the learning plan based on the learner's progress. For example, if a learner is behind schedule, the service provider can provide a detailed, step-by-step learning plan. If the learner is progressing well, the service provider can also provide an overview plan. Furthermore, if the learner is progressing quickly, the service provider can provide a learning plan that includes more advanced content. By adjusting the level of detail in the learning plan based on the learner's progress, more effective learning support becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input learner progress data into AI and have the AI ​​adjust the level of detail in the learning plan.

[0089] The service provider can apply different learning plan provision algorithms depending on the learner's category. For example, the service provider can provide a visual and interactive learning plan for elementary school students. It can also provide a learning plan for high school students that includes detailed explanations and practice problems. Furthermore, it can provide a learning plan for working adults that includes content directly related to their work. By applying different learning plan provision algorithms depending on the learner's category, more effective learning support becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input learner category data into AI and have the AI ​​apply the optimal learning plan provision algorithm.

[0090] The service provider can estimate the learner's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if the learner is tired, the service provider can provide a short learning plan. Conversely, if the learner is focused, the service provider can provide a longer learning plan. Furthermore, if the learner is relaxed, the service provider can provide a learning plan of appropriate length. This allows for more effective learning support by adjusting the length of the learning plan according to the learner'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 service provider may be performed using AI, or not. For example, the service provider can input learner emotion data into an AI and have the AI ​​adjust the length of the learning plan.

[0091] The service provider can prioritize learning plans based on learners' submission timings. For example, it can prioritize assignments with approaching deadlines in the learning plan. It can also postpone assignments with longer deadlines and prioritize other important assignments. Furthermore, it can adjust the progress schedule of the learning plan according to the submission timing. This allows for more effective learning support by prioritizing learning plans based on submission timing. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input learner submission timing data into AI and have the AI ​​perform the task of prioritizing learning plans.

[0092] The service provider can adjust the order of the learning plan based on the learner's relevance. For example, the service provider can prioritize incorporating areas of interest to the learner into the learning plan. It can also prioritize highly relevant content based on the learner's past learning history. Furthermore, the service provider can provide the learning plan in the optimal order according to the learner's current learning situation. This allows for more effective learning support by adjusting the order of the learning plan based on relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input learner relevance data into AI and have the AI ​​adjust the order of the learning plan.

[0093] The content unit can estimate the learner's emotions and adjust how the content is displayed based on those emotions. For example, if the learner is stressed, the content unit can display simple, visually easy-to-understand content. If the learner is relaxed, the content unit can also display content with detailed explanations. Furthermore, if the learner is excited, the content unit can display content with interactive elements. This allows for more effective learning support by adjusting how the content is displayed according to the learner'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 content unit may be performed using AI or not. For example, the content unit can input learner emotion data into an AI and have the AI ​​adjust how the content is displayed.

[0094] The content unit can select the most suitable content by referring to the learner's past learning history. For example, the content unit may prioritize displaying content related to areas the learner has struggled with in the past. It can also display advanced content related to areas the learner excels at. Furthermore, the content unit can select the most suitable content based on the learner's past learning patterns. This allows for more effective learning support by selecting the most suitable content based on past learning history. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For example, the content unit can input the learner's past learning data into AI and have the AI ​​select the most suitable content.

[0095] The content unit can customize the content format based on the learner's current learning environment. For example, if the learner is studying in a quiet environment, the content unit can provide content with detailed explanations. Alternatively, if the learner is studying while on the go, the content unit can provide content that can be completed in a short amount of time. Furthermore, the content unit can provide the optimal content format depending on the capabilities of the device the learner is using. This allows for more effective learning support by customizing the content format based on the current learning environment. Some or all of the above processing in the content unit may be performed using AI, for example, or without AI. For instance, the content unit can input data on the learner's current learning environment into the AI ​​and have the AI ​​customize the content format.

[0096] The content unit can estimate the learner's emotions and prioritize content based on those emotions. For example, if the learner is feeling anxious, the content unit may prioritize content in areas where they have a low level of understanding. It can also prioritize more advanced content if the learner is confident. Furthermore, it can prioritize content in areas of interest to the learner. This allows for more effective learning support by prioritizing content according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content unit may be performed using AI, or not. For example, the content unit can input learner emotion data into an AI and have the AI ​​determine the content prioritization.

[0097] The content unit can provide optimal content by taking into account the learner's geographical location. For example, the content unit can provide content based on the educational curriculum of the area where the learner lives. It can also reflect popular learning resources in the learner's area in its content. Furthermore, the content unit can provide content related to the culture and history specific to the learner's area. In this way, by providing optimal content that takes geographical location into account, it can address region-specific learning needs. Some or all of the above processing in the content unit may be performed using AI, for example, or not using AI. For example, the content unit can input the learner's geographical location data into AI and have the AI ​​perform the task of providing optimal content.

[0098] The content department can analyze learners' social media activity and provide relevant content. For example, the content department can incorporate learning resources shared by learners on social media into the content. It can also include information about educational accounts that learners follow in the content. Furthermore, the content department can incorporate topics that learners have shown interest in on social media into the content. This allows for the provision of relevant content and improved learning effectiveness by analyzing social media activity. Some or all of the above processing in the content department may be performed using AI, for example, or not. For example, the content department can input learners' social media data into an AI and have the AI ​​provide relevant content.

[0099] The assistant unit can estimate the learner's emotions and adjust its response method based on the estimated emotions. For example, if the learner is nervous, the assistant unit will respond in a calm voice. If the learner is relaxed, the assistant unit can also respond in a cheerful voice. Furthermore, if the learner is in a hurry, the assistant unit can provide a quick and concise response. This allows for more effective learning support by adjusting the assistant's response method according to the learner'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 assistant unit may be performed using AI, or not using AI. For example, the assistant unit can input learner emotion data into an AI and have the AI ​​adjust the assistant's response method.

[0100] The assistant unit can provide the most appropriate answers by referring to the learner's past question history. For example, the assistant unit can provide relevant answers based on the content of questions the learner has asked in the past. The assistant unit can also predict common questions from the learner's past question history and prepare answers in advance. Furthermore, the assistant unit can provide more detailed explanations for questions that the learner did not understand in the past. This enhances learning effectiveness by providing the most appropriate answers based on past question history. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not. For example, the assistant unit can input the learner's past question data into an AI and have the AI ​​provide the most appropriate answers.

[0101] The assistant unit can customize its responses based on the learner's current learning status. For example, the assistant unit can provide information related to the task the learner is currently working on. It can also suggest what the learner should learn next, depending on their progress. Furthermore, the assistant unit can provide additional support in areas where the learner is finding it difficult. This allows for more effective learning support by customizing the assistant's responses based on the learner's current learning status. Some or all of the above processes in the assistant unit may be performed using AI, for example, or not. For example, the assistant unit can input the learner's current learning status data into the AI ​​and have the AI ​​customize the responses.

[0102] The assistant unit can estimate the learner's emotions and determine the assistant's priorities based on the estimated emotions. For example, if the learner is feeling anxious, the assistant unit will prioritize responding to urgent questions. Conversely, if the learner is relaxed, the assistant unit can also respond quickly to normal questions. Furthermore, if the learner is excited, the assistant unit can prioritize responding to questions related to topics of interest. This allows for more effective learning support by determining the assistant's priorities according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 assistant unit may be performed using AI, or not using AI. For example, the assistant unit can input learner emotion data into an AI and have the AI ​​determine the assistant's priorities.

[0103] The assistant unit can provide optimal responses by taking into account the learner's geographical location. For example, the assistant unit can provide responses based on the educational curriculum of the area where the learner lives. The assistant unit can also reflect popular learning resources in the learner's area in its responses. Furthermore, the assistant unit can provide information related to the culture and history specific to the learner's area. In this way, by providing optimal assistant responses that take geographical location into account, it can address region-specific learning needs. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not using AI. For example, the assistant unit can input the learner's geographical location data into AI and have the AI ​​perform the task of providing optimal responses.

[0104] The assistant unit can analyze the learner's social media activity and provide relevant assistant responses. For example, the assistant unit can reflect learning resources shared by the learner on social media in its responses. It can also include information about educational accounts the learner follows in its responses. Furthermore, the assistant unit can provide information related to topics the learner has shown interest in on social media. This allows for the provision of relevant assistant responses by analyzing social media activity, thereby enhancing learning effectiveness. Some or all of the above processing in the assistant unit may be performed using AI, for example, or not. For example, the assistant unit can input the learner's social media data into an AI and have the AI ​​provide relevant responses.

[0105] The reporting unit can estimate the learner's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the learner is stressed, the reporting unit can display a simple and visually easy-to-understand report. If the learner is relaxed, the reporting unit can also display a report with detailed explanations. Furthermore, if the learner is excited, the reporting unit can display a report with interactive elements. This allows for more effective learning support by adjusting how the report is displayed according to the learner'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 reporting unit may be performed using AI or not. For example, the reporting unit can input learner emotion data into AI and have the AI ​​adjust how the report is displayed.

[0106] The reporting unit can provide optimal reports by referring to the learner's past learning history. For example, the reporting unit may prioritize displaying reports related to areas the learner has struggled with in the past. It can also display advanced reports related to areas the learner excels at. Furthermore, the reporting unit can provide optimal reports based on the learner's past learning patterns. This enhances learning effectiveness by providing optimal reports based on past learning history. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the learner's past learning data into AI and have the AI ​​perform the task of providing optimal reports.

[0107] The reporting function can customize the content of reports based on the learner's current learning status. For example, the reporting function can provide reports related to the assignments the learner is currently working on. It can also include in the report what the learner should learn next, depending on their progress. Furthermore, the reporting function can create reports that provide additional support for areas where the learner is finding difficult. This allows for more effective learning support by customizing the content of reports based on the current learning status. Some or all of the above processes in the reporting function may be performed using AI, for example, or not. For example, the reporting function can input the learner's current learning status data into an AI and have the AI ​​customize the content of the reports.

[0108] The reporting unit can estimate the learner's emotions and prioritize reports based on those emotions. For example, if the learner is feeling anxious, the reporting unit will prioritize reports on areas where they have a low level of understanding. It can also prioritize reports on more advanced topics if the learner is confident. Furthermore, it can prioritize reports on areas of interest to the learner. This allows for more effective learning support by prioritizing reports according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input learner emotion data into an AI and have the AI ​​determine the report prioritization.

[0109] The reporting function can provide optimal reports by taking into account the learner's geographical location. For example, the reporting function can provide reports based on the educational curriculum of the area where the learner lives. It can also reflect popular learning resources in the learner's area in the report. Furthermore, the reporting function can provide reports related to the culture and history specific to the learner's area. In this way, by providing optimal reports that take geographical location into account, it can address region-specific learning needs. Some or all of the above processing in the reporting function may be performed using AI, for example, or not using AI. For example, the reporting function can input the learner's geographical location data into AI and have the AI ​​perform the task of providing optimal reports.

[0110] The reporting unit can analyze learners' social media activity and provide relevant reports. For example, the reporting unit can reflect learning resources shared by learners on social media in the report. It can also include information about educational accounts that learners follow in the report. Furthermore, the reporting unit can provide reports related to topics that learners have shown interest in on social media. In this way, analyzing social media activity can provide relevant reports and improve learning effectiveness. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input learners' social media data into an AI and have the AI ​​perform the task of providing relevant reports.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The analysis unit can acquire the learner's physiological data and estimate the optimal timing for learning. For example, it can use data such as heart rate and skin electrical activity to identify the time of day when the learner is most focused. The analysis unit can also analyze the learner's sleep patterns and suggest the optimal learning time. Furthermore, the analysis unit can consider the learner's diet and exercise data to estimate the timing that maximizes learning effectiveness. In this way, learning effectiveness can be enhanced by estimating the optimal timing for learning based on physiological data.

[0113] The system can estimate the learner's emotions and adjust the difficulty level of the learning plan based on those estimates. For example, if a learner is stressed, it can provide easier tasks. Conversely, if a learner is relaxed, it can provide more challenging tasks. Furthermore, if a learner is excited, it can provide more challenging tasks. By adjusting the difficulty level of the learning plan according to the learner's emotions, more effective learning support becomes possible.

[0114] The content section can select the most suitable content by referring to the learner's past learning history. For example, it can prioritize displaying content related to areas the learner has struggled with in the past. It can also display advanced content related to areas the learner excels at. Furthermore, it can select the most suitable content based on the learner's past learning patterns. This allows for more effective learning support by selecting the most suitable content based on past learning history.

[0115] The assistant unit can estimate the learner's emotions and adjust its response method based on the estimated emotions. For example, if the learner is nervous, it will respond in a calm voice. If the learner is relaxed, it can respond in a cheerful voice. Furthermore, if the learner is in a hurry, it can provide a quick and concise response. By adjusting the assistant's response method according to the learner's emotions, more effective learning support becomes possible.

[0116] The reporting function can estimate the learner's emotions and adjust how the report is displayed based on those estimates. For example, if a learner is stressed, a simple and visually easy-to-understand report is displayed. If the learner is relaxed, a report with detailed explanations can be displayed. Furthermore, if the learner is excited, a report with interactive elements can be displayed. This allows for more effective learning support by adjusting how the report is displayed according to the learner's emotions.

[0117] The analysis unit can perform analyses while considering the learner's geographical location, thereby reflecting region-specific learning trends. For example, it can perform analyses based on the educational curriculum of the area where the learner lives. It can also incorporate popular learning resources in the learner's area into the analysis. Furthermore, it can include data related to the culture and history specific to the learner's area in the analysis. In this way, by performing analyses while considering geographical location, it can reflect region-specific learning trends.

[0118] The learning department can prioritize learning plans based on the learner's submission schedule. For example, assignments with approaching deadlines can be prioritized in the learning plan. Alternatively, assignments with longer deadlines can be postponed, allowing other important assignments to take priority. Furthermore, the learning plan's progress schedule can be adjusted according to submission timing. This allows for more effective learning support by prioritizing learning plans based on submission timing.

[0119] The content section can customize the content format based on the learner's current learning environment. For example, if a learner is studying in a quiet environment, it can provide content with detailed explanations. Alternatively, if a learner is studying on the go, it can provide content that can be completed in a short amount of time. Furthermore, it can provide the optimal content format depending on the capabilities of the device the learner is using. This allows for more effective learning support by customizing the content format based on the current learning environment.

[0120] The assistant unit can analyze learners' social media activity and provide relevant assistant responses. For example, it can reflect learning resources shared by learners on social media in its responses. It can also include information about educational accounts that learners follow. Furthermore, it can provide information related to topics that learners have shown interest in on social media. In this way, by analyzing social media activity, relevant assistant responses can be provided, thereby enhancing learning effectiveness.

[0121] The reporting system can estimate the learner's emotions and prioritize reports based on those emotions. For example, if a learner is feeling anxious, reports on areas where they have difficulty understanding will be displayed first. Conversely, if a learner is confident, reports on more advanced topics may be displayed first. Furthermore, reports on areas of interest to the learner may also be displayed first. This allows for more effective learning support by prioritizing reports according to the learner's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The analysis unit analyzes the learner's progress and understanding. For example, it analyzes the learner's test results and learning history to evaluate their comprehension. It can also analyze the learner's learning style and interests. For example, it can analyze whether the learner prefers visual or auditory learning. Step 2: The provisioning unit provides individually customized learning plans based on the data analyzed by the analysis unit. For example, it provides real-time feedback according to the learner's progress and recommends content that matches their learning style and interests. It provides video content for learners who prefer visual learning and simulations for learners who prefer practical learning. Step 3: The content department provides interactive content. For example, it provides diverse content formats such as videos, quizzes, and simulations, allowing learners to learn at their own pace. It can also update the content according to the learners' progress. Step 4: The Assistant Department provides an AI teaching assistant. For example, it provides a chatbot that is available 24 / 7 to answer learners' questions. It can also provide advice based on the learner's progress. Step 5: The reporting department visualizes learning progress and provides reports to parents and teachers. For example, it displays learners' progress using graphs and charts so that parents and teachers can understand the learners' situation. It can also evaluate the learners' level of understanding and suggest the next steps.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the analysis unit, provision unit, content unit, assistant unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's progress and understanding. The provision unit is implemented by the control unit 46A of the smart device 14 and provides an individually customized learning plan based on the analyzed data. The content unit is implemented by the control unit 46A of the smart device 14 and provides interactive content. The assistant unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an AI teaching assistant. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the learning progress and provides reports to parents and teachers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the analysis unit, provision unit, content unit, assistant unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's progress and understanding. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides an individually customized learning plan based on the analyzed data. The content unit is implemented by the control unit 46A of the smart glasses 214 and provides interactive content. The assistant unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an AI teaching assistant. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the learning progress and provides a report to parents and teachers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the analysis unit, provision unit, content unit, assistant unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's progress and level of understanding. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides an individually customized learning plan based on the analyzed data. The content unit is implemented by the control unit 46A of the headset terminal 314 and provides interactive content. The assistant unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an AI teaching assistant. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the learning progress and provides a report to parents and teachers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the analysis unit, provision unit, content unit, assistant unit, and reporting unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's progress and understanding. The provision unit is implemented by the control unit 46A of the robot 414 and provides an individually customized learning plan based on the analyzed data. The content unit is implemented by the control unit 46A of the robot 414 and provides interactive content. The assistant unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an AI teaching assistant. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the learning progress and provides a report to parents and teachers. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) An analysis unit that analyzes the learner's progress and level of understanding, A provisioning unit provides a learning plan that is individually customized based on the data analyzed by the aforementioned analysis unit. The Content Department provides interactive content, The Assistant Department provides AI teaching assistants, It includes a reporting section that visualizes learning progress and provides reports. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze learners' learning styles and interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide real-time feedback based on the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned content section is, We offer content in various formats, including videos, quizzes, and simulations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned assistant section is We offer a chatbot that is available 24 hours a day. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned report section is, Provide reports to parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The system estimates the learner's emotions and adjusts the timing of the analysis based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyze the learner's past learning history and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Improve the accuracy of the analysis based on the learner's current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the learner's emotions and prioritizes the analysis results based on the estimated learner emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The analysis takes into account the learners' geographical location to reflect region-specific learning trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Analyze learners' social media activity, obtain relevant learning data, and incorporate it into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We estimate learners' emotions and adjust the way learning plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, Adjust the level of detail in the learning plan based on the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, Apply different learning plan delivery algorithms depending on the learner's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The system estimates the learner's emotions and adjusts the length of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, Prioritize learning plans based on learners' submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, Adjust the order of the learning plan based on the learner's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned content section is, It estimates learners' emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned content section is, Select the most suitable content by referring to the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned content section is, Customize the content format based on the learner's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned content section is, It estimates learners' emotions and prioritizes content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned content section is, Provide optimal content by taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned content section is, Analyze learners' social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned assistant section is The system estimates the learner's emotions and adjusts the assistant's response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned assistant section is Provides the best answer by referring to the learner's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned assistant section is Customize the assistant's responses based on the learner's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned assistant section is The system estimates the learner's emotions and prioritizes the assistant based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned assistant section is Provides optimal assistant responses considering the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned assistant section is Analyze learners' social media activity and provide relevant assistant responses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned report section is, The system estimates learners' emotions and adjusts how reports are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned report section is, Provides optimal reports by referencing the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned report section is, Customize the report content based on the learner's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned report section is, The system estimates learners' emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned report section is, Provides optimal reports that take into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned report section is, We analyze learners' social media activity and provide relevant reports. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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. An analysis unit that analyzes the learner's progress and level of understanding, A provisioning unit provides a learning plan that is individually customized based on the data analyzed by the aforementioned analysis unit. The Content Department provides interactive content, The Assistant Department provides AI teaching assistants, It includes a reporting section that visualizes learning progress and provides reports. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze learners' learning styles and interests. The system according to feature 1.

3. The aforementioned supply unit is, Provide real-time feedback based on the learner's progress. The system according to feature 1.

4. The aforementioned content section is, We offer content in various formats, including videos, quizzes, and simulations. The system according to feature 1.

5. The aforementioned assistant section is We offer a chatbot that is available 24 hours a day. The system according to feature 1.

6. The aforementioned report section is, Provide reports to parents and teachers. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates the learner's emotions and adjusts the timing of the analysis based on the estimated learner's emotions. The system according to feature 1.

8. The aforementioned analysis unit, Analyze the learner's past learning history and select the optimal analysis method. The system according to feature 1.

9. The aforementioned analysis unit, Improve the accuracy of the analysis based on the learner's current learning environment and areas of interest. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the learner's emotions and prioritizes the analysis results based on the estimated learner emotions. The system according to feature 1.

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