system

Abstract

The system according to this embodiment aims to provide optimal learning content and environment based on the user's interests and learning style. [Solution] The system according to the embodiment comprises a recommendation unit, an environment provision unit, a progress management unit, a collaborative learning unit, and a certification unit. The recommendation unit proposes optimal learning materials and courses based on the user's interests. The environment provision unit provides virtual experiments and simulations based on the learning materials and courses proposed by the recommendation unit. The progress management unit grasps the learning status in the learning environment provided by the environment provision unit in real time and provides appropriate feedback. The collaborative learning unit promotes collaborative learning and discussions with other learners based on the learning status obtained by the progress management unit. The certification unit issues NFTs based on the learning outcomes obtained by the progress management unit.

Description

Technical Field

[0006] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to provide optimal learning content and environment based on the user's interests and learning styles, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal learning content and environment based on the user's interests and learning styles.

Means for Solving the Problems

[0006] ]The system according to this embodiment comprises a recommendation unit, an environment provision unit, a progress management unit, a collaborative learning unit, and a certification unit. The recommendation unit proposes optimal learning materials and courses based on the user's interests. The environment provision unit provides virtual experiments and simulations based on the learning materials and courses proposed by the recommendation unit. The progress management unit monitors the learning status in the learning environment provided by the environment provision unit in real time and provides appropriate feedback. The collaborative learning unit facilitates collaborative learning and discussions with other learners based on the learning status obtained by the progress management unit. The certification unit issues NFTs based on the learning outcomes obtained by the progress management unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal learning content and environment based on the user's interests and learning style. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is an AI agent that personalizes the learning experience on the metaverse. This system can elicit deeper understanding and sustained motivation by providing optimal learning content and environment tailored to the user's interests and learning style. For example, the system suggests optimal learning materials and courses based on the user's interests. Next, the system provides a practical learning environment utilizing virtual experiments and simulations. Furthermore, the system grasps the learning status in real time and provides appropriate feedback. The system also facilitates collaborative learning and discussion with other learners. Finally, the system issues learning results as NFTs, allowing them to be recorded as history. The target audience is students, working adults, and educational institutions. Conventional online learning tends to be passive and has the challenge of making it difficult to maintain motivation, but this system uses an AI agent to provide an optimized learning experience for each learner, realizing an interactive and immersive learning environment. Methods of utilizing generative AI include personalized learning, interactive material generation, and natural language processing. In personalized learning, machine learning is used to analyze the user's level of understanding and interests and create an optimal learning plan. In interactive material generation, the generative AI creates learning content in real time. Natural language processing allows systems to instantly answer user questions and deepen their understanding. This enables systems to personalize the user's learning experience, fostering deeper understanding and sustained motivation.

[0029] The system according to this embodiment comprises a recommendation unit, an environment provision unit, a progress management unit, a collaborative learning unit, and a certification unit. The recommendation unit proposes optimal learning materials and courses based on the user's interests. For example, the recommendation unit selects optimal learning materials and courses based on the user's survey results and past behavioral history. The recommendation unit can also propose optimal learning materials and courses considering the user's learning goals and learning style. The environment provision unit provides virtual experiments and simulations based on the learning materials and courses proposed by the recommendation unit. For example, the environment provision unit provides a learning environment that the user can actually experience using virtual reality technology. The environment provision unit can also customize the type and content of the simulations to match the user's learning style. The progress management unit grasps the learning status in the learning environment provided by the environment provision unit in real time and provides appropriate feedback. For example, the progress management unit monitors the user's learning progress and provides feedback as needed. The progress management unit can also adjust the content and timing of the feedback according to the user's learning status. The collaborative learning unit promotes collaborative learning and discussions with other learners based on the learning status obtained by the progress management unit. The collaborative learning unit provides, for example, an environment where users can learn collaboratively. The collaborative learning unit can also adjust the method and content of discussions to suit the users' learning progress. The certification unit issues NFTs based on the learning outcomes obtained by the progress management unit. The certification unit issues NFTs of users' learning outcomes using blockchain technology, for example. The certification unit can also store the issued NFTs as the user's learning history. As a result, the system according to this embodiment can personalize the user's learning experience and elicit a deeper understanding and sustained motivation to learn.

[0030] The recommendation team suggests the most suitable learning materials and courses based on the user's interests. Specifically, it collects behavioral data such as the results of questionnaires entered by users when they register in the system, as well as past learning history, browsing history, and click history. By analyzing this data, it identifies the user's interests and preferences. For example, it analyzes the genres of learning materials the user has viewed in the past, their learning time, and their progress to understand what areas the user is interested in. Furthermore, it takes into account the user's learning goals and learning style. For example, it suggests intensive courses to users who want to acquire specific skills in a short period of time, and continuous courses to users who want to acquire a wide range of knowledge over the long term. Based on this information, the recommendation team uses an algorithm to suggest the most suitable learning materials and courses to the user. This algorithm utilizes machine learning technology to make optimal recommendations based on user data. For example, it combines methods such as collaborative filtering and content-based filtering to select the most suitable learning materials and courses for the user. This allows users to efficiently find learning materials and courses that suit them, maximizing the effectiveness of their learning.

[0031] The Environment Provisioning Department provides virtual experiments and simulations based on the teaching materials and courses proposed by the Recommendation Department. Specifically, it uses virtual reality (VR) and augmented reality (AR) technologies to create a learning environment that users can actually experience. For example, it provides a virtual laboratory for learning chemistry experiments and simulations for experiencing historical events. This allows users to gain a deeper understanding through actual experiments and experiences. The Environment Provisioning Department can also customize the type and content of simulations according to the user's learning style and progress. For example, it provides graphical simulations for users who prefer visual learning and interactive experiments for users who prefer practical learning. The Environment Provisioning Department also has a function to provide real-time feedback on challenges and problems that users face during their learning. This allows users to quickly resolve any questions or problems that arise during their learning, thereby increasing the efficiency of their learning.

[0032] The Progress Management Department monitors the learning progress in real time within the learning environment provided by the Environment Provision Department and provides appropriate feedback. Specifically, it monitors users' learning progress and evaluates their learning status and achievements. For example, it collects and analyzes data such as which learning materials users have studied and to what extent, how much time they have spent on which problems, and what results they have achieved. Based on this data, the Progress Management Department provides appropriate feedback to users. For example, it provides advice to users who are progressing well on how to move on to the next step, and suggests reviewing learning methods or providing additional support to users whose learning has stalled. The Progress Management Department can also adjust the content and timing of feedback according to the user's learning situation. For example, if a user is struggling with a particular task, it provides immediate feedback, offering hints and advice for problem solving. This allows users to always be aware of their learning status and proceed with their learning while receiving appropriate support.

[0033] The Collaborative Learning Department facilitates collaborative learning and discussions with other learners based on the learning status obtained by the Progress Management Department. Specifically, it provides an environment where users can learn together. For example, it sets up online forums and chat rooms to provide a space where users can freely exchange opinions and ask questions. It also sets up group projects and collaborative tasks to provide opportunities for users to work together to solve problems. The Collaborative Learning Department can also adjust the method and content of discussions to suit the learning status of the users. For example, when conducting a discussion on a specific theme, it selects a topic that matches the user's interest and level of understanding based on data obtained from the Progress Management Department. It also monitors the progress of discussions and supports smooth communication by assigning facilitators as needed. As a result, users can advance their learning while collaborating with other learners, and their motivation to learn can be increased through mutual stimulation.

[0034] The Certification Department issues NFTs based on learning outcomes obtained by the Progress Management Department. Specifically, it issues NFTs using blockchain technology based on the learning outcomes achieved by users and stores them as the user's learning history. For example, a user who completes a specific course will be issued an NFT as proof of completion and stored in the user's digital wallet. This allows users to prove their learning achievements in digital format and use them on other platforms and services. In addition to storing issued NFTs as the user's learning history, the Certification Department also has a function to centrally manage the qualifications and skills acquired by users. For example, if a user completes multiple courses, a certificate of completion for each will be issued as an NFT and centrally managed as the user's learning history. Furthermore, the Certification Department uses blockchain technology to ensure tamper-proofing and transparency in order to guarantee the reliability of issued NFTs. This allows users to prove their learning achievements in a reliable manner and can increase their motivation to learn.

[0035] The recommendation system can analyze a user's past learning history and select the most suitable learning materials and courses. For example, it can suggest learning materials and courses that a user should study next based on what they have learned in the past. The recommendation system can also suggest learning materials and courses that reinforce areas where the user is weak, based on their past learning history. The recommendation system can also analyze a user's past learning history and suggest learning materials and courses in new areas that the user might be interested in. In this way, the recommendation system can select the most suitable learning materials and courses by analyzing the user's past learning history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the user's past learning history data into a generating AI and have the generating AI select the most suitable learning materials and courses.

[0036] The recommendation system can filter learning materials and courses based on the user's current learning progress and goals. For example, the recommendation system can suggest learning materials and courses that the user should learn next, according to their current learning progress. The recommendation system can also suggest learning materials and courses necessary to achieve the user's learning goals. The recommendation system can also suggest an optimal learning plan, taking into account the user's learning progress and goals. This allows for the provision of a more appropriate learning plan by filtering learning materials and courses based on the user's current learning progress and goals. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's learning progress data and goal data into a generating AI and have the generating AI perform the filtering of learning materials and courses.

[0037] The recommendation system can prioritize suggesting highly relevant learning materials and courses by considering the user's geographical location when proposing materials and courses. For example, the recommendation system can suggest materials and courses related to the user's region based on the user's geographical location. The recommendation system can also suggest materials and courses related to local events and activities, considering the user's geographical location. The recommendation system can also suggest materials and courses related to local culture and history, considering the user's geographical location. In this way, by suggesting materials and courses that consider the user's geographical location, learning content related to the region can be provided. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant materials and courses.

[0038] The recommendation department can analyze a user's social media activity when suggesting educational materials and courses, and then suggest relevant materials and courses. For example, the recommendation department can analyze a user's social media activity and suggest materials and courses that the user might be interested in. The recommendation department can also suggest relevant materials and courses based on the user's interests on social media. The recommendation department can also suggest the most suitable materials and courses based on the user's social media activity history. In this way, by analyzing a user's social media activity, it is possible to suggest materials and courses that the user might be interested in. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the user's social media activity data into a generating AI and have the generating AI suggest relevant materials and courses.

[0039] The environment provider can add different interactive elements depending on the user's learning style when providing virtual experiments and simulations. For example, if the user has a visual learning style, the environment provider can add graphical interactive elements. If the user has an auditory learning style, the environment provider can also add audio guides and explanations. If the user has an experiential learning style, the environment provider can also add interactive elements that the user can actually manipulate. This allows for a more effective learning experience by adding interactive elements according to the user's learning style. Some or all of the above processing in the environment provider may be performed using AI, for example, or without AI. For example, the environment provider can input user learning style data into a generating AI and have the generating AI perform the addition of interactive elements.

[0040] The environment provider can customize the content of virtual experiments and simulations by referring to the user's past learning achievements. For example, the environment provider can customize the content to be learned next based on the user's past learning achievements. The environment provider can also customize content to reinforce areas where the user is weak, based on the user's past learning achievements. The environment provider can also customize content in new areas that the user might be interested in, by referring to the user's past learning achievements. In this way, a more effective learning experience can be provided by customizing the content by referring to the user's past learning achievements. Some or all of the above processes in the environment provider may be performed using AI, for example, or not using AI. For example, the environment provider can input the user's past learning achievement data into a generating AI and have the generating AI perform the content customization.

[0041] The environment provider can select the optimal display method when providing virtual experiments and simulations, taking into account the user's device information. For example, if the user is using a smartphone, the environment provider can provide a display method that matches the screen size. If the user is using a tablet, the environment provider can also provide a display method optimized for a larger screen. If the user is using a VR device, the environment provider can also provide an immersive display method. By selecting the optimal display method considering the user's device information, a more effective learning experience can be provided. Some or all of the above processing in the environment provider may be performed using AI, for example, or without AI. For example, the environment provider can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0042] The environment provision unit can provide highly relevant content by considering the user's geographical location when providing virtual experiments and simulations. For example, the environment provision unit can provide virtual experiments and simulations related to a region based on the user's geographical location. The environment provision unit can also provide virtual experiments and simulations related to local events and activities by considering the user's geographical location. The environment provision unit can also provide virtual experiments and simulations related to local culture and history by considering the user's geographical location. In this way, by providing highly relevant content by considering the user's geographical location, it is possible to provide learning content related to the region. Some or all of the above processing in the environment provision unit may be performed using AI, for example, or without using AI. For example, the environment provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant content.

[0043] The progress management unit can improve the accuracy of progress management by referring to the user's past learning data when monitoring learning progress. For example, the progress management unit can suggest what the user should learn next based on the user's past learning data. The progress management unit can also suggest content to reinforce areas where the user is weak, based on the user's past learning data. The progress management unit can also suggest content in new fields that the user might be interested in, by referring to the user's past learning data. In this way, by improving the accuracy of progress management by referring to the user's past learning data, a more effective learning experience can be provided. Some or all of the above processes in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input the user's past learning data into a generating AI and have the generating AI perform the improvement of progress management accuracy.

[0044] The progress management unit can apply different progress management methods depending on the user's learning goals when monitoring learning progress. For example, the progress management unit can propose progress management methods necessary to achieve the user's learning goals based on those goals. The progress management unit can also propose an optimal learning plan considering the user's learning goals. The progress management unit can also customize the progress management method according to the user's learning goals. This allows for a more effective learning experience by applying progress management methods according to the user's learning goals. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the user's learning goal data into a generating AI and have the generating AI execute the application of progress management methods.

[0045] The progress management unit can provide highly relevant feedback by considering the user's geographical location when monitoring learning progress. For example, the progress management unit can provide region-related feedback based on the user's geographical location. The progress management unit can also provide feedback related to local events and activities by considering the user's geographical location. The progress management unit can also provide feedback related to local culture and history by considering the user's geographical location. In this way, by providing highly relevant feedback by considering the user's geographical location, it is possible to provide learning content related to the region. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant feedback.

[0046] The progress management unit can analyze the user's social media activity and adjust the content of feedback when monitoring learning progress. For example, the progress management unit can analyze the user's social media activity and provide feedback that is likely to be of interest to the user. The progress management unit can also provide relevant feedback based on the user's interests on social media. The progress management unit can also provide optimal feedback based on the user's social media activity history. In this way, by analyzing the user's social media activity, it is possible to provide feedback that is likely to be of interest to the user. Some or all of the above processes in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the feedback.

[0047] The collaborative learning unit can select the optimal method of progress by referring to the user's past collaborative learning history when facilitating collaborative learning and discussions. For example, the collaborative learning unit can suggest what the user should learn next based on their past collaborative learning history. The collaborative learning unit can also suggest content to reinforce areas where the user is weak, based on their past collaborative learning history. The collaborative learning unit can also suggest content in new fields that the user might be interested in, by referring to their past collaborative learning history. In this way, the optimal method of progress can be selected by referring to the user's past collaborative learning history. Some or all of the above processes in the collaborative learning unit may be performed using AI, for example, or not using AI. For example, the collaborative learning unit can input the user's past collaborative learning history data into a generating AI and have the generating AI select the optimal method of progress.

[0048] The collaborative learning unit can apply different collaborative learning methods according to the user's learning objectives when facilitating collaborative learning and discussions. For example, the collaborative learning unit can propose collaborative learning methods necessary to achieve the user's learning objectives based on those objectives. The collaborative learning unit can also propose an optimal collaborative learning plan considering the user's learning objectives. The collaborative learning unit can also customize the collaborative learning method according to the user's learning objectives. This allows for a more effective learning experience by applying collaborative learning methods according to the user's learning objectives. Some or all of the above processes in the collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input the user's learning objective data into a generating AI and have the generating AI execute the application of collaborative learning methods.

[0049] The collaborative learning unit can provide highly relevant collaborative learning content by considering the user's geographical location when facilitating collaborative learning and discussions. For example, the collaborative learning unit can provide regionally relevant collaborative learning and discussions based on the user's geographical location. The collaborative learning unit can also provide collaborative learning and discussions related to regional events and activities by considering the user's geographical location. The collaborative learning unit can also provide collaborative learning and discussions related to regional culture and history based on the user's geographical location. In this way, by providing highly relevant collaborative learning content by considering the user's geographical location, it is possible to provide learning content related to the region. Some or all of the above processing in the collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant collaborative learning content.

[0050] The collaborative learning unit can analyze users' social media activity to adjust the content of collaborative learning activities when facilitating collaborative learning and discussions. For example, the collaborative learning unit can analyze users' social media activity and provide collaborative learning activities and discussions that are likely to interest them. The collaborative learning unit can also provide relevant collaborative learning activities and discussions based on users' interests on social media. The collaborative learning unit can also provide optimal collaborative learning activities and discussions based on users' social media activity history. In this way, by analyzing users' social media activity, it is possible to provide collaborative learning activities and discussions that are likely to interest them. Some or all of the above processing in the collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input users' social media activity data into a generating AI and have the generating AI adjust the content of collaborative learning activities.

[0051] The certification unit can customize the content of an NFT by referring to the user's past learning achievements when issuing it. For example, the certification unit can customize the content the user should learn next based on their past learning achievements. The certification unit can also customize content to reinforce areas where the user is weak, based on their past learning achievements. The certification unit can also customize content in new areas that the user might be interested in, by referring to their past learning achievements. This allows the certification unit to provide more appropriate learning outcomes by customizing the content of the NFT by referring to the user's past learning achievements. Some or all of the above processes in the certification unit may be performed using AI, for example, or not using AI. For example, the certification unit can input the user's past learning achievement data into a generating AI and have the generating AI perform the customization of the content of the NFT.

[0052] The certification unit can apply different issuance methods to NFTs depending on the user's learning objectives. For example, the certification unit can propose an issuance method necessary to achieve the user's learning objectives based on those objectives. The certification unit can also propose an optimal issuance plan considering the user's learning objectives. The certification unit can also customize the issuance method according to the user's learning objectives. This allows for the provision of more appropriate learning outcomes by applying the issuance method according to the user's learning objectives. Some or all of the above processes in the certification unit may be performed using AI, for example, or not using AI. For example, the certification unit can input the user's learning objective data into a generating AI and have the generating AI execute the application of the issuance method.

[0053] The certification department can issue NFTs that are highly relevant to the user, taking into account the user's geographical location information. For example, the certification department can issue NFTs related to a region based on the user's geographical location information. The certification department can also issue NFTs related to local events and activities, taking into account the user's geographical location information. The certification department can also issue NFTs related to local culture and history, taking into account the user's geographical location information. This allows the certification department to provide learning outcomes related to the region by issuing highly relevant NFTs that take into account the user's geographical location information. Some or all of the above processing in the certification department may be performed using AI, for example, or not using AI. For example, the certification department can input the user's geographical location information data into a generating AI and have the generating AI issue highly relevant NFTs.

[0054] The Certification Department can analyze a user's social media activity and adjust the content of an NFT when issuing it. For example, the Certification Department can analyze a user's social media activity and issue NFTs that are likely to be of interest to them. The Certification Department can also issue relevant NFTs based on a user's interests on social media. The Certification Department can also issue the most suitable NFT based on a user's social media activity history. This allows the Certification Department to issue NFTs that are likely to be of interest by analyzing a user's social media activity. Some or all of the above processes in the Certification Department may be performed using AI, for example, or not using AI. For example, the Certification Department can input user social media activity data into a generating AI and have the generating AI adjust the content of the NFTs.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The recommendation system can analyze a user's past learning history and select the most suitable learning materials and courses. For example, it can suggest learning materials and courses that a user should study next based on what they have learned in the past. It can also suggest learning materials and courses that reinforce areas where the user struggles, based on their past learning history. Furthermore, it can analyze the user's past learning history and suggest learning materials and courses in new fields that might interest them. In this way, the system can select the most suitable learning materials and courses by analyzing the user's past learning history.

[0057] The environment provider can add different interactive elements to virtual experiments and simulations depending on the user's learning style. For example, if the user has a visual learning style, graphical interactive elements can be added. If the user has an auditory learning style, audio guides and explanations can be added. Furthermore, if the user has an experiential learning style, interactive elements that they can actually manipulate can be added. By adding interactive elements according to the user's learning style, a more effective learning experience can be provided.

[0058] The progress management unit can improve the accuracy of progress management by referring to the user's past learning data when monitoring learning progress. For example, it can suggest what the user should learn next based on their past learning data. It can also suggest content to reinforce areas where the user is weak. Furthermore, it can suggest new areas that the user might be interested in by referring to their past learning data. In this way, by improving the accuracy of progress management by referring to the user's past learning data, a more effective learning experience can be provided.

[0059] The collaborative learning section can select the optimal method for facilitating collaborative learning and discussions by referring to the user's past collaborative learning history. For example, it can suggest what the user should learn next based on their past collaborative learning history. It can also suggest content to reinforce areas where the user is weak, based on their past collaborative learning history. Furthermore, it can suggest new topics that the user might be interested in by referring to their past collaborative learning history. In this way, the optimal method for facilitating collaborative learning can be selected by referring to the user's past collaborative learning history.

[0060] The certification department can customize the content of NFTs when issuing them by referencing the user's past learning achievements. For example, it can customize the content the user should learn next based on their past learning achievements. It can also customize content to reinforce areas where the user struggles, based on their past learning achievements. Furthermore, it can customize content in new areas that the user might be interested in, by referencing their past learning achievements. In this way, by customizing the content of NFTs based on the user's past learning achievements, it is possible to provide more appropriate learning outcomes.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The recommendation team suggests the most suitable learning materials and courses based on the user's interests. For example, they select the most suitable materials and courses based on the user's survey results and past activity history. Furthermore, they can also suggest the most suitable materials and courses considering the user's learning goals and learning style. Step 2: The Environment Provision Department provides virtual experiments and simulations based on the teaching materials and courses proposed by the Recommendation Department. For example, they use virtual reality technology to provide a learning environment that users can actually experience. The type and content of the simulations can also be customized to suit the user's learning style. Step 3: The progress management unit monitors the learning status in the learning environment provided by the environment provision unit in real time and provides appropriate feedback. For example, it monitors the user's learning progress and provides feedback as needed. The content and timing of the feedback can also be adjusted according to the user's learning status. Step 4: The Collaborative Learning Department facilitates collaborative learning and discussions with other learners based on the learning status obtained by the Progress Management Department. For example, it provides an environment where users can learn together. It can also adjust the method and content of discussions to suit the users' learning progress. Step 5: The certification department issues NFTs based on the learning outcomes obtained by the progress management department. For example, the user's learning outcomes are issued as NFTs using blockchain technology. The issued NFTs can also be stored as the user's learning history.

[0063] (Example of form 2) The system according to an embodiment of the present invention is an AI agent that personalizes the learning experience on the metaverse. This system can elicit deeper understanding and sustained motivation by providing optimal learning content and environment tailored to the user's interests and learning style. For example, the system suggests optimal learning materials and courses based on the user's interests. Next, the system provides a practical learning environment utilizing virtual experiments and simulations. Furthermore, the system grasps the learning status in real time and provides appropriate feedback. The system also facilitates collaborative learning and discussion with other learners. Finally, the system issues learning results as NFTs, allowing them to be recorded as history. The target audience is students, working adults, and educational institutions. Conventional online learning tends to be passive and has the challenge of making it difficult to maintain motivation, but this system uses an AI agent to provide an optimized learning experience for each learner, realizing an interactive and immersive learning environment. Methods of utilizing generative AI include personalized learning, interactive material generation, and natural language processing. In personalized learning, machine learning is used to analyze the user's level of understanding and interests and create an optimal learning plan. In interactive material generation, the generative AI creates learning content in real time. Natural language processing allows systems to instantly answer user questions and deepen their understanding. This enables systems to personalize the user's learning experience, fostering deeper understanding and sustained motivation.

[0064] The system according to this embodiment comprises a recommendation unit, an environment provision unit, a progress management unit, a collaborative learning unit, and a certification unit. The recommendation unit proposes optimal learning materials and courses based on the user's interests. For example, the recommendation unit selects optimal learning materials and courses based on the user's survey results and past behavioral history. The recommendation unit can also propose optimal learning materials and courses considering the user's learning goals and learning style. The environment provision unit provides virtual experiments and simulations based on the learning materials and courses proposed by the recommendation unit. For example, the environment provision unit provides a learning environment that the user can actually experience using virtual reality technology. The environment provision unit can also customize the type and content of the simulations to match the user's learning style. The progress management unit grasps the learning status in the learning environment provided by the environment provision unit in real time and provides appropriate feedback. For example, the progress management unit monitors the user's learning progress and provides feedback as needed. The progress management unit can also adjust the content and timing of the feedback according to the user's learning status. The collaborative learning unit promotes collaborative learning and discussions with other learners based on the learning status obtained by the progress management unit. The collaborative learning unit provides, for example, an environment where users can learn collaboratively. The collaborative learning unit can also adjust the method and content of discussions to suit the users' learning progress. The certification unit issues NFTs based on the learning outcomes obtained by the progress management unit. The certification unit issues NFTs of users' learning outcomes using blockchain technology, for example. The certification unit can also store the issued NFTs as the user's learning history. As a result, the system according to this embodiment can personalize the user's learning experience and elicit a deeper understanding and sustained motivation to learn.

[0065] The recommendation team suggests the most suitable learning materials and courses based on the user's interests. Specifically, it collects behavioral data such as the results of questionnaires entered by users when they register in the system, as well as past learning history, browsing history, and click history. By analyzing this data, it identifies the user's interests and preferences. For example, it analyzes the genres of learning materials the user has viewed in the past, their learning time, and their progress to understand what areas the user is interested in. Furthermore, it takes into account the user's learning goals and learning style. For example, it suggests intensive courses to users who want to acquire specific skills in a short period of time, and continuous courses to users who want to acquire a wide range of knowledge over the long term. Based on this information, the recommendation team uses an algorithm to suggest the most suitable learning materials and courses to the user. This algorithm utilizes machine learning technology to make optimal recommendations based on user data. For example, it combines methods such as collaborative filtering and content-based filtering to select the most suitable learning materials and courses for the user. This allows users to efficiently find learning materials and courses that suit them, maximizing the effectiveness of their learning.

[0066] The Environment Provisioning Department provides virtual experiments and simulations based on the teaching materials and courses proposed by the Recommendation Department. Specifically, it uses virtual reality (VR) and augmented reality (AR) technologies to create a learning environment that users can actually experience. For example, it provides a virtual laboratory for learning chemistry experiments and simulations for experiencing historical events. This allows users to gain a deeper understanding through actual experiments and experiences. The Environment Provisioning Department can also customize the type and content of simulations according to the user's learning style and progress. For example, it provides graphical simulations for users who prefer visual learning and interactive experiments for users who prefer practical learning. The Environment Provisioning Department also has a function to provide real-time feedback on challenges and problems that users face during their learning. This allows users to quickly resolve any questions or problems that arise during their learning, thereby increasing the efficiency of their learning.

[0067] The Progress Management Department monitors the learning progress in real time within the learning environment provided by the Environment Provision Department and provides appropriate feedback. Specifically, it monitors users' learning progress and evaluates their learning status and achievements. For example, it collects and analyzes data such as which learning materials users have studied and to what extent, how much time they have spent on which problems, and what results they have achieved. Based on this data, the Progress Management Department provides appropriate feedback to users. For example, it provides advice to users who are progressing well on how to move on to the next step, and suggests reviewing learning methods or providing additional support to users whose learning has stalled. The Progress Management Department can also adjust the content and timing of feedback according to the user's learning situation. For example, if a user is struggling with a particular task, it provides immediate feedback, offering hints and advice for problem solving. This allows users to always be aware of their learning status and proceed with their learning while receiving appropriate support.

[0068] The Collaborative Learning Department facilitates collaborative learning and discussions with other learners based on the learning status obtained by the Progress Management Department. Specifically, it provides an environment where users can learn together. For example, it sets up online forums and chat rooms to provide a space where users can freely exchange opinions and ask questions. It also sets up group projects and collaborative tasks to provide opportunities for users to work together to solve problems. The Collaborative Learning Department can also adjust the method and content of discussions to suit the learning status of the users. For example, when conducting a discussion on a specific theme, it selects a topic that matches the user's interest and level of understanding based on data obtained from the Progress Management Department. It also monitors the progress of discussions and supports smooth communication by assigning facilitators as needed. As a result, users can advance their learning while collaborating with other learners, and their motivation to learn can be increased through mutual stimulation.

[0069] The Certification Department issues NFTs based on learning outcomes obtained by the Progress Management Department. Specifically, it issues NFTs using blockchain technology based on the learning outcomes achieved by users and stores them as the user's learning history. For example, a user who completes a specific course will be issued an NFT as proof of completion and stored in the user's digital wallet. This allows users to prove their learning achievements in digital format and use them on other platforms and services. In addition to storing issued NFTs as the user's learning history, the Certification Department also has a function to centrally manage the qualifications and skills acquired by users. For example, if a user completes multiple courses, a certificate of completion for each will be issued as an NFT and centrally managed as the user's learning history. Furthermore, the Certification Department uses blockchain technology to ensure tamper-proofing and transparency in order to guarantee the reliability of issued NFTs. This allows users to prove their learning achievements in a reliable manner and can increase their motivation to learn.

[0070] The recommendation system can estimate the user's emotions and adjust the suggested learning materials and courses based on those emotions. For example, if the user is stressed, the recommendation system can suggest relaxing learning materials and courses. If the user is excited, the recommendation system can also suggest challenging learning materials and courses. If the user is tired, the recommendation system can also suggest learning materials and courses that can be learned in a short amount of time. By adjusting the suggested learning materials and courses based on the user's emotions, a more appropriate learning experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI, or not using AI. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the suggested learning materials and courses based on those emotions.

[0071] The recommendation system can analyze a user's past learning history and select the most suitable learning materials and courses. For example, it can suggest learning materials and courses that a user should study next based on what they have learned in the past. The recommendation system can also suggest learning materials and courses that reinforce areas where the user is weak, based on their past learning history. The recommendation system can also analyze a user's past learning history and suggest learning materials and courses in new areas that the user might be interested in. In this way, the recommendation system can select the most suitable learning materials and courses by analyzing the user's past learning history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the user's past learning history data into a generating AI and have the generating AI select the most suitable learning materials and courses.

[0072] The recommendation system can filter learning materials and courses based on the user's current learning progress and goals. For example, the recommendation system can suggest learning materials and courses that the user should learn next, according to their current learning progress. The recommendation system can also suggest learning materials and courses necessary to achieve the user's learning goals. The recommendation system can also suggest an optimal learning plan, taking into account the user's learning progress and goals. This allows for the provision of a more appropriate learning plan by filtering learning materials and courses based on the user's current learning progress and goals. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's learning progress data and goal data into a generating AI and have the generating AI perform the filtering of learning materials and courses.

[0073] The recommendation system can estimate the user's emotions and prioritize the learning materials and courses it suggests based on those emotions. For example, if the user is stressed, the recommendation system will prioritize relaxing materials and courses. If the user is excited, the recommendation system may prioritize challenging materials and courses. If the user is tired, the recommendation system may prioritize materials and courses that can be learned in a short amount of time. This allows for a more appropriate learning experience by prioritizing materials and courses based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform the priority determination of materials and courses based on emotions.

[0074] The recommendation system can prioritize suggesting highly relevant learning materials and courses by considering the user's geographical location when proposing materials and courses. For example, the recommendation system can suggest materials and courses related to the user's region based on the user's geographical location. The recommendation system can also suggest materials and courses related to local events and activities, considering the user's geographical location. The recommendation system can also suggest materials and courses related to local culture and history, considering the user's geographical location. In this way, by suggesting materials and courses that consider the user's geographical location, learning content related to the region can be provided. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant materials and courses.

[0075] The recommendation department can analyze a user's social media activity when suggesting educational materials and courses, and then suggest relevant materials and courses. For example, the recommendation department can analyze a user's social media activity and suggest materials and courses that the user might be interested in. The recommendation department can also suggest relevant materials and courses based on the user's interests on social media. The recommendation department can also suggest the most suitable materials and courses based on the user's social media activity history. In this way, by analyzing a user's social media activity, it is possible to suggest materials and courses that the user might be interested in. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the user's social media activity data into a generating AI and have the generating AI suggest relevant materials and courses.

[0076] The environment provider can estimate the user's emotions and adjust the content of virtual experiments and simulations based on the estimated emotions. For example, if the user is stressed, the environment provider can provide relaxing virtual experiments and simulations. If the user is excited, the environment provider can also provide challenging virtual experiments and simulations. If the user is tired, the environment provider can also provide virtual experiments and simulations that can be completed in a short time. In this way, by adjusting the content of virtual experiments and simulations based on the user's emotions, a more appropriate learning experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environment provider may be performed using AI, for example, or not using AI. For example, the environment provider can input user emotion data into a generative AI and have the generative AI perform adjustments to the content of virtual experiments and simulations based on emotions.

[0077] The environment provider can add different interactive elements depending on the user's learning style when providing virtual experiments and simulations. For example, if the user has a visual learning style, the environment provider can add graphical interactive elements. If the user has an auditory learning style, the environment provider can also add audio guides and explanations. If the user has an experiential learning style, the environment provider can also add interactive elements that the user can actually manipulate. This allows for a more effective learning experience by adding interactive elements according to the user's learning style. Some or all of the above processing in the environment provider may be performed using AI, for example, or without AI. For example, the environment provider can input user learning style data into a generating AI and have the generating AI perform the addition of interactive elements.

[0078] The environment provider can customize the content of virtual experiments and simulations by referring to the user's past learning achievements. For example, the environment provider can customize the content to be learned next based on the user's past learning achievements. The environment provider can also customize content to reinforce areas where the user is weak, based on the user's past learning achievements. The environment provider can also customize content in new areas that the user might be interested in, by referring to the user's past learning achievements. In this way, a more effective learning experience can be provided by customizing the content by referring to the user's past learning achievements. Some or all of the above processes in the environment provider may be performed using AI, for example, or not using AI. For example, the environment provider can input the user's past learning achievement data into a generating AI and have the generating AI perform the content customization.

[0079] The environment provider can estimate the user's emotions and adjust the difficulty level of virtual experiments and simulations based on the estimated emotions. For example, if the user is stressed, the environment provider can provide virtual experiments or simulations with a lower difficulty level. If the user is excited, the environment provider can also provide virtual experiments or simulations with a higher difficulty level. If the user is tired, the environment provider can also provide virtual experiments or simulations with a difficulty level that can be completed in a short time. In this way, a more appropriate learning experience can be provided by adjusting the difficulty level of virtual experiments and simulations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the environment provider may be performed using AI, for example, or not using AI. For example, the environment provider can input user emotion data into a generative AI and have the generative AI perform difficulty level adjustments based on emotions.

[0080] The environment provider can select the optimal display method when providing virtual experiments and simulations, taking into account the user's device information. For example, if the user is using a smartphone, the environment provider can provide a display method that matches the screen size. If the user is using a tablet, the environment provider can also provide a display method optimized for a larger screen. If the user is using a VR device, the environment provider can also provide an immersive display method. By selecting the optimal display method considering the user's device information, a more effective learning experience can be provided. Some or all of the above processing in the environment provider may be performed using AI, for example, or without AI. For example, the environment provider can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0081] The environment provision unit can provide highly relevant content by considering the user's geographical location when providing virtual experiments and simulations. For example, the environment provision unit can provide virtual experiments and simulations related to a region based on the user's geographical location. The environment provision unit can also provide virtual experiments and simulations related to local events and activities by considering the user's geographical location. The environment provision unit can also provide virtual experiments and simulations related to local culture and history by considering the user's geographical location. In this way, by providing highly relevant content by considering the user's geographical location, it is possible to provide learning content related to the region. Some or all of the above processing in the environment provision unit may be performed using AI, for example, or without using AI. For example, the environment provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant content.

[0082] The progress management unit can estimate the user's emotions and adjust the content and timing of feedback based on the estimated emotions. For example, if the user is stressed, the progress management unit can provide relaxing feedback. If the user is excited, the progress management unit can also provide challenging feedback. If the user is tired, the progress management unit can also provide feedback that can be completed in a short time. In this way, a more appropriate learning experience can be provided by adjusting the content and timing of feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input user emotion data into a generative AI and have the generative AI adjust the content and timing of feedback based on emotions.

[0083] The progress management unit can improve the accuracy of progress management by referring to the user's past learning data when monitoring learning progress. For example, the progress management unit can suggest what the user should learn next based on the user's past learning data. The progress management unit can also suggest content to reinforce areas where the user is weak, based on the user's past learning data. The progress management unit can also suggest content in new fields that the user might be interested in, by referring to the user's past learning data. In this way, by improving the accuracy of progress management by referring to the user's past learning data, a more effective learning experience can be provided. Some or all of the above processes in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input the user's past learning data into a generating AI and have the generating AI perform the improvement of progress management accuracy.

[0084] The progress management unit can apply different progress management methods depending on the user's learning goals when monitoring learning progress. For example, the progress management unit can propose progress management methods necessary to achieve the user's learning goals based on those goals. The progress management unit can also propose an optimal learning plan considering the user's learning goals. The progress management unit can also customize the progress management method according to the user's learning goals. This allows for a more effective learning experience by applying progress management methods according to the user's learning goals. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the user's learning goal data into a generating AI and have the generating AI execute the application of progress management methods.

[0085] The progress management unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the progress management unit will prioritize providing relaxing feedback. If the user is excited, the progress management unit may also prioritize providing challenging feedback. If the user is tired, the progress management unit may also prioritize providing feedback that can be completed quickly. This allows for a more appropriate learning experience by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI or not. For example, the progress management unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based feedback priorities.

[0086] The progress management unit can provide highly relevant feedback by considering the user's geographical location when monitoring learning progress. For example, the progress management unit can provide region-related feedback based on the user's geographical location. The progress management unit can also provide feedback related to local events and activities by considering the user's geographical location. The progress management unit can also provide feedback related to local culture and history by considering the user's geographical location. In this way, by providing highly relevant feedback by considering the user's geographical location, it is possible to provide learning content related to the region. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant feedback.

[0087] The progress management unit can analyze the user's social media activity and adjust the content of feedback when monitoring learning progress. For example, the progress management unit can analyze the user's social media activity and provide feedback that is likely to be of interest to the user. The progress management unit can also provide relevant feedback based on the user's interests on social media. The progress management unit can also provide optimal feedback based on the user's social media activity history. In this way, by analyzing the user's social media activity, it is possible to provide feedback that is likely to be of interest to the user. Some or all of the above processes in the progress management unit may be performed using AI, for example, or not using AI. For example, the progress management unit can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the feedback.

[0088] The collaborative learning unit can estimate the user's emotions and adjust the way collaborative learning and discussions are conducted based on those estimated emotions. For example, if the user is feeling stressed, the collaborative learning unit can provide a relaxing approach. If the user is excited, the collaborative learning unit can also provide a challenging approach. If the user is tired, the collaborative learning unit can also provide a quick approach. This allows for a more appropriate learning experience by adjusting the way collaborative learning and discussions are conducted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the approach based on the emotion.

[0089] The collaborative learning unit can select the optimal method of progress by referring to the user's past collaborative learning history when facilitating collaborative learning and discussions. For example, the collaborative learning unit can suggest what the user should learn next based on their past collaborative learning history. The collaborative learning unit can also suggest content to reinforce areas where the user is weak, based on their past collaborative learning history. The collaborative learning unit can also suggest content in new fields that the user might be interested in, by referring to their past collaborative learning history. In this way, the optimal method of progress can be selected by referring to the user's past collaborative learning history. Some or all of the above processes in the collaborative learning unit may be performed using AI, for example, or not using AI. For example, the collaborative learning unit can input the user's past collaborative learning history data into a generating AI and have the generating AI select the optimal method of progress.

[0090] The collaborative learning unit can apply different collaborative learning methods according to the user's learning objectives when facilitating collaborative learning and discussions. For example, the collaborative learning unit can propose collaborative learning methods necessary to achieve the user's learning objectives based on those objectives. The collaborative learning unit can also propose an optimal collaborative learning plan considering the user's learning objectives. The collaborative learning unit can also customize the collaborative learning method according to the user's learning objectives. This allows for a more effective learning experience by applying collaborative learning methods according to the user's learning objectives. Some or all of the above processes in the collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input the user's learning objective data into a generating AI and have the generating AI execute the application of collaborative learning methods.

[0091] The collaborative learning unit can estimate the user's emotions and prioritize collaborative learning and discussions based on those estimated emotions. For example, if the user is stressed, the collaborative learning unit can prioritize relaxing collaborative learning and discussions. If the user is excited, the collaborative learning unit can also prioritize challenging collaborative learning and discussions. If the user is tired, the collaborative learning unit can also prioritize collaborative learning and discussions that can be completed in a short time. This allows for a more appropriate learning experience by prioritizing collaborative learning and discussions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaborative learning unit may be performed using AI or not. For example, the collaborative learning unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0092] The collaborative learning unit can provide highly relevant collaborative learning content by considering the user's geographical location when facilitating collaborative learning and discussions. For example, the collaborative learning unit can provide regionally relevant collaborative learning and discussions based on the user's geographical location. The collaborative learning unit can also provide collaborative learning and discussions related to regional events and activities by considering the user's geographical location. The collaborative learning unit can also provide collaborative learning and discussions related to regional culture and history based on the user's geographical location. In this way, by providing highly relevant collaborative learning content by considering the user's geographical location, it is possible to provide learning content related to the region. Some or all of the above processing in the collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant collaborative learning content.

[0093] The collaborative learning unit can analyze users' social media activity to adjust the content of collaborative learning activities when facilitating collaborative learning and discussions. For example, the collaborative learning unit can analyze users' social media activity and provide collaborative learning activities and discussions that are likely to interest them. The collaborative learning unit can also provide relevant collaborative learning activities and discussions based on users' interests on social media. The collaborative learning unit can also provide optimal collaborative learning activities and discussions based on users' social media activity history. In this way, by analyzing users' social media activity, it is possible to provide collaborative learning activities and discussions that are likely to interest them. Some or all of the above processing in the collaborative learning unit may be performed using AI, for example, or without AI. For example, the collaborative learning unit can input users' social media activity data into a generating AI and have the generating AI adjust the content of collaborative learning activities.

[0094] The certification unit can estimate the user's emotions and adjust the content of the NFT issued based on the estimated emotions. For example, if the user is stressed, the certification unit may issue an NFT with relaxing content. If the user is excited, the certification unit may also issue an NFT with challenging content. If the user is tired, the certification unit may also issue an NFT that can be completed in a short time. By adjusting the content of the NFT issued based on the user's emotions, more appropriate learning outcomes can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the certification unit may be performed using AI, for example, or not using AI. For example, the certification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the issued content based on emotions.

[0095] The certification unit can customize the content of an NFT by referring to the user's past learning achievements when issuing it. For example, the certification unit can customize the content the user should learn next based on their past learning achievements. The certification unit can also customize content to reinforce areas where the user is weak, based on their past learning achievements. The certification unit can also customize content in new areas that the user might be interested in, by referring to their past learning achievements. This allows the certification unit to provide more appropriate learning outcomes by customizing the content of the NFT by referring to the user's past learning achievements. Some or all of the above processes in the certification unit may be performed using AI, for example, or not using AI. For example, the certification unit can input the user's past learning achievement data into a generating AI and have the generating AI perform the customization of the content of the NFT.

[0096] The certification unit can apply different issuance methods to NFTs depending on the user's learning objectives. For example, the certification unit can propose an issuance method necessary to achieve the user's learning objectives based on those objectives. The certification unit can also propose an optimal issuance plan considering the user's learning objectives. The certification unit can also customize the issuance method according to the user's learning objectives. This allows for the provision of more appropriate learning outcomes by applying the issuance method according to the user's learning objectives. Some or all of the above processes in the certification unit may be performed using AI, for example, or not using AI. For example, the certification unit can input the user's learning objective data into a generating AI and have the generating AI execute the application of the issuance method.

[0097] The certification unit can estimate the user's emotions and determine the priority of NFT issuance based on the estimated emotions. For example, if the user is stressed, the certification unit may prioritize issuing NFTs with relaxing content. If the user is excited, the certification unit may also prioritize issuing NFTs with challenging content. If the user is tired, the certification unit may also prioritize issuing NFTs that can be completed in a short time. This allows for more appropriate learning outcomes by determining the priority of NFT issuance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 certification unit may be performed using AI or not using AI. For example, the certification unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based issuance priorities.

[0098] The certification department can issue NFTs that are highly relevant to the user, taking into account the user's geographical location information. For example, the certification department can issue NFTs related to a region based on the user's geographical location information. The certification department can also issue NFTs related to local events and activities, taking into account the user's geographical location information. The certification department can also issue NFTs related to local culture and history, taking into account the user's geographical location information. This allows the certification department to provide learning outcomes related to the region by issuing highly relevant NFTs that take into account the user's geographical location information. Some or all of the above processing in the certification department may be performed using AI, for example, or not using AI. For example, the certification department can input the user's geographical location information data into a generating AI and have the generating AI issue highly relevant NFTs.

[0099] The Certification Department can analyze a user's social media activity and adjust the content of an NFT when issuing it. For example, the Certification Department can analyze a user's social media activity and issue NFTs that are likely to be of interest to them. The Certification Department can also issue relevant NFTs based on a user's interests on social media. The Certification Department can also issue the most suitable NFT based on a user's social media activity history. This allows the Certification Department to issue NFTs that are likely to be of interest by analyzing a user's social media activity. Some or all of the above processes in the Certification Department may be performed using AI, for example, or not using AI. For example, the Certification Department can input user social media activity data into a generating AI and have the generating AI adjust the content of the NFTs.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The recommendation system can estimate the user's emotions and adjust the suggested learning materials and courses based on those estimates. For example, if a user is stressed, it can suggest relaxing learning materials and courses. If a user is excited, it can suggest challenging learning materials and courses. Furthermore, if a user is tired, it can suggest learning materials and courses that can be completed in a short amount of time. By adjusting the suggested learning materials and courses based on the user's emotions, a more appropriate learning experience can be provided.

[0102] The environment provisioning unit can estimate the user's emotions and adjust the content of virtual experiments and simulations based on those emotions. For example, if a user is feeling stressed, it can provide virtual experiments or simulations with relaxing content. If a user is excited, it can provide virtual experiments or simulations with challenging content. Furthermore, if a user is tired, it can provide virtual experiments or simulations that can be completed in a short time. In this way, by adjusting the content of virtual experiments and simulations based on the user's emotions, a more appropriate learning experience can be provided.

[0103] The progress management department can estimate the user's emotions and adjust the content and timing of feedback based on those estimates. For example, if a user is stressed, it can provide relaxing feedback. If a user is excited, it can provide challenging feedback. Furthermore, if a user is tired, it can provide feedback that can be completed quickly. By adjusting the content and timing of feedback based on the user's emotions, a more appropriate learning experience can be provided.

[0104] The collaborative learning unit can estimate the user's emotions and adjust the way collaborative learning and discussions are conducted based on those estimated emotions. For example, if a user is feeling stressed, it can provide a relaxing approach. If a user is excited, it can provide a challenging approach. Furthermore, if a user is tired, it can provide an approach that can be completed in a short amount of time. In this way, by adjusting the way collaborative learning and discussions are conducted based on the user's emotions, a more appropriate learning experience can be provided.

[0105] The certification department can estimate the user's emotions and adjust the content of the NFT issued based on those emotions. For example, if the user is stressed, it can issue an NFT with relaxing content. If the user is excited, it can issue an NFT with challenging content. Furthermore, if the user is tired, it can issue an NFT that can be completed in a short time. By adjusting the content of the NFT issued based on the user's emotions, it is possible to provide more appropriate learning outcomes.

[0106] The recommendation system can analyze a user's past learning history and select the most suitable learning materials and courses. For example, it can suggest learning materials and courses that a user should study next based on what they have learned in the past. It can also suggest learning materials and courses that reinforce areas where the user struggles, based on their past learning history. Furthermore, it can analyze the user's past learning history and suggest learning materials and courses in new fields that might interest them. In this way, the system can select the most suitable learning materials and courses by analyzing the user's past learning history.

[0107] The environment provider can add different interactive elements to virtual experiments and simulations depending on the user's learning style. For example, if the user has a visual learning style, graphical interactive elements can be added. If the user has an auditory learning style, audio guides and explanations can be added. Furthermore, if the user has an experiential learning style, interactive elements that they can actually manipulate can be added. By adding interactive elements according to the user's learning style, a more effective learning experience can be provided.

[0108] The progress management unit can improve the accuracy of progress management by referring to the user's past learning data when monitoring learning progress. For example, it can suggest what the user should learn next based on their past learning data. It can also suggest content to reinforce areas where the user is weak. Furthermore, it can suggest new areas that the user might be interested in by referring to their past learning data. In this way, by improving the accuracy of progress management by referring to the user's past learning data, a more effective learning experience can be provided.

[0109] The collaborative learning section can select the optimal method for facilitating collaborative learning and discussions by referring to the user's past collaborative learning history. For example, it can suggest what the user should learn next based on their past collaborative learning history. It can also suggest content to reinforce areas where the user is weak, based on their past collaborative learning history. Furthermore, it can suggest new topics that the user might be interested in by referring to their past collaborative learning history. In this way, the optimal method for facilitating collaborative learning can be selected by referring to the user's past collaborative learning history.

[0110] The certification department can customize the content of NFTs when issuing them by referencing the user's past learning achievements. For example, it can customize the content the user should learn next based on their past learning achievements. It can also customize content to reinforce areas where the user struggles, based on their past learning achievements. Furthermore, it can customize content in new areas that the user might be interested in, by referencing their past learning achievements. In this way, by customizing the content of NFTs based on the user's past learning achievements, it is possible to provide more appropriate learning outcomes.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The recommendation team suggests the most suitable learning materials and courses based on the user's interests. For example, they select the most suitable materials and courses based on the user's survey results and past activity history. Furthermore, they can also suggest the most suitable materials and courses considering the user's learning goals and learning style. Step 2: The Environment Provision Department provides virtual experiments and simulations based on the teaching materials and courses proposed by the Recommendation Department. For example, they use virtual reality technology to provide a learning environment that users can actually experience. The type and content of the simulations can also be customized to suit the user's learning style. Step 3: The progress management unit monitors the learning status in the learning environment provided by the environment provision unit in real time and provides appropriate feedback. For example, it monitors the user's learning progress and provides feedback as needed. The content and timing of the feedback can also be adjusted according to the user's learning status. Step 4: The Collaborative Learning Department facilitates collaborative learning and discussions with other learners based on the learning status obtained by the Progress Management Department. For example, it provides an environment where users can learn together. It can also adjust the method and content of discussions to suit the users' learning progress. Step 5: The certification department issues NFTs based on the learning outcomes obtained by the progress management department. For example, the user's learning outcomes are issued as NFTs using blockchain technology. The issued NFTs can also be stored as the user's learning history.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the recommendation unit, environment provision unit, progress management unit, collaborative learning unit, and certification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the smart device 14 and suggests optimal learning materials and courses based on the user's interests. The environment provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides virtual experiments and simulations. The progress management unit is implemented by, for example, the control unit 46A of the smart device 14 and grasps the learning status in real time and provides appropriate feedback. The collaborative learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and facilitates collaborative learning and discussion with other learners. The certification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and issues learning results as NFTs. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements mentioned above, including the recommendation unit, environment provision unit, progress management unit, collaborative learning unit, and certification unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the smart glasses 214 and suggests optimal learning materials and courses based on the user's interests. The environment provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides virtual experiments and simulations. The progress management unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time information on learning progress and appropriate feedback. The collaborative learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and facilitates collaborative learning and discussions with other learners. The certification unit is implemented by the specific processing unit 290 of the data processing unit 12 and issues learning results as NFTs. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements mentioned above, including the recommendation unit, environment provision unit, progress management unit, collaborative learning unit, and certification unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the headset terminal 314 and suggests optimal learning materials and courses based on the user's interests. The environment provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides virtual experiments and simulations. The progress management unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time monitoring of learning progress and appropriate feedback. The collaborative learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and facilitates collaborative learning and discussions with other learners. The certification unit is implemented by the specific processing unit 290 of the data processing unit 12 and issues learning results as NFTs. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements mentioned above, including the recommendation unit, environment provision unit, progress management unit, collaborative learning unit, and certification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recommendation unit is implemented by the control unit 46A of the robot 414 and suggests the most suitable learning materials and courses based on the user's interests. The environment provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides virtual experiments and simulations. The progress management unit is implemented by, for example, the control unit 46A of the robot 414 and grasps the learning status in real time and provides appropriate feedback. The collaborative learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and facilitates collaborative learning and discussion with other learners. The certification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and issues learning results as NFTs. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A recommendation department that suggests the most suitable learning materials and courses based on the user's interests, The Environment Provision Department provides virtual experiments and simulations based on the teaching materials and courses proposed by the aforementioned Recommendation Department, The progress management unit provides real-time information on the learning status in the learning environment provided by the aforementioned environment provision unit and provides appropriate feedback. Based on the learning status obtained by the aforementioned progress management unit, the collaborative learning unit promotes collaborative learning and discussions with other learners, The system includes a certification unit that issues NFTs based on the learning outcomes obtained by the aforementioned progress management unit. A system characterized by the following features. (Note 2) The aforementioned recommendation department, The system estimates the user's emotions and adjusts the suggested learning materials and courses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recommendation department, Analyze the user's past learning history to select the most suitable learning materials and courses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, When suggesting learning materials and courses, filter them based on the user's current learning progress and goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, It estimates the user's emotions and determines the priority of suggested learning materials and courses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, When suggesting teaching materials and courses, the system prioritizes suggesting highly relevant materials and courses by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recommendation department, When suggesting teaching materials and courses, we analyze users' social media activity and suggest relevant materials and courses. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned environment provision unit, It estimates the user's emotions and adjusts the content of virtual experiments and simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned environment provision unit, When providing virtual experiments and simulations, we add different interactive elements depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned environment provision unit, When providing virtual experiments and simulations, the content is customized by referencing the user's past learning achievements. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned environment provision unit, It estimates the user's emotions and adjusts the difficulty of virtual experiments and simulations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned environment provision unit, When providing virtual experiments and simulations, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned environment provision unit, When providing virtual experiments and simulations, we take the user's geographical location into consideration to deliver highly relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned progress management unit, It estimates the user's emotions and adjusts the content and timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress management unit, When monitoring learning progress, referencing the user's past learning data improves the accuracy of progress management. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress management unit, When monitoring learning progress, different progress management methods are applied according to the user's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress management unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress management unit, When assessing learning progress, provide highly relevant feedback by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress management unit, When assessing learning progress, we analyze users' social media activity to adjust the content of feedback. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collaborative learning department, It estimates the user's emotions and adjusts the collaborative learning and discussion process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collaborative learning department, When facilitating collaborative learning and discussions, the system selects the optimal method of progress by referring to the user's past collaborative learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collaborative learning department, When facilitating collaborative learning and discussions, different collaborative learning methods are applied according to the user's learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collaborative learning department, It estimates user emotions and prioritizes collaborative learning and discussions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collaborative learning department, When facilitating collaborative learning and discussions, the system takes into account the user's geographical location to provide highly relevant collaborative learning opportunities. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collaborative learning department, When facilitating collaborative learning and discussions, analyze users' social media activity to adjust the content of collaborative learning. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned certification department, The system estimates the user's emotions and adjusts the content of the NFT issuance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned certification department, When issuing an NFT, the content of the NFT is customized by referencing the user's past learning achievements. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned certification department, When issuing NFTs, different issuance methods are applied depending on the user's learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned certification department, The system estimates user sentiment and determines the priority of NFT issuance based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned certification department, When issuing NFTs, the system will consider the user's geographical location information to issue NFTs that are highly relevant to the user. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned certification department, When issuing NFTs, we analyze the user's social media activity and adjust the issuance content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. A recommendation department that suggests the most suitable learning materials and courses based on the user's interests, The Environment Provision Department provides virtual experiments and simulations based on the teaching materials and courses proposed by the aforementioned Recommendation Department, The progress management unit provides real-time information on the learning status in the learning environment provided by the aforementioned environment provision unit and provides appropriate feedback. Based on the learning status obtained by the aforementioned progress management unit, the collaborative learning unit promotes collaborative learning and discussions with other learners, The system includes a certification unit that issues NFTs based on the learning outcomes obtained by the aforementioned progress management unit. A system characterized by the following features.

2. The aforementioned recommendation department, The system estimates the user's emotions and adjusts the suggested learning materials and courses based on those estimated emotions. The system according to feature 1.

3. The aforementioned recommendation department, Analyze the user's past learning history to select the most suitable learning materials and courses. The system according to feature 1.

4. The aforementioned recommendation department, When suggesting learning materials and courses, filter them based on the user's current learning progress and goals. The system according to feature 1.

5. The aforementioned recommendation department, It estimates the user's emotions and determines the priority of suggested learning materials and courses based on those estimated emotions. The system according to feature 1.

6. The aforementioned recommendation department, When suggesting teaching materials and courses, the system prioritizes suggesting highly relevant materials and courses by considering the user's geographical location. The system according to feature 1.

7. The aforementioned recommendation department, When suggesting teaching materials and courses, we analyze users' social media activity and suggest relevant materials and courses. The system according to feature 1.

8. The aforementioned environment provision unit, It estimates the user's emotions and adjusts the content of virtual experiments and simulations based on the estimated user emotions. The system according to feature 1.

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