system

Abstract

The system according to this embodiment aims to provide a personalized learning plan based on individual learning styles and needs. [Solution] The system according to the embodiment comprises a planning unit, a feedback unit, and an optimization unit. The planning unit provides a personalized learning plan based on individual learning styles and needs. The feedback unit provides immediate feedback based on learning progress based on the learning plan provided by the planning unit. The optimization unit analyzes learning data based on the feedback provided by the feedback unit and optimizes the educational program.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can provide a personalized learning plan based on individual learning styles and needs. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a 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 AI education agent according to an embodiment of the present invention is a system that provides a comprehensive online education platform for AI technology. This system is characterized by its ability to enable corporations and individuals to learn AI from its fundamentals to its applications. The AI ​​education agent provides personalized learning plans based on individual learning styles and needs. This allows learners to learn at their own optimal pace, improving learning efficiency. For example, the AI ​​education agent analyzes the learner's learning history and learning style to generate an optimal learning plan. Next, the AI ​​education agent provides immediate feedback based on learning progress. This allows learners to constantly understand their level of comprehension and make necessary adjustments. For example, the AI ​​education agent analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. Furthermore, the AI ​​education agent analyzes learning data to optimize the educational program. This ensures that the educational program is always up-to-date and provides learners with the most suitable content. For example, the AI ​​education agent automatically updates the content of the educational program based on the learner's progress and feedback. The AI ​​education agent also implements a cloud-based learning management system and develops interactive learning tools. This allows learners to learn anytime, anywhere, improving learning flexibility. For example, an AI education agent stores learning data in the cloud, allowing learners to access it from different devices. Interactive learning tools are designed to allow learners to learn by doing, enriching the learning experience. This enables the AI ​​education agent to leverage AI technology to provide learners with the optimal learning experience, thereby promoting the widespread adoption and efficiency of AI technology. As a result, the AI ​​education agent can provide learners with optimal learning plans, instant feedback, and optimized educational programs.

[0029] The AI ​​education agent according to this embodiment comprises a planning unit, a feedback unit, and an optimization unit. The planning unit provides a personalized learning plan based on the individual's learning style and needs. For example, the planning unit analyzes the learner's learning history and learning style to generate an optimal learning plan. The planning unit can use AI to analyze the learner's data and customize it based on individual learning goals. For example, the planning unit can provide plans that suit learning styles such as visual learning, auditory learning, and experiential learning. Based on the learner's needs, the planning unit can provide plans that take into account learning goals, learning pace, and acquisition of specific skills. The feedback unit provides real-time feedback based on the learning progress, using the learning plan provided by the planning unit. For example, the feedback unit analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. The feedback unit can use AI to analyze the learner's data and provide real-time comments, scoring, advice, etc. For example, the feedback unit can provide detailed explanations and additional reference materials according to the learner's level of understanding. The optimization unit analyzes learning data based on the feedback provided by the feedback unit to optimize the educational program. The optimization unit automatically updates the content of the educational program based, for example, on the learner's progress and feedback. The optimization unit can analyze learning data using AI and optimize the educational program using algorithms and data analysis techniques. For example, the optimization unit can adjust the content of the educational program according to the learner's needs and progress. As a result, the AI ​​educational agent according to this embodiment can provide learners with an optimal learning plan, immediate feedback, and an optimized educational program.

[0030] The Planning Department provides personalized learning plans based on individual learning styles and needs. For example, the Planning Department analyzes learners' learning history and learning styles to generate optimal learning plans. Specifically, it collects data such as learners' past performance, learning progress, and strengths and weaknesses, and this data is analyzed by AI. The AI ​​uses machine learning algorithms to understand learners' patterns and tendencies and customizes plans based on individual learning goals. For example, visual learners are provided with materials that make extensive use of diagrams and graphs, while auditory learners are provided with materials centered on audio and lecture videos. Experiential learners are provided with materials that include practical exercises and simulations. Furthermore, the Planning Department provides plans that take into account learning goals, learning pace, and specific skill acquisition based on learners' needs. For example, it can create flexible plans tailored to learners' goals, such as short-term intensive plans for specific exams or plans aimed at long-term skill acquisition. The Planning Department regularly reviews these plans and makes adjustments based on learners' progress and feedback. This allows the Planning Department to provide learners with the optimal learning plan and support effective learning.

[0031] The Feedback Department provides real-time feedback based on learning progress, using the learning plan provided by the Planning Department. For example, the Feedback Department analyzes learners' test results and assignment submission status in real time to provide appropriate feedback. Specifically, it uses AI to analyze learners' test results and understand their accuracy and error patterns. Based on this, it identifies what learners understand and what they don't fully understand, and provides detailed explanations and additional reference materials. For example, if a learner makes many mistakes on a particular question, it provides a detailed explanation of the fundamental concepts and application examples related to that question. It also monitors assignment submission status in real time and sends reminders if a learner is likely to miss the deadline. Furthermore, the Feedback Department provides real-time comments, scoring, and advice according to the learner's level of understanding. For example, when a learner completes a particular assignment, it immediately displays the score and suggests the next assignment or reference materials to tackle. In this way, the Feedback Department can provide learners with immediate and appropriate feedback, supporting effective learning while maintaining their motivation.

[0032] The optimization unit analyzes learning data based on feedback provided by the feedback unit to optimize the educational program. For example, the optimization unit automatically updates the content of the educational program based on the learner's progress and feedback. Specifically, it uses AI to analyze learner data and optimizes the educational program using algorithms and data analysis methods. For example, if a learner has difficulty in a particular area, the optimization unit supports deepening their understanding by increasing the amount of teaching materials and practice problems related to that area. It can also adjust the difficulty level of the educational program according to the learner's progress. For example, if a learner is progressing well, it provides more advanced content, and conversely, if they are falling behind, it provides opportunities to review basic content. Furthermore, the optimization unit adjusts the content of the educational program according to the learner's needs and progress. For example, learners aiming to acquire a specific skill are provided with teaching materials and exercises specialized for that skill, while learners aiming for overall academic improvement are provided with a balanced educational program. In this way, the optimization unit can provide learners with the most suitable educational program and support effective learning. Furthermore, the optimization unit regularly evaluates the effectiveness of the educational program and makes improvements as needed. This allows the optimization unit to constantly incorporate the latest information and technology to provide optimal educational programs and support the growth of learners.

[0033] The management department provides a cloud-based learning management system. For example, the management department uses cloud services to store learning data, allowing learners to access it from different devices. Using the cloud-based learning management system, the management department can centrally manage learners' progress and learning history. For example, the management department stores learner data in the cloud, enabling learners to study anytime, anywhere. The management department can update learner data in real time using the cloud-based learning management system, providing the latest information. For example, the management department automatically updates learning plans based on learners' progress and feedback. This allows learners to study anytime, anywhere through the cloud-based learning management system.

[0034] The Tools Division provides interactive learning tools. For example, the Tools Division develops interactive learning tools such as quizzes, simulations, and interactive content. The Tools Division designs interactive learning tools to allow learners to learn through hands-on experience. For example, the Tools Division allows learners to confirm their learning by answering quizzes. The Tools Division uses simulations to enable learners to learn by experiencing real-world situations. For example, the Tools Division provides simulation tools that allow learners to conduct experiments in a virtual environment. The Tools Division uses interactive content to enable learners to learn interactively. For example, the Tools Division provides interactive content that allows learners to learn by interacting with a virtual teacher. This enhances the learner's learning experience through interactive learning tools.

[0035] The adjustment unit can adjust the curriculum based on the learner's progress. For example, the adjustment unit can analyze the learner's test results and assignment submission status and adjust the curriculum content. The adjustment unit can use AI to analyze learner data and adjust the curriculum according to the learner's progress. For example, the adjustment unit can focus on areas where the learner struggles and incorporate them into the learning plan. The adjustment unit can leverage the learner's strengths to provide a curriculum that includes applied content. The adjustment unit can adjust the learning pace according to the learner's progress. For example, if the learner is falling behind, the adjustment unit can slow down the learning pace. By adjusting the curriculum according to the learner's progress, learning efficiency is improved. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input learner progress data into a generating AI and have the generating AI perform the curriculum adjustment.

[0036] The planning unit can analyze a learner's past learning history and generate an optimal learning plan. For example, the planning unit can focus on areas the learner has struggled with in the past and incorporate more advanced content that leverages the learner's strengths. The planning unit can provide a manageable learning plan based on the learner's past learning pace. In this way, by analyzing past learning history, the planning unit can provide the learner with the most suitable learning plan. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input the learner's past learning history data into a generating AI and have the generating AI generate an optimal learning plan.

[0037] The planning unit can customize learning plans based on the learner's current skill level and goals when generating them. For example, the planning unit can assess the learner's current skill level and provide a step-by-step learning plan from basic to advanced levels. The planning unit can create learning plans to strengthen specific skills in line with the learner's goals. The planning unit can include tasks of appropriate difficulty depending on the learner's skill level. This improves learning effectiveness by providing learning plans tailored to the learner's skill level and goals. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input learner skill level data into a generating AI and have the generating AI customize the learning plan.

[0038] The planning unit can prioritize the inclusion of highly relevant content when generating a learning plan, taking into account the learner's geographical location. For example, if a learner lives in a specific region, the planning unit can include examples related to that region in the learning plan. If a learner is traveling, the planning unit can prioritize providing content related to their travel destination. If a learner is interested in the culture or history of a particular region, the planning unit can include learning content related to that region. This allows the planning unit to provide highly relevant learning content by considering the learner's geographical location. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input the learner's geographical location data into a generating AI and have the generating AI select highly relevant content.

[0039] The planning unit can analyze the learner's social media activity and include relevant content when generating a learning plan. For example, the planning unit can incorporate topics the learner has shown interest in on social media into the learning plan. The planning unit can create a learning plan by referencing posts from experts and influencers the learner follows. The planning unit can reflect topics from online communities the learner participates in into the learning plan. This allows the planning unit to provide relevant learning content by analyzing the learner's social media activity. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input the learner's social media activity data into a generating AI and have the generating AI select relevant content.

[0040] The feedback unit can adjust the level of detail of the feedback based on the learner's level of understanding when providing feedback. For example, if the learner's level of understanding is low, the feedback unit can provide feedback that includes detailed explanations. If the learner's level of understanding is high, the feedback unit can provide concise feedback. The feedback unit can also provide additional reference materials depending on the learner's level of understanding. By adjusting the level of detail of the feedback according to the learner's level of understanding, the learner's understanding is deepened. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0041] The feedback unit can apply different feedback algorithms depending on the learner's learning style when providing feedback. For example, the feedback unit can provide feedback that makes extensive use of diagrams and graphs to learners with a visual learning style. The feedback unit can provide audio feedback to learners with an auditory learning style. The feedback unit can provide feedback that includes practical advice to learners with an experiential learning style. This improves learning effectiveness by providing feedback tailored to the learner's learning style. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner learning style data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0042] The feedback unit can prioritize feedback based on the learner's submission timing when providing feedback. For example, the feedback unit can prioritize feedback for learners whose submission deadlines are approaching. The feedback unit can provide detailed feedback to learners who submit early. The feedback unit can provide prompt feedback to learners who submit late. This enables efficient feedback by prioritizing feedback according to the learner's submission timing. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner submission timing data into a generating AI and have the generating AI determine the priority of feedback.

[0043] The optimization unit can optimize the optimization algorithm by referring to past training data during the optimization process. For example, the optimization unit can apply an algorithm that focuses on strengthening the learner's weak areas based on past training data. The optimization unit can apply an algorithm that provides advanced content that leverages the learner's strengths based on past training data. The optimization unit can apply an optimization algorithm that matches the learner's learning pace based on past training data. As a result, the accuracy of the optimization algorithm is improved by referring to past training data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past training data into a generating AI and have the generating AI perform the optimization of the optimization algorithm.

[0044] The optimization unit can customize the educational program based on the learner's current skill level during optimization. For example, the optimization unit can evaluate the learner's current skill level and provide a step-by-step educational program from basic to advanced levels. The optimization unit can provide an educational program that includes tasks of appropriate difficulty according to the learner's skill level. The optimization unit can provide an educational program to strengthen specific skills based on the learner's skill level. This improves learning effectiveness by providing an educational program tailored to the learner's skill level. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input learner skill level data into a generating AI and have the generating AI perform the customization of the educational program.

[0045] The management unit can select the optimal display method when displaying the learning management system by referring to the learner's past operation history. For example, the management unit can prioritize displaying interface designs that the learner has preferred to use in the past. The management unit can place functions that the learner has frequently accessed in the past in a prominent position. The management unit can analyze the learner's operation history and propose the most efficient display method. In this way, the optimal display method can be provided by referring to the learner's past operation history. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the learner's operation history data into a generating AI and have the generating AI select the optimal display method.

[0046] The management unit can select the optimal display method when displaying the learning management system, taking into account the learner's device information. For example, if the learner is using a smartphone, the management unit can provide a display method that matches the screen size. If the learner is using a tablet, the management unit can provide a display method optimized for a larger screen. If the learner is using a desktop, the management unit can provide a display method that efficiently arranges multiple windows. In this way, the optimal display method can be provided by taking into account the learner's device information. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input learner device information data into a generating AI and have the generating AI select the optimal display method.

[0047] The tool unit can select the optimal operation method by referring to the learner's past operation history when operating an interactive learning tool. For example, the tool unit can prioritize providing operation methods that the learner has preferred to use in the past. The tool unit can place functions that the learner has frequently used in the past in a prominent position. The tool unit can analyze the learner's operation history and suggest the most efficient operation method. In this way, it can provide the optimal operation method by referring to the learner's past operation history. Some or all of the above processing in the tool unit may be performed using AI or not. For example, the tool unit can input the learner's operation history data into a generating AI and have the generating AI perform the selection of the optimal operation method.

[0048] The tool unit can select the optimal operation method when interacting with interactive learning tools, taking into account the learner's device information. For example, if the learner is using a smartphone, the tool unit can provide an operation method adapted to the screen size. If the learner is using a tablet, the tool unit can provide an operation method optimized for a larger screen. If the learner is using a desktop, the tool unit can provide a method for efficiently operating multiple windows. In this way, the tool unit can provide the optimal operation method by taking into account the learner's device information. Some or all of the above processing in the tool unit may be performed using AI or not. For example, the tool unit can input learner device information data into a generating AI and have the generating AI select the optimal operation method.

[0049] The adjustment unit can select the optimal adjustment method when adjusting the curriculum by referring to the learner's past learning history. For example, the adjustment unit can focus on incorporating areas that the learner has struggled with in the past into the curriculum. The adjustment unit can provide a curriculum that includes applied content by leveraging the learner's strengths. The adjustment unit can provide a curriculum that is manageable by referring to the learner's past learning pace. In this way, the optimal curriculum can be provided by referring to the learner's past learning history. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the learner's past learning history data into a generating AI and have the generating AI select the optimal adjustment method.

[0050] The adjustment unit can select the optimal adjustment method when adjusting the curriculum, taking into account the learner's geographical location information. For example, if a learner lives in a specific region, the adjustment unit can provide a curriculum that includes examples related to that region. If a learner is traveling, the adjustment unit can provide a curriculum that includes content related to their travel destination. If a learner is interested in the culture or history of a specific region, the adjustment unit can provide a curriculum related to that region. In this way, the optimal curriculum can be provided by taking into account the learner's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the learner's geographical location information data into a generating AI and have the generating AI select the optimal adjustment method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The planning department can monitor learners' health status and adjust learning plans based on that status. For example, if a learner is feeling unwell, the planning department can provide a plan that reduces the learning load and includes more breaks. If a learner is healthy, the planning department can provide a normal learning plan. If a learner is tired after exercise, the planning department can provide a plan that includes relaxing content. By adjusting learning plans according to the learner's health status, learning efficiency is improved.

[0053] The feedback unit can monitor the learner's learning environment and adjust the content of the feedback based on that environment. For example, if the learner is learning in a quiet environment, the feedback unit can provide detailed feedback. If the learner is learning in a noisy environment, the feedback unit can provide concise and to-the-point feedback. If the learner is learning while on the go, the feedback unit can provide audio feedback. By adjusting the content of the feedback according to the learner's learning environment, the learning effect is improved.

[0054] The optimization unit can analyze learners' learning history and optimize educational programs based on their long-term goals. For example, if a learner is aiming to obtain a specific qualification, the optimization unit can provide an educational program that focuses on content related to that qualification. If a learner is aiming for career advancement, the optimization unit can provide an educational program that strengthens the necessary skills. If a learner is learning as a hobby, the optimization unit can provide an educational program that includes content that is interesting to them. By optimizing educational programs according to learners' long-term goals, learners' motivation is improved.

[0055] The management unit can monitor the battery level of the learner's device and adjust the display method of the learning management system based on the battery level. For example, when the battery level is low, the management unit can provide a simple, low-power display method. When the battery level is sufficient, the management unit can provide a normal display method. When the battery level is moderate, the management unit can provide a balanced display method. This improves the learner's convenience by adjusting the display method according to the battery level of the learner's device.

[0056] The tool unit can monitor the learner's progress and adjust the content of the interactive learning tools based on that progress. For example, if a learner is falling behind, the tool unit can focus on providing foundational content. If a learner is progressing well, the tool unit can provide more advanced content. If a learner is progressing quickly, the tool unit can provide more challenging content. By adjusting the content of the interactive learning tools according to the learner's progress, learning effectiveness is improved.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The planning department provides personalized learning plans based on individual learning styles and needs. The planning department analyzes the learner's learning history and learning style to generate an optimal learning plan. Using AI, it analyzes the learner's data and customizes it based on individual learning goals. For example, it can provide plans tailored to learning styles such as visual learning, auditory learning, and experiential learning, and can provide plans that take into account learning goals, learning pace, and acquisition of specific skills. Step 2: The Feedback Department provides real-time feedback based on learning progress, using the learning plan provided by the Planning Department. The Feedback Department analyzes learners' test results and assignment submission status in real time and provides appropriate feedback. Using AI, it analyzes learner data and provides real-time comments, scoring, and advice. For example, it can provide detailed explanations and additional reference materials depending on the learner's level of understanding. Step 3: The optimization unit analyzes the learning data based on the feedback provided by the feedback unit and optimizes the educational program. The optimization unit automatically updates the content of the educational program based on the learner's progress and feedback. It uses AI to analyze the learning data and optimizes the educational program using algorithms and data analysis methods. For example, it can adjust the content of the educational program according to the learner's needs and progress.

[0059] (Example of form 2) An AI education agent according to an embodiment of the present invention is a system that provides a comprehensive online education platform for AI technology. This system is characterized by its ability to enable corporations and individuals to learn AI from its fundamentals to its applications. The AI ​​education agent provides personalized learning plans based on individual learning styles and needs. This allows learners to learn at their own optimal pace, improving learning efficiency. For example, the AI ​​education agent analyzes the learner's learning history and learning style to generate an optimal learning plan. Next, the AI ​​education agent provides immediate feedback based on learning progress. This allows learners to constantly understand their level of comprehension and make necessary adjustments. For example, the AI ​​education agent analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. Furthermore, the AI ​​education agent analyzes learning data to optimize the educational program. This ensures that the educational program is always up-to-date and provides learners with the most suitable content. For example, the AI ​​education agent automatically updates the content of the educational program based on the learner's progress and feedback. The AI ​​education agent also implements a cloud-based learning management system and develops interactive learning tools. This allows learners to learn anytime, anywhere, improving learning flexibility. For example, an AI education agent stores learning data in the cloud, allowing learners to access it from different devices. Interactive learning tools are designed to allow learners to learn by doing, enriching the learning experience. This enables the AI ​​education agent to leverage AI technology to provide learners with the optimal learning experience, thereby promoting the widespread adoption and efficiency of AI technology. As a result, the AI ​​education agent can provide learners with optimal learning plans, instant feedback, and optimized educational programs.

[0060] The AI ​​education agent according to this embodiment comprises a planning unit, a feedback unit, and an optimization unit. The planning unit provides a personalized learning plan based on the individual's learning style and needs. For example, the planning unit analyzes the learner's learning history and learning style to generate an optimal learning plan. The planning unit can use AI to analyze the learner's data and customize it based on individual learning goals. For example, the planning unit can provide plans that suit learning styles such as visual learning, auditory learning, and experiential learning. Based on the learner's needs, the planning unit can provide plans that take into account learning goals, learning pace, and acquisition of specific skills. The feedback unit provides real-time feedback based on the learning progress, using the learning plan provided by the planning unit. For example, the feedback unit analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. The feedback unit can use AI to analyze the learner's data and provide real-time comments, scoring, advice, etc. For example, the feedback unit can provide detailed explanations and additional reference materials according to the learner's level of understanding. The optimization unit analyzes learning data based on the feedback provided by the feedback unit to optimize the educational program. The optimization unit automatically updates the content of the educational program based, for example, on the learner's progress and feedback. The optimization unit can analyze learning data using AI and optimize the educational program using algorithms and data analysis techniques. For example, the optimization unit can adjust the content of the educational program according to the learner's needs and progress. As a result, the AI ​​educational agent according to this embodiment can provide learners with an optimal learning plan, immediate feedback, and an optimized educational program.

[0061] The Planning Department provides personalized learning plans based on individual learning styles and needs. For example, the Planning Department analyzes learners' learning history and learning styles to generate optimal learning plans. Specifically, it collects data such as learners' past performance, learning progress, and strengths and weaknesses, and this data is analyzed by AI. The AI ​​uses machine learning algorithms to understand learners' patterns and tendencies and customizes plans based on individual learning goals. For example, visual learners are provided with materials that make extensive use of diagrams and graphs, while auditory learners are provided with materials centered on audio and lecture videos. Experiential learners are provided with materials that include practical exercises and simulations. Furthermore, the Planning Department provides plans that take into account learning goals, learning pace, and specific skill acquisition based on learners' needs. For example, it can create flexible plans tailored to learners' goals, such as short-term intensive plans for specific exams or plans aimed at long-term skill acquisition. The Planning Department regularly reviews these plans and makes adjustments based on learners' progress and feedback. This allows the Planning Department to provide learners with the optimal learning plan and support effective learning.

[0062] The Feedback Department provides real-time feedback based on learning progress, using the learning plan provided by the Planning Department. For example, the Feedback Department analyzes learners' test results and assignment submission status in real time to provide appropriate feedback. Specifically, it uses AI to analyze learners' test results and understand their accuracy and error patterns. Based on this, it identifies what learners understand and what they don't fully understand, and provides detailed explanations and additional reference materials. For example, if a learner makes many mistakes on a particular question, it provides a detailed explanation of the fundamental concepts and application examples related to that question. It also monitors assignment submission status in real time and sends reminders if a learner is likely to miss the deadline. Furthermore, the Feedback Department provides real-time comments, scoring, and advice according to the learner's level of understanding. For example, when a learner completes a particular assignment, it immediately displays the score and suggests the next assignment or reference materials to tackle. In this way, the Feedback Department can provide learners with immediate and appropriate feedback, supporting effective learning while maintaining their motivation.

[0063] The optimization unit analyzes learning data based on feedback provided by the feedback unit to optimize the educational program. For example, the optimization unit automatically updates the content of the educational program based on the learner's progress and feedback. Specifically, it uses AI to analyze learner data and optimizes the educational program using algorithms and data analysis methods. For example, if a learner has difficulty in a particular area, the optimization unit supports deepening their understanding by increasing the amount of teaching materials and practice problems related to that area. It can also adjust the difficulty level of the educational program according to the learner's progress. For example, if a learner is progressing well, it provides more advanced content, and conversely, if they are falling behind, it provides opportunities to review basic content. Furthermore, the optimization unit adjusts the content of the educational program according to the learner's needs and progress. For example, learners aiming to acquire a specific skill are provided with teaching materials and exercises specialized for that skill, while learners aiming for overall academic improvement are provided with a balanced educational program. In this way, the optimization unit can provide learners with the most suitable educational program and support effective learning. Furthermore, the optimization unit regularly evaluates the effectiveness of the educational program and makes improvements as needed. This allows the optimization unit to constantly incorporate the latest information and technology to provide optimal educational programs and support the growth of learners.

[0064] The management department provides a cloud-based learning management system. For example, the management department uses cloud services to store learning data, allowing learners to access it from different devices. Using the cloud-based learning management system, the management department can centrally manage learners' progress and learning history. For example, the management department stores learner data in the cloud, enabling learners to study anytime, anywhere. The management department can update learner data in real time using the cloud-based learning management system, providing the latest information. For example, the management department automatically updates learning plans based on learners' progress and feedback. This allows learners to study anytime, anywhere through the cloud-based learning management system.

[0065] The Tools Division provides interactive learning tools. For example, the Tools Division develops interactive learning tools such as quizzes, simulations, and interactive content. The Tools Division designs interactive learning tools to allow learners to learn through hands-on experience. For example, the Tools Division allows learners to confirm their learning by answering quizzes. The Tools Division uses simulations to enable learners to learn by experiencing real-world situations. For example, the Tools Division provides simulation tools that allow learners to conduct experiments in a virtual environment. The Tools Division uses interactive content to enable learners to learn interactively. For example, the Tools Division provides interactive content that allows learners to learn by interacting with a virtual teacher. This enhances the learner's learning experience through interactive learning tools.

[0066] The adjustment unit can adjust the curriculum based on the learner's progress. For example, the adjustment unit can analyze the learner's test results and assignment submission status and adjust the curriculum content. The adjustment unit can use AI to analyze learner data and adjust the curriculum according to the learner's progress. For example, the adjustment unit can focus on areas where the learner struggles and incorporate them into the learning plan. The adjustment unit can leverage the learner's strengths to provide a curriculum that includes applied content. The adjustment unit can adjust the learning pace according to the learner's progress. For example, if the learner is falling behind, the adjustment unit can slow down the learning pace. By adjusting the curriculum according to the learner's progress, learning efficiency is improved. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input learner progress data into a generating AI and have the generating AI perform the curriculum adjustment.

[0067] The planning unit can estimate the learner's emotions and adjust the content of the learning plan based on the estimated emotions. For example, if the learner is stressed, the planning unit can make the learning plan easier and include relaxing content. If the learner is excited, the planning unit can increase their motivation by including challenging tasks. If the learner is tired, the planning unit can provide a learning plan that includes breaks. In this way, the burden on the learner is reduced by adjusting the content 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 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 planning unit may be performed using AI or not. For example, the planning unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the learning plan.

[0068] The planning unit can analyze a learner's past learning history and generate an optimal learning plan. For example, the planning unit can focus on areas the learner has struggled with in the past and incorporate more advanced content that leverages the learner's strengths. The planning unit can provide a manageable learning plan based on the learner's past learning pace. In this way, by analyzing past learning history, the planning unit can provide the learner with the most suitable learning plan. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input the learner's past learning history data into a generating AI and have the generating AI generate an optimal learning plan.

[0069] The planning unit can customize learning plans based on the learner's current skill level and goals when generating them. For example, the planning unit can assess the learner's current skill level and provide a step-by-step learning plan from basic to advanced levels. The planning unit can create learning plans to strengthen specific skills in line with the learner's goals. The planning unit can include tasks of appropriate difficulty depending on the learner's skill level. This improves learning effectiveness by providing learning plans tailored to the learner's skill level and goals. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input learner skill level data into a generating AI and have the generating AI customize the learning plan.

[0070] The planning unit can estimate the learner's emotions and determine the priority of the learning plan based on the estimated emotions. For example, if the learner is stressed, the planning unit can prioritize incorporating relaxing content into the learning plan. If the learner is excited, the planning unit can prioritize providing challenging tasks. If the learner is tired, the planning unit can prioritize including content that includes breaks. This reduces the burden on the learner by prioritizing the learning plan according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 planning unit may be performed using AI or not. For example, the planning unit can input learner emotion data into a generative AI and have the generative AI determine the priority of the learning plan.

[0071] The planning unit can prioritize the inclusion of highly relevant content when generating a learning plan, taking into account the learner's geographical location. For example, if a learner lives in a specific region, the planning unit can include examples related to that region in the learning plan. If a learner is traveling, the planning unit can prioritize providing content related to their travel destination. If a learner is interested in the culture or history of a particular region, the planning unit can include learning content related to that region. This allows the planning unit to provide highly relevant learning content by considering the learner's geographical location. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input the learner's geographical location data into a generating AI and have the generating AI select highly relevant content.

[0072] The planning unit can analyze the learner's social media activity and include relevant content when generating a learning plan. For example, the planning unit can incorporate topics the learner has shown interest in on social media into the learning plan. The planning unit can create a learning plan by referencing posts from experts and influencers the learner follows. The planning unit can reflect topics from online communities the learner participates in into the learning plan. This allows the planning unit to provide relevant learning content by analyzing the learner's social media activity. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input the learner's social media activity data into a generating AI and have the generating AI select relevant content.

[0073] The feedback unit can estimate the learner's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the learner is stressed, the feedback unit can provide feedback in gentle language. If the learner is excited, the feedback unit can provide feedback that includes many words of encouragement. If the learner is tired, the feedback unit can provide concise and easy-to-understand feedback. By adjusting the way feedback is expressed according to the learner's emotions, the learner's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner emotion data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0074] The feedback unit can adjust the level of detail of the feedback based on the learner's level of understanding when providing feedback. For example, if the learner's level of understanding is low, the feedback unit can provide feedback that includes detailed explanations. If the learner's level of understanding is high, the feedback unit can provide concise feedback. The feedback unit can also provide additional reference materials depending on the learner's level of understanding. By adjusting the level of detail of the feedback according to the learner's level of understanding, the learner's understanding is deepened. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0075] The feedback unit can apply different feedback algorithms depending on the learner's learning style when providing feedback. For example, the feedback unit can provide feedback that makes extensive use of diagrams and graphs to learners with a visual learning style. The feedback unit can provide audio feedback to learners with an auditory learning style. The feedback unit can provide feedback that includes practical advice to learners with an experiential learning style. This improves learning effectiveness by providing feedback tailored to the learner's learning style. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner learning style data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0076] The feedback unit can estimate the learner's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the learner is stressed, the feedback unit can delay providing feedback. If the learner is excited, the feedback unit can provide feedback immediately. If the learner is tired, the feedback unit can provide feedback after a break. By adjusting the timing of feedback according to the learner's emotions, the learner's understanding is deepened. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner emotion data into a generative AI and have the generative AI adjust the timing of feedback.

[0077] The feedback unit can prioritize feedback based on the learner's submission timing when providing feedback. For example, the feedback unit can prioritize feedback for learners whose submission deadlines are approaching. The feedback unit can provide detailed feedback to learners who submit early. The feedback unit can provide prompt feedback to learners who submit late. This enables efficient feedback by prioritizing feedback according to the learner's submission timing. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner submission timing data into a generating AI and have the generating AI determine the priority of feedback.

[0078] The optimization unit can estimate the learner's emotions and adjust the optimization method of the educational program based on the estimated learner's emotions. For example, if the learner is feeling stressed, the optimization unit can provide an educational program that includes relaxing content. If the learner is excited, the optimization unit can provide an educational program that includes challenging tasks. If the learner is tired, the optimization unit can provide an educational program that includes breaks. In this way, the burden on the learner is reduced by adjusting the optimization method of the educational program according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not using AI. For example, the optimization unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the optimization method of the educational program.

[0079] The optimization unit can optimize the optimization algorithm by referring to past training data during the optimization process. For example, the optimization unit can apply an algorithm that focuses on strengthening the learner's weak areas based on past training data. The optimization unit can apply an algorithm that provides advanced content that leverages the learner's strengths based on past training data. The optimization unit can apply an optimization algorithm that matches the learner's learning pace based on past training data. As a result, the accuracy of the optimization algorithm is improved by referring to past training data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past training data into a generating AI and have the generating AI perform the optimization of the optimization algorithm.

[0080] The optimization unit can customize the educational program based on the learner's current skill level during optimization. For example, the optimization unit can evaluate the learner's current skill level and provide a step-by-step educational program from basic to advanced levels. The optimization unit can provide an educational program that includes tasks of appropriate difficulty according to the learner's skill level. The optimization unit can provide an educational program to strengthen specific skills based on the learner's skill level. This improves learning effectiveness by providing an educational program tailored to the learner's skill level. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input learner skill level data into a generating AI and have the generating AI perform the customization of the educational program.

[0081] The management unit can estimate the learner's emotions and adjust the display method of the learning management system based on the estimated learner's emotions. For example, if the learner is stressed, the management unit can provide an interface with calming colors. If the learner is excited, the management unit can provide an interface with bright colors. If the learner is tired, the management unit can provide a simple and highly visible interface. This reduces the burden on learners by adjusting the display method of the learning management system according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 management unit may be performed using AI or not. For example, the management unit can input learner emotion data into a generative AI and have the generative AI adjust the display method of the learning management system.

[0082] The management unit can select the optimal display method when displaying the learning management system by referring to the learner's past operation history. For example, the management unit can prioritize displaying interface designs that the learner has preferred to use in the past. The management unit can place functions that the learner has frequently accessed in the past in a prominent position. The management unit can analyze the learner's operation history and propose the most efficient display method. In this way, the optimal display method can be provided by referring to the learner's past operation history. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the learner's operation history data into a generating AI and have the generating AI select the optimal display method.

[0083] The management unit can estimate the learner's emotions and adjust the operating procedures of the learning management system based on the estimated emotions. For example, if the learner is stressed, the management unit can simplify the operating procedures. If the learner is excited, the management unit can provide detailed operating procedures. If the learner is tired, the management unit can minimize the operating procedures. This reduces the burden on learners by adjusting the operating procedures of the learning management system according to their 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 management unit may be performed using AI or not. For example, the management unit can input learner emotion data into a generative AI and have the generative AI adjust the operating procedures of the learning management system.

[0084] The management unit can select the optimal display method when displaying the learning management system, taking into account the learner's device information. For example, if the learner is using a smartphone, the management unit can provide a display method that matches the screen size. If the learner is using a tablet, the management unit can provide a display method optimized for a larger screen. If the learner is using a desktop, the management unit can provide a display method that efficiently arranges multiple windows. In this way, the optimal display method can be provided by taking into account the learner's device information. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input learner device information data into a generating AI and have the generating AI select the optimal display method.

[0085] The tool unit can estimate the learner's emotions and adjust the operation of interactive learning tools based on the estimated emotions. For example, if the learner is stressed, the tool unit can simplify the operation. If the learner is excited, the tool unit can provide detailed operation instructions. If the learner is tired, the tool unit can minimize the operation instructions. This reduces the burden on the learner by adjusting the operation of interactive learning tools 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 tool unit may be performed using AI or not. For example, the tool unit can input learner emotion data into a generative AI and have the generative AI adjust the operation of the interactive learning tool.

[0086] The tool unit can select the optimal operation method by referring to the learner's past operation history when operating an interactive learning tool. For example, the tool unit can prioritize providing operation methods that the learner has preferred to use in the past. The tool unit can place functions that the learner has frequently used in the past in a prominent position. The tool unit can analyze the learner's operation history and suggest the most efficient operation method. In this way, it can provide the optimal operation method by referring to the learner's past operation history. Some or all of the above processing in the tool unit may be performed using AI or not. For example, the tool unit can input the learner's operation history data into a generating AI and have the generating AI perform the selection of the optimal operation method.

[0087] The tool unit can estimate the learner's emotions and adjust the operation steps of the interactive learning tool based on the estimated emotions. For example, if the learner is stressed, the tool unit can simplify the operation steps. If the learner is excited, the tool unit can provide detailed operation steps. If the learner is tired, the tool unit can minimize the operation steps. This reduces the burden on the learner by adjusting the operation steps of the interactive learning tool according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tool unit may be performed using AI or not. For example, the tool unit can input learner emotion data into a generative AI and have the generative AI adjust the operation steps of the interactive learning tool.

[0088] The tool unit can select the optimal operation method when interacting with interactive learning tools, taking into account the learner's device information. For example, if the learner is using a smartphone, the tool unit can provide an operation method adapted to the screen size. If the learner is using a tablet, the tool unit can provide an operation method optimized for a larger screen. If the learner is using a desktop, the tool unit can provide a method for efficiently operating multiple windows. In this way, the tool unit can provide the optimal operation method by taking into account the learner's device information. Some or all of the above processing in the tool unit may be performed using AI or not. For example, the tool unit can input learner device information data into a generating AI and have the generating AI select the optimal operation method.

[0089] The adjustment unit can estimate the learner's emotions and adjust the curriculum adjustment method based on the estimated learner's emotions. For example, if the learner is stressed, the adjustment unit can provide a curriculum that includes relaxing content. If the learner is excited, the adjustment unit can provide a curriculum that includes challenging tasks. If the learner is tired, the adjustment unit can provide a curriculum that includes breaks. In this way, the burden on the learner is reduced by adjusting the curriculum adjustment method according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input learner emotion data into a generative AI and have the generative AI perform adjustments to the curriculum adjustment method.

[0090] The adjustment unit can select the optimal adjustment method when adjusting the curriculum by referring to the learner's past learning history. For example, the adjustment unit can focus on incorporating areas that the learner has struggled with in the past into the curriculum. The adjustment unit can provide a curriculum that includes applied content by leveraging the learner's strengths. The adjustment unit can provide a curriculum that is manageable by referring to the learner's past learning pace. In this way, the optimal curriculum can be provided by referring to the learner's past learning history. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the learner's past learning history data into a generating AI and have the generating AI select the optimal adjustment method.

[0091] The adjustment unit can estimate the learner's emotions and determine curriculum priorities based on the estimated emotions. For example, if the learner is stressed, the adjustment unit can prioritize relaxing content in the curriculum. If the learner is excited, the adjustment unit can prioritize providing challenging tasks. If the learner is tired, the adjustment unit can prioritize including content that includes breaks. This reduces the learner's burden by prioritizing the curriculum according to their 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 adjustment unit may be performed using AI or not. For example, the adjustment unit can input learner emotion data into a generative AI and have the generative AI determine the curriculum priorities.

[0092] The adjustment unit can select the optimal adjustment method when adjusting the curriculum, taking into account the learner's geographical location information. For example, if a learner lives in a specific region, the adjustment unit can provide a curriculum that includes examples related to that region. If a learner is traveling, the adjustment unit can provide a curriculum that includes content related to their travel destination. If a learner is interested in the culture or history of a specific region, the adjustment unit can provide a curriculum related to that region. In this way, the optimal curriculum can be provided by taking into account the learner's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the learner's geographical location information data into a generating AI and have the generating AI select the optimal adjustment method.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The planning department can monitor learners' health status and adjust learning plans based on that status. For example, if a learner is feeling unwell, the planning department can provide a plan that reduces the learning load and includes more breaks. If a learner is healthy, the planning department can provide a normal learning plan. If a learner is tired after exercise, the planning department can provide a plan that includes relaxing content. By adjusting learning plans according to the learner's health status, learning efficiency is improved.

[0095] The feedback unit can monitor the learner's learning environment and adjust the content of the feedback based on that environment. For example, if the learner is learning in a quiet environment, the feedback unit can provide detailed feedback. If the learner is learning in a noisy environment, the feedback unit can provide concise and to-the-point feedback. If the learner is learning while on the go, the feedback unit can provide audio feedback. By adjusting the content of the feedback according to the learner's learning environment, the learning effect is improved.

[0096] The optimization unit can analyze learners' learning history and optimize educational programs based on their long-term goals. For example, if a learner is aiming to obtain a specific qualification, the optimization unit can provide an educational program that focuses on content related to that qualification. If a learner is aiming for career advancement, the optimization unit can provide an educational program that strengthens the necessary skills. If a learner is learning as a hobby, the optimization unit can provide an educational program that includes content that is interesting to them. By optimizing educational programs according to learners' long-term goals, learners' motivation is improved.

[0097] The management unit can monitor the battery level of the learner's device and adjust the display method of the learning management system based on the battery level. For example, when the battery level is low, the management unit can provide a simple, low-power display method. When the battery level is sufficient, the management unit can provide a normal display method. When the battery level is moderate, the management unit can provide a balanced display method. This improves the learner's convenience by adjusting the display method according to the battery level of the learner's device.

[0098] The tool unit can monitor the learner's progress and adjust the content of the interactive learning tools based on that progress. For example, if a learner is falling behind, the tool unit can focus on providing foundational content. If a learner is progressing well, the tool unit can provide more advanced content. If a learner is progressing quickly, the tool unit can provide more challenging content. By adjusting the content of the interactive learning tools according to the learner's progress, learning effectiveness is improved.

[0099] The planning department can estimate the learner's emotions and adjust the content of the learning plan based on those estimates. For example, if the learner is feeling stressed, the planning department can ease the learning plan and include relaxing content. If the learner is excited, the planning department can include challenging tasks to increase their motivation. If the learner is tired, the planning department can provide a learning plan that includes breaks. In this way, the burden on the learner is reduced by adjusting the content of the learning plan according to their emotions.

[0100] The feedback unit can estimate the learner's emotions and adjust the way feedback is presented based on those estimates. For example, if the learner is stressed, the feedback unit can provide feedback in gentle language. If the learner is excited, the feedback unit can provide feedback that includes many words of encouragement. If the learner is tired, the feedback unit can provide concise and easy-to-understand feedback. By adjusting the way feedback is presented according to the learner's emotions, learner understanding is deepened.

[0101] The optimization unit can estimate the learner's emotions and adjust the optimization method of the educational program based on the estimated learner's emotions. For example, if the optimization unit is stressed, it can provide an educational program that includes relaxing content. If the learner is excited, it can provide an educational program that includes challenging tasks. If the learner is tired, it can provide an educational program that includes breaks. In this way, the burden on the learner is reduced by adjusting the optimization method of the educational program according to the learner's emotions.

[0102] The management unit can estimate the learner's emotions and adjust the display method of the learning management system based on the estimated learner's emotions. For example, if the management unit is stressed, it can provide an interface with calming colors. If the learner is excited, it can provide an interface with bright colors. If the learner is tired, it can provide a simple and highly visible interface. In this way, the burden on learners is reduced by adjusting the display method of the learning management system according to the learner's emotions.

[0103] The tool can estimate the learner's emotions and adjust how interactive learning tools are used based on those emotions. For example, if the learner is stressed, the tool can simplify the operation. If the learner is excited, it can provide more detailed instructions. If the learner is tired, it can minimize the operation. This reduces the burden on learners by adjusting how interactive learning tools are used according to their emotions.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The planning department provides personalized learning plans based on individual learning styles and needs. The planning department analyzes the learner's learning history and learning style to generate an optimal learning plan. Using AI, it analyzes the learner's data and customizes it based on individual learning goals. For example, it can provide plans tailored to learning styles such as visual learning, auditory learning, and experiential learning, and can provide plans that take into account learning goals, learning pace, and acquisition of specific skills. Step 2: The Feedback Department provides real-time feedback based on learning progress, using the learning plan provided by the Planning Department. The Feedback Department analyzes learners' test results and assignment submission status in real time and provides appropriate feedback. Using AI, it analyzes learner data and provides real-time comments, scoring, and advice. For example, it can provide detailed explanations and additional reference materials depending on the learner's level of understanding. Step 3: The optimization unit analyzes the learning data based on the feedback provided by the feedback unit and optimizes the educational program. The optimization unit automatically updates the content of the educational program based on the learner's progress and feedback. It uses AI to analyze the learning data and optimizes the educational program using algorithms and data analysis methods. For example, it can adjust the content of the educational program according to the learner's needs and progress.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the planning unit, feedback unit, optimization unit, management unit, tool unit, and adjustment unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart device 14, which analyzes the learner's learning history and learning style and generates an optimal learning plan. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes learning data and optimizes the educational program. The management unit provides, for example, a cloud-based learning management system, which stores learning data and allows learners to access it from different devices. The tool unit is implemented, for example, by the control unit 46A of the smart device 14, which provides interactive learning tools. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which adjusts the curriculum based on the learner's progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the planning unit, feedback unit, optimization unit, management unit, tool unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the learner's learning history and learning style and generates an optimal learning plan. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes learning data and optimizes the educational program. The management unit provides, for example, a cloud-based learning management system, which stores learning data and allows learners to access it from different devices. The tool unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides interactive learning tools. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which adjusts the curriculum based on the learner's progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the planning unit, feedback unit, optimization unit, management unit, tool unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the learner's learning history and learning style and generates an optimal learning plan. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes learning data and optimizes the educational program. The management unit provides, for example, a cloud-based learning management system, which stores learning data and allows learners to access it from different devices. The tool unit is implemented by the control unit 46A of the headset terminal 314, which provides interactive learning tools. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the curriculum based on the learner's progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the planning unit, feedback unit, optimization unit, management unit, tool unit, and adjustment unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the planning unit is implemented by the control unit 46A of the robot 414, which analyzes the learner's learning history and learning style and generates an optimal learning plan. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the learner's test results and assignment submission status in real time and provides appropriate feedback. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes learning data and optimizes the educational program. The management unit provides, for example, a cloud-based learning management system, which stores learning data and allows learners to access it from different devices. The tool unit is implemented, for example, by the control unit 46A of the robot 414, which provides interactive learning tools. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which adjusts the curriculum based on the learner's progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) The planning department provides personalized learning plans based on individual learning styles and needs, A feedback unit provides immediate feedback based on learning progress, based on the learning plan provided by the planning unit. The system includes an optimization unit that analyzes learning data based on feedback provided by the feedback unit and optimizes the educational program. A system characterized by the following features. (Note 2) It has an administration department that provides a cloud-based learning management system. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a tool section that provides interactive learning tools. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an adjustment unit that adjusts the curriculum based on the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned planning department, The system estimates the learner's emotions and adjusts the content of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, Analyze the learner's past learning history and generate an optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned planning department, When generating a learning plan, customize it based on the learner's current skill level and goals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned planning department, The system estimates learners' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned planning department, When generating a learning plan, the system prioritizes including highly relevant content, taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned planning department, When generating a learning plan, analyze the learner's social media activity and include relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the learner's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on when the learner submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, It estimates learners' emotions and adjusts how the educational program is optimized based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, During optimization, the optimization algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, During optimization, the educational program is customized based on the learner's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, The system estimates learners' emotions and adjusts the display method of the learning management system based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, When displaying information in the learning management system, the system selects the optimal display method by referring to the learner's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, The system estimates the learner's emotions and adjusts the operating procedures of the learning management system based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When displaying information in the learning management system, the optimal display method is selected considering the learner's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tool section is It estimates the learner's emotions and adjusts how they interact with interactive learning tools based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tool section is When using interactive learning tools, the system selects the optimal method of operation by referring to the learner's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tool section is It estimates the learner's emotions and adjusts the instructions for using interactive learning tools based on the estimated learner emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tool section is When using interactive learning tools, the system selects the optimal method of operation by considering the learner's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, We estimate learners' emotions and adjust the curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting the curriculum, the optimal adjustment method is selected by referring to the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, The system estimates learners' emotions and prioritizes the curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, When adjusting the curriculum, the most suitable adjustment method will be selected, taking into account the learners' geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The planning department provides personalized learning plans based on individual learning styles and needs, A feedback unit provides immediate feedback based on learning progress, based on the learning plan provided by the planning unit. The system includes an optimization unit that analyzes learning data based on feedback provided by the feedback unit and optimizes the educational program. A system characterized by the following features.

2. It has an administration department that provides a cloud-based learning management system. The system according to feature 1.

3. It includes a tool section that provides interactive learning tools. The system according to feature 1.

4. It includes an adjustment unit that adjusts the curriculum based on the learner's progress. The system according to feature 1.

5. The aforementioned planning department, The system estimates the learner's emotions and adjusts the content of the learning plan based on those estimated emotions. The system according to feature 1.

6. The aforementioned planning department, Analyze the learner's past learning history and generate an optimal learning plan. The system according to feature 1.

7. The aforementioned planning department, When generating a learning plan, customize it based on the learner's current skill level and goals. The system according to feature 1.

8. The aforementioned planning department, The system estimates learners' emotions and prioritizes learning plans based on those estimated emotions. The system according to feature 1.

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