system

Abstract

The system according to this embodiment aims to monitor learning progress in real time and provide appropriate advice. [Solution] The system according to the embodiment comprises a progress tracking unit, an advice unit, and a matching unit. The progress tracking unit grasps learning progress in real time. The advice unit provides appropriate advice based on the progress grasped by the progress tracking unit. The matching unit matches other users based on the advice provided by the advice unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=3X]] In the conventional technology, the learning progress is not sufficiently grasped in real time and appropriate advice is not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to grasp the learning progress in real time and provide appropriate advice.

Means for Solving the Problems

[0006] Note: The tags and are marked as "X" in the translation because they seem to be specific tags in the original that don't have a clear semantic meaning for translation. If there is more context available, a more accurate translation might be possible for these.The system according to this embodiment comprises a progress tracking unit, an advice unit, and a matching unit. The progress tracking unit monitors learning progress in real time. The advice unit provides appropriate advice based on the progress monitored by the progress tracking unit. The matching unit matches the user with other users based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor learning progress in real time and provide appropriate advice. [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, etc. The communication I / F manages 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 learning support system according to an embodiment of the present invention is a system in which AI provides support for learning content and assistance in problem solving through a messaging application. This learning support system grasps the user's learning progress in real time and provides appropriate advice. The learning support system also matches the user with other users who are learning the same content, supporting learning in a community. Furthermore, the learning support system proposes a personalized learning plan and answers questions using natural language processing. For example, the learning support system grasps the user's learning progress in real time and provides appropriate advice. For example, the learning support system matches the user with other users who are learning the same content, supporting learning in a community. Furthermore, the learning support system proposes a personalized learning plan and answers questions using natural language processing. In addition, the learning support system is equipped with a motivation maintenance function and a testing and evaluation function, comprehensively supporting the user's learning. As a result, the user can efficiently advance their learning and continue learning while maintaining motivation through learning in a community. In this way, the learning support system can comprehensively support the user's learning.

[0029] The learning support system according to this embodiment comprises a progress tracking unit, an advice unit, and a matching unit. The progress tracking unit grasps learning progress in real time. For example, the progress tracking unit monitors the user's learning activities and collects progress data. For example, the progress tracking unit can collect data such as learning time, learning content, and learning speed in real time. The progress tracking unit can also refer to the user's learning history and predict progress. For example, the progress tracking unit predicts future learning progress based on past learning data. The advice unit provides appropriate advice based on the progress grasped by the progress tracking unit. For example, the advice unit provides advice on learning methods and learning content according to the user's learning progress. For example, if learning progress is behind schedule, the advice unit can provide advice to increase the learning pace. The advice unit can also provide advice to move on to the next learning step if learning progress is on track. The matching unit matches other users based on the advice provided by the advice unit. For example, the matching unit matches users who are learning the same content. The matching unit can, for example, match users with similar learning goals and learning styles. It can also match users whose learning content complements each other. For instance, if one user excels in a particular area while another struggles, the matching unit can match them. This allows the learning support system according to this embodiment to efficiently support user learning.

[0030] The progress tracking unit monitors learning progress in real time. For example, it monitors the user's learning activities and collects progress data. Specifically, it can collect data such as learning time, learning content, and learning speed in real time through tracking functions integrated into the learning platform or application used by the user. This allows for a detailed understanding of which materials the user is studying, how much time they are spending on them, and their level of understanding. The progress tracking unit can also refer to the user's learning history and predict progress. For example, based on past learning data, it can analyze how much time the user spends on a particular subject or topic and what pace they tend to follow. This makes it possible to predict future learning progress and propose an appropriate learning plan to the user. Furthermore, the progress tracking unit utilizes AI to analyze the user's learning patterns and provide learning support optimized for each individual user. For example, the AI ​​can analyze the user's learning data to detect learning stagnation or delays in progress early and issue alerts at the appropriate time. This allows users to always be aware of their learning status and revise their learning methods as needed. The progress tracking unit plays a crucial role in maximizing user learning efficiency and provides support to maintain learning motivation.

[0031] The Advice Unit provides appropriate advice based on the progress tracked by the Progress Tracking Unit. For example, the Advice Unit provides advice on learning methods and content according to the user's learning progress. Specifically, it analyzes the user's learning data and, if learning progress is slow, suggests specific ways to increase the learning pace. For example, this could include increasing learning time, restructuring learning content, or introducing efficient learning techniques. The Advice Unit can also provide advice on moving to the next learning step if learning progress is on track. For example, this could include suggesting the next topic or materials to learn, or providing additional assignments to deepen understanding. The Advice Unit uses AI to analyze the user's learning data and provide advice optimized for each individual user. For example, the AI ​​determines what learning method the user should adopt and which materials are most effective based on the user's learning history and current progress. This allows the user to find the optimal learning method for themselves and learn efficiently. Furthermore, the Advice Unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it analyzes how the user reacted to the advice provided and how the advice affected learning outcomes, and incorporates this into future advice. This means the advice section plays a crucial role in efficiently supporting the user's learning and maximizing learning outcomes.

[0032] The matching unit matches users with each other based on advice provided by the advice unit. Specifically, it matches users who are studying the same learning content. For example, it can find users studying the same subject or topic and form a learning group. This allows users to cooperate with each other and deepen their understanding of the material. The matching unit can also match users with similar learning goals and learning styles. For example, it can match users with the same goals or those progressing at the same learning pace, allowing them to create learning plans together and share their progress. Furthermore, the matching unit can match users whose learning content complements each other. For example, if one user excels in a particular area while another user struggles, matching them allows them to teach each other and improve learning efficiency. The matching unit utilizes AI to analyze users' learning data and perform optimal matching. For example, the AI ​​finds the most suitable partner based on the user's learning history, current progress, and learning style. This allows users to find a learning partner that suits them and learn efficiently. In addition, the matching unit can collect user feedback and continuously improve the accuracy and effectiveness of the matching process. For example, the system analyzes how users felt about their learning experience with their matched partners and how the matching impacted their learning outcomes, and uses this information to improve future matching processes. This allows the matching system to efficiently support user learning and play a crucial role in maximizing learning outcomes.

[0033] The Plan Proposal Unit proposes personalized learning plans. For example, the Plan Proposal Unit creates individual learning plans based on the user's learning goals and learning style. The Plan Proposal Unit can also propose the optimal learning plan by referring to the user's learning progress and learning history. Furthermore, the Plan Proposal Unit can adjust the learning plan according to the user's learning pace and learning content. For example, if the user is falling behind in their learning progress, the Plan Proposal Unit will revise the learning plan and propose a more efficient learning method. This improves learning efficiency by proposing the optimal learning plan for the user. Some or all of the above processes in the Plan Proposal Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Plan Proposal Unit can input the user's learning goals and learning style into a generative AI, which can then propose the optimal learning plan.

[0034] The question-answering unit performs question-answering using natural language processing. For example, the question-answering unit analyzes user questions using natural language processing techniques and provides appropriate answers. The question-answering unit can understand the intent of user questions using, for example, morphological analysis and grammatical analysis. The question-answering unit can also generate the optimal answer to user questions using semantic analysis. For example, if a user asks, "Please tell me how to solve this problem," the question-answering unit will provide an answer that explains how to solve the problem in detail. In this way, it can provide appropriate answers to user questions using natural language processing. Some or all of the above processing in the question-answering unit may be performed using, for example, generative AI, or without generative AI. For example, the question-answering unit can input user questions into generative AI, and the generative AI can generate the optimal answer.

[0035] The Motivation Maintenance Unit maintains the user's motivation. For example, the Motivation Maintenance Unit can enhance the user's desire to learn by using a reward system. For example, the Motivation Maintenance Unit can set learning goals and provide rewards when those goals are achieved. The Motivation Maintenance Unit can also maintain the user's desire to learn by using feedback. For example, the Motivation Maintenance Unit can provide encouraging messages and advice according to the user's learning progress. This helps maintain the user's motivation and supports continued learning. Some or all of the above processes in the Motivation Maintenance Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Motivation Maintenance Unit can input the user's learning progress data into a generative AI, which can then generate optimal feedback.

[0036] The Test and Evaluation Unit conducts tests and evaluations to check the level of understanding. For example, the Test and Evaluation Unit creates tests based on the user's learning content and evaluates the user's level of understanding. The Test and Evaluation Unit can evaluate the user's level of understanding using, for example, multiple-choice questions or written response questions. The Test and Evaluation Unit can also provide feedback to the user based on the evaluation results. For example, the Test and Evaluation Unit analyzes the user's test results and identifies areas where the user's understanding is high and low. This allows for checking the user's level of understanding and providing appropriate evaluation, thereby improving the effectiveness of learning. Some or all of the above processes in the Test and Evaluation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Test and Evaluation Unit can input the user's test results into a generative AI, which can then analyze the evaluation results and generate feedback.

[0037] The progress tracking unit can predict learning progress by referring to the user's past learning history when understanding learning progress. For example, the progress tracking unit can predict future learning progress based on the content and speed of learning the user has learned in the past. For example, the progress tracking unit can predict the user's level of understanding of specific learning content from the user's past learning history and display the progress. The progress tracking unit can also analyze the user's past learning patterns and predict delays or advancements in progress. As a result, by referring to the user's past learning history, it is possible to predict future learning progress and provide appropriate support. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's past learning history data into a generating AI, which can then predict progress.

[0038] The progress tracking unit can apply different progress tracking algorithms depending on the user's learning style when understanding learning progress. For example, if the user is a visual learner, the progress tracking unit can display progress using graphs and charts. If the user is an auditory learner, the progress tracking unit can report progress verbally. Furthermore, if the user is an experiential learner, the progress tracking unit can provide interactive progress displays. This allows for more effective learning support by applying a progress tracking algorithm tailored to the user's learning style. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input user learning style data into a generating AI, which can then apply the optimal progress tracking algorithm.

[0039] The progress tracking unit can customize the display of learning progress by taking into account the user's geographical location. For example, if the user is at home, the progress tracking unit can display progress in a way that is appropriate for a relaxed environment. If the user is out and about, the progress tracking unit can display progress in a concise and easy-to-read manner. Furthermore, if the user is in a specific learning environment, the progress tracking unit can provide a progress display optimized for that environment. By customizing the display of progress based on the user's geographical location, more appropriate learning support can be provided. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's geographical location data into a generating AI, which can then customize the display of progress.

[0040] The progress tracking unit can analyze the user's social media activity to predict learning progress. For example, the progress tracking unit can analyze the user's motivation and enthusiasm for learning from their social media posts and predict progress. For example, the progress tracking unit can predict progress based on the user's participation in learning communities on social media. Furthermore, the progress tracking unit can analyze the user's learning-related comments and feedback on social media and predict progress. In this way, by analyzing the user's social media activity, it is possible to grasp their motivation and enthusiasm for learning and predict progress. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's social media activity data into a generating AI, which can then predict progress.

[0041] The advice unit can adjust the level of detail of the advice based on the importance of the learning content when providing advice. For example, the advice unit can provide detailed advice for important learning content, and concise advice for less important learning content. The advice unit can also adjust the length and level of detail of the advice according to the importance of the learning content. By adjusting the level of detail of the advice according to the importance of the learning content, more effective learning support can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learning content importance data into a generating AI, and the generating AI can adjust the level of detail of the advice.

[0042] The advice unit can apply different advice algorithms depending on the category of learning content when providing advice. For example, for science-related learning content, the advice unit can provide advice using mathematical formulas and graphs. For example, for humanities-related learning content, the advice unit can provide advice using text and diagrams. Furthermore, for practical-related learning content, the advice unit can provide advice using videos and audio. By applying an advice algorithm appropriate to the category of learning content, more effective advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learning content category data into a generating AI, which can then apply the optimal advice algorithm.

[0043] The advice unit can prioritize advice based on the submission deadline of the learning material when providing advice. For example, the advice unit can prioritize advice for learning material with an approaching deadline. For example, it can postpone advice for learning material with a distant submission deadline. The advice unit can also adjust the priority of advice according to the submission deadline of the learning material. This allows for more effective learning support by prioritizing advice according to the submission deadline of the learning material. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learning material submission deadline data into a generating AI, which can then determine the priority of advice.

[0044] The advice unit can adjust the order of advice based on the relevance of the learning content when providing advice. For example, the advice unit can prioritize providing advice on highly relevant learning content. For example, it can postpone providing advice on less relevant learning content. The advice unit can also adjust the order of advice according to the relevance of the learning content. By adjusting the order of advice according to the relevance of the learning content, more effective learning support can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the relevance of the learning content into a generating AI, and the generating AI can adjust the order of advice.

[0045] The matching unit can improve the accuracy of matching by considering the interrelationships of learning content during the matching process. For example, the matching unit can match users who are learning the same content. For example, the matching unit can match users whose learning content complements each other. Furthermore, the matching unit can analyze the interrelationships of learning content and match the most suitable users together. This allows for more accurate matching by considering the interrelationships of learning content. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of learning content into a generating AI, which can then improve the accuracy of the matching.

[0046] The matching unit can perform matching while considering user attribute information. For example, the matching unit can perform matching while considering the user's age and gender. For example, the matching unit can perform matching while considering the user's learning style and interests. Furthermore, the matching unit can also perform matching while considering the user's learning history and performance. In this way, by considering user attribute information, it is possible to match more appropriate users with each other. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input user attribute information data into a generating AI, and the generating AI can perform the matching.

[0047] The matching unit can perform matching while considering the geographical distribution of users. For example, the matching unit can match users who live in the same neighborhood. For example, the matching unit can match users who live in the same region. The matching unit can also prioritize matching users who are geographically close to each other. This allows for more appropriate matching of users by considering the geographical distribution of users. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user geographical distribution data into a generating AI, which can then perform the matching.

[0048] The matching unit can improve the accuracy of matching by referring to relevant literature related to the learning content during the matching process. For example, the matching unit can match the most suitable users based on literature related to the learning content. For example, the matching unit can analyze relevant literature related to the learning content and match users who complement each other. The matching unit can also refer to relevant literature related to the learning content and match users with high motivation to learn. This allows for more accurate matching by referring to relevant literature related to the learning content. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on relevant literature related to the learning content into a generating AI, which can then improve the accuracy of the matching.

[0049] The plan proposal unit can propose the optimal plan by referring to the user's past learning history when proposing a learning plan. For example, the plan proposal unit can propose the optimal learning plan based on the user's past learning history. For example, the plan proposal unit can analyze the user's past learning content and progress and propose an effective learning plan. In addition, the plan proposal unit can propose an individually customized learning plan by referring to the user's past learning patterns. In this way, the optimal learning plan can be proposed by referring to the user's past learning history. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or without using AI. For example, the plan proposal unit can input the user's past learning history data into a generating AI, and the generating AI can propose the optimal plan.

[0050] The plan proposal unit can propose the optimal learning plan by considering the user's geographical location information. For example, if the user is at home, the plan proposal unit can propose a plan suitable for home study. If the user is out, the plan proposal unit can propose a plan suitable for studying while out. Furthermore, if the user is in a specific learning environment, the plan proposal unit can propose a learning plan optimized for that environment. This allows the optimal learning plan to be proposed based on the user's geographical location information. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or without AI. For example, the plan proposal unit can input the user's geographical location information data into a generating AI, which can then propose the optimal plan.

[0051] The question-answering unit can provide the optimal answer by referring to the user's past question history when answering a question. For example, the question-answering unit can provide the optimal answer based on the user's past question history. For example, the question-answering unit can analyze the user's past questions and answers to provide an effective answer. The question-answering unit can also provide individually customized answers by referring to the user's past question patterns. In this way, the optimal answer can be provided by referring to the user's past question history. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's past question history data into a generating AI, and the generating AI can provide the optimal answer.

[0052] The question-answering unit can provide the most appropriate answer when answering a question, taking into account the user's device information. For example, if the user is using a smartphone, the question-answering unit can provide an answer that is adapted to the screen size. If the user is using a tablet, the question-answering unit can provide an answer optimized for a larger screen. Furthermore, if the user is using a smartwatch, the question-answering unit can provide a concise and highly visible answer. This allows the system to provide the most appropriate answer based on the user's device information. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's device information data into a generating AI, which can then provide the most appropriate answer.

[0053] The motivation maintenance unit can propose the optimal motivation maintenance method by referring to the user's past learning history when maintaining motivation. For example, the motivation maintenance unit can propose the optimal motivation maintenance method based on the user's past learning history. For example, the motivation maintenance unit can analyze the user's past learning content and progress and propose an effective motivation maintenance method. Furthermore, the motivation maintenance unit can propose individually customized motivation maintenance methods by referring to the user's past learning patterns. In this way, the optimal motivation maintenance method can be proposed by referring to the user's past learning history. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's past learning history data into a generating AI, and the generating AI can propose the optimal maintenance method.

[0054] The motivation maintenance unit can propose the optimal motivation maintenance method when considering the user's geographical location information. For example, if the user is at home, the motivation maintenance unit can propose a motivation maintenance method suitable for home learning. For example, if the user is out, the motivation maintenance unit can propose a motivation maintenance method suitable for learning while out. Furthermore, if the user is in a specific learning environment, the motivation maintenance unit can propose a motivation maintenance method optimized for that environment. This allows the optimal motivation maintenance method to be proposed based on the user's geographical location information. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the user's geographical location information data into a generating AI, which can then propose the optimal maintenance method.

[0055] The test evaluation unit can propose the optimal evaluation method by referring to the user's past test history during test evaluation. For example, the test evaluation unit can propose the optimal evaluation method based on the user's past test history. For example, the test evaluation unit can propose an effective evaluation method by analyzing the user's past test content and performance. Furthermore, the test evaluation unit can propose an individually customized evaluation method by referring to the user's past test patterns. In this way, the optimal evaluation method can be proposed by referring to the user's past test history. Some or all of the above processing in the test evaluation unit may be performed using AI, for example, or without using AI. For example, the test evaluation unit can input the user's past test history data into a generating AI, and the generating AI can propose the optimal evaluation method.

[0056] The test evaluation unit can propose the optimal evaluation method during test evaluation, taking into account the user's device information. For example, if the user is using a smartphone, the test evaluation unit can provide an evaluation method adapted to the screen size. For example, if the user is using a tablet, the test evaluation unit can provide an evaluation method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the test evaluation unit can provide a concise and highly visible evaluation method. This allows the optimal evaluation method to be proposed based on the user's device information. Some or all of the above processing in the test evaluation unit may be performed using AI, for example, or without AI. For example, the test evaluation unit can input user device information data into a generating AI, which can then propose the optimal evaluation method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The progress tracking unit can predict user learning progress by referencing the user's physiological data. For example, it can predict learning concentration and fatigue levels based on the user's heart rate and sleep data. For example, it can predict learning efficiency during specific time periods based on the user's physiological data and suggest the optimal learning time. Furthermore, by analyzing the user's physiological data and displaying learning progress, the progress tracking unit can provide feedback tailored to the user's physical condition. In this way, by referring to the user's physiological data, it is possible to provide more accurate predictions and support for learning progress.

[0059] The learning plan proposal department can propose learning plans that take into account the user's hobbies and interests when monitoring their learning progress. For example, the department can prioritize suggesting learning content related to areas of interest to the user. By incorporating topics related to the user's hobbies into the learning plan, the department can increase the user's motivation to learn. Furthermore, the department can customize the content and progression of the learning plan based on the user's interests. This allows for the provision of more effective learning plans that take into account the user's hobbies and interests.

[0060] The question-answering unit can adjust its question-answering method to take into account the user's learning environment when understanding the user's learning progress. For example, if the user is learning in a quiet environment, the question-answering unit can provide answers that include detailed explanations. If the user is learning in a noisy environment, for example, the question-answering unit can provide concise and to-the-point answers. Furthermore, the question-answering unit can also adjust the format and content of the answers according to the user's learning environment. This allows for more effective question-answering by taking the user's learning environment into consideration.

[0061] The motivation maintenance unit can suggest methods for maintaining motivation while considering the user's learning goals when monitoring the user's learning progress. For example, if the user has short-term goals, the motivation maintenance unit can provide short-term rewards. For example, if the user has long-term goals, the motivation maintenance unit can provide long-term rewards. Furthermore, the motivation maintenance unit can customize methods for maintaining motivation according to the user's learning goals. This allows for more effective motivation maintenance by considering the user's learning goals.

[0062] The test evaluation unit can adjust its test evaluation methods to take into account the user's learning style when understanding the user's learning progress. For example, if the user is a visual learner, the test evaluation unit can provide evaluations using graphs and charts. If the user is an auditory learner, the test evaluation unit can provide evaluations using audio. Furthermore, if the user is an experiential learner, the test evaluation unit can provide interactive evaluation methods. This allows for more effective test evaluation by taking into account the user's learning style.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The progress tracking unit monitors learning progress in real time. The progress tracking unit monitors the user's learning activities and collects progress data. For example, it can collect data such as learning time, learning content, and learning speed in real time. The progress tracking unit can also refer to the user's learning history and predict future learning progress based on past learning data. Step 2: The advice unit provides appropriate advice based on the progress tracked by the progress tracking unit. The advice unit provides advice on learning methods and content according to the user's learning progress. For example, if learning progress is slow, it can provide advice on how to increase the learning pace, and if learning progress is on track, it can provide advice on how to move on to the next learning step. Step 3: The matching unit matches users with each other based on the advice provided by the advice unit. The matching unit can match users who are learning the same material, or users with similar learning goals and styles. It can also match users whose learning content complements each other. For example, if one user is strong in a particular area but another user struggles with it, the matching unit can match the two.

[0065] (Example of form 2) The learning support system according to an embodiment of the present invention is a system in which AI provides support for learning content and assistance in problem solving through a messaging application. This learning support system grasps the user's learning progress in real time and provides appropriate advice. The learning support system also matches the user with other users who are learning the same content, supporting learning in a community. Furthermore, the learning support system proposes a personalized learning plan and answers questions using natural language processing. For example, the learning support system grasps the user's learning progress in real time and provides appropriate advice. For example, the learning support system matches the user with other users who are learning the same content, supporting learning in a community. Furthermore, the learning support system proposes a personalized learning plan and answers questions using natural language processing. In addition, the learning support system is equipped with a motivation maintenance function and a testing and evaluation function, comprehensively supporting the user's learning. As a result, the user can efficiently advance their learning and continue learning while maintaining motivation through learning in a community. In this way, the learning support system can comprehensively support the user's learning.

[0066] The learning support system according to this embodiment comprises a progress tracking unit, an advice unit, and a matching unit. The progress tracking unit grasps learning progress in real time. For example, the progress tracking unit monitors the user's learning activities and collects progress data. For example, the progress tracking unit can collect data such as learning time, learning content, and learning speed in real time. The progress tracking unit can also refer to the user's learning history and predict progress. For example, the progress tracking unit predicts future learning progress based on past learning data. The advice unit provides appropriate advice based on the progress grasped by the progress tracking unit. For example, the advice unit provides advice on learning methods and learning content according to the user's learning progress. For example, if learning progress is behind schedule, the advice unit can provide advice to increase the learning pace. The advice unit can also provide advice to move on to the next learning step if learning progress is on track. The matching unit matches other users based on the advice provided by the advice unit. For example, the matching unit matches users who are learning the same content. The matching unit can, for example, match users with similar learning goals and learning styles. It can also match users whose learning content complements each other. For instance, if one user excels in a particular area while another struggles, the matching unit can match them. This allows the learning support system according to this embodiment to efficiently support user learning.

[0067] The progress tracking unit monitors learning progress in real time. For example, it monitors the user's learning activities and collects progress data. Specifically, it can collect data such as learning time, learning content, and learning speed in real time through tracking functions integrated into the learning platform or application used by the user. This allows for a detailed understanding of which materials the user is studying, how much time they are spending on them, and their level of understanding. The progress tracking unit can also refer to the user's learning history and predict progress. For example, based on past learning data, it can analyze how much time the user spends on a particular subject or topic and what pace they tend to follow. This makes it possible to predict future learning progress and propose an appropriate learning plan to the user. Furthermore, the progress tracking unit utilizes AI to analyze the user's learning patterns and provide learning support optimized for each individual user. For example, the AI ​​can analyze the user's learning data to detect learning stagnation or delays in progress early and issue alerts at the appropriate time. This allows users to always be aware of their learning status and revise their learning methods as needed. The progress tracking unit plays a crucial role in maximizing user learning efficiency and provides support to maintain learning motivation.

[0068] The Advice Unit provides appropriate advice based on the progress tracked by the Progress Tracking Unit. For example, the Advice Unit provides advice on learning methods and content according to the user's learning progress. Specifically, it analyzes the user's learning data and, if learning progress is slow, suggests specific ways to increase the learning pace. For example, this could include increasing learning time, restructuring learning content, or introducing efficient learning techniques. The Advice Unit can also provide advice on moving to the next learning step if learning progress is on track. For example, this could include suggesting the next topic or materials to learn, or providing additional assignments to deepen understanding. The Advice Unit uses AI to analyze the user's learning data and provide advice optimized for each individual user. For example, the AI ​​determines what learning method the user should adopt and which materials are most effective based on the user's learning history and current progress. This allows the user to find the optimal learning method for themselves and learn efficiently. Furthermore, the Advice Unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it analyzes how the user reacted to the advice provided and how the advice affected learning outcomes, and incorporates this into future advice. This means the advice section plays a crucial role in efficiently supporting the user's learning and maximizing learning outcomes.

[0069] The matching unit matches users with each other based on advice provided by the advice unit. Specifically, it matches users who are studying the same learning content. For example, it can find users studying the same subject or topic and form a learning group. This allows users to cooperate with each other and deepen their understanding of the material. The matching unit can also match users with similar learning goals and learning styles. For example, it can match users with the same goals or those progressing at the same learning pace, allowing them to create learning plans together and share their progress. Furthermore, the matching unit can match users whose learning content complements each other. For example, if one user excels in a particular area while another user struggles, matching them allows them to teach each other and improve learning efficiency. The matching unit utilizes AI to analyze users' learning data and perform optimal matching. For example, the AI ​​finds the most suitable partner based on the user's learning history, current progress, and learning style. This allows users to find a learning partner that suits them and learn efficiently. In addition, the matching unit can collect user feedback and continuously improve the accuracy and effectiveness of the matching process. For example, the system analyzes how users felt about their learning experience with their matched partners and how the matching impacted their learning outcomes, and uses this information to improve future matching processes. This allows the matching system to efficiently support user learning and play a crucial role in maximizing learning outcomes.

[0070] The Plan Proposal Unit proposes personalized learning plans. For example, the Plan Proposal Unit creates individual learning plans based on the user's learning goals and learning style. The Plan Proposal Unit can also propose the optimal learning plan by referring to the user's learning progress and learning history. Furthermore, the Plan Proposal Unit can adjust the learning plan according to the user's learning pace and learning content. For example, if the user is falling behind in their learning progress, the Plan Proposal Unit will revise the learning plan and propose a more efficient learning method. This improves learning efficiency by proposing the optimal learning plan for the user. Some or all of the above processes in the Plan Proposal Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Plan Proposal Unit can input the user's learning goals and learning style into a generative AI, which can then propose the optimal learning plan.

[0071] The question-answering unit performs question-answering using natural language processing. For example, the question-answering unit analyzes user questions using natural language processing techniques and provides appropriate answers. The question-answering unit can understand the intent of user questions using, for example, morphological analysis and grammatical analysis. The question-answering unit can also generate the optimal answer to user questions using semantic analysis. For example, if a user asks, "Please tell me how to solve this problem," the question-answering unit will provide an answer that explains how to solve the problem in detail. In this way, it can provide appropriate answers to user questions using natural language processing. Some or all of the above processing in the question-answering unit may be performed using, for example, generative AI, or without generative AI. For example, the question-answering unit can input user questions into generative AI, and the generative AI can generate the optimal answer.

[0072] The Motivation Maintenance Unit maintains the user's motivation. For example, the Motivation Maintenance Unit can enhance the user's desire to learn by using a reward system. For example, the Motivation Maintenance Unit can set learning goals and provide rewards when those goals are achieved. The Motivation Maintenance Unit can also maintain the user's desire to learn by using feedback. For example, the Motivation Maintenance Unit can provide encouraging messages and advice according to the user's learning progress. This helps maintain the user's motivation and supports continued learning. Some or all of the above processes in the Motivation Maintenance Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Motivation Maintenance Unit can input the user's learning progress data into a generative AI, which can then generate optimal feedback.

[0073] The Test and Evaluation Unit conducts tests and evaluations to check the level of understanding. For example, the Test and Evaluation Unit creates tests based on the user's learning content and evaluates the user's level of understanding. The Test and Evaluation Unit can evaluate the user's level of understanding using, for example, multiple-choice questions or written response questions. The Test and Evaluation Unit can also provide feedback to the user based on the evaluation results. For example, the Test and Evaluation Unit analyzes the user's test results and identifies areas where the user's understanding is high and low. This allows for checking the user's level of understanding and providing appropriate evaluation, thereby improving the effectiveness of learning. Some or all of the above processes in the Test and Evaluation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Test and Evaluation Unit can input the user's test results into a generative AI, which can then analyze the evaluation results and generate feedback.

[0074] The progress tracking unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated user emotions. For example, if the user is feeling stressed, the progress tracking unit can provide a simple and visually less burdensome display method. For example, if the user is relaxed, the progress tracking unit can display detailed progress information to enhance the sense of accomplishment in learning. Furthermore, if the user is in a hurry, the progress tracking unit can provide a concise display of progress that gets straight to the point. In this way, by adjusting the display method of learning progress according to the user's emotions, the burden of learning can be reduced and efficient learning can be supported. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of learning progress.

[0075] The progress tracking unit can predict learning progress by referring to the user's past learning history when understanding learning progress. For example, the progress tracking unit can predict future learning progress based on the content and speed of learning the user has learned in the past. For example, the progress tracking unit can predict the user's level of understanding of specific learning content from the user's past learning history and display the progress. The progress tracking unit can also analyze the user's past learning patterns and predict delays or advancements in progress. As a result, by referring to the user's past learning history, it is possible to predict future learning progress and provide appropriate support. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's past learning history data into a generating AI, which can then predict progress.

[0076] The progress tracking unit can apply different progress tracking algorithms depending on the user's learning style when understanding learning progress. For example, if the user is a visual learner, the progress tracking unit can display progress using graphs and charts. If the user is an auditory learner, the progress tracking unit can report progress verbally. Furthermore, if the user is an experiential learner, the progress tracking unit can provide interactive progress displays. This allows for more effective learning support by applying a progress tracking algorithm tailored to the user's learning style. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input user learning style data into a generating AI, which can then apply the optimal progress tracking algorithm.

[0077] The progress tracking unit can estimate the user's emotions and adjust the frequency of progress tracking based on the estimated emotions. For example, if the user is stressed, the progress tracking unit can reduce the frequency of progress tracking to alleviate the burden. For example, if the user is relaxed, the progress tracking unit can track progress more frequently to maintain motivation for learning. Furthermore, if the user is in a hurry, the progress tracking unit can increase the frequency of progress tracking to support efficient learning. In this way, by adjusting the frequency of progress tracking according to the user's emotions, the burden of learning can be reduced and efficient learning can be supported. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input user emotion data into the generative AI, and the generative AI can adjust the frequency of progress tracking.

[0078] The progress tracking unit can customize the display of learning progress by taking into account the user's geographical location. For example, if the user is at home, the progress tracking unit can display progress in a way that is appropriate for a relaxed environment. If the user is out and about, the progress tracking unit can display progress in a concise and easy-to-read manner. Furthermore, if the user is in a specific learning environment, the progress tracking unit can provide a progress display optimized for that environment. By customizing the display of progress based on the user's geographical location, more appropriate learning support can be provided. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's geographical location data into a generating AI, which can then customize the display of progress.

[0079] The progress tracking unit can analyze the user's social media activity to predict learning progress. For example, the progress tracking unit can analyze the user's motivation and enthusiasm for learning from their social media posts and predict progress. For example, the progress tracking unit can predict progress based on the user's participation in learning communities on social media. Furthermore, the progress tracking unit can analyze the user's learning-related comments and feedback on social media and predict progress. In this way, by analyzing the user's social media activity, it is possible to grasp their motivation and enthusiasm for learning and predict progress. Some or all of the above processing in the progress tracking unit may be performed using AI, for example, or without AI. For example, the progress tracking unit can input the user's social media activity data into a generating AI, which can then predict progress.

[0080] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide advice in gentle language. If the user is relaxed, the advice unit can provide detailed advice to encourage deeper learning. If the user is in a hurry, the advice unit can provide concise and to-the-point advice. By adjusting the way advice is expressed according to the user's emotions, more effective advice can be provided. 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI, which can then adjust the way the advice is expressed.

[0081] The advice unit can adjust the level of detail of the advice based on the importance of the learning content when providing advice. For example, the advice unit can provide detailed advice for important learning content, and concise advice for less important learning content. The advice unit can also adjust the length and level of detail of the advice according to the importance of the learning content. By adjusting the level of detail of the advice according to the importance of the learning content, more effective learning support can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learning content importance data into a generating AI, and the generating AI can adjust the level of detail of the advice.

[0082] The advice unit can apply different advice algorithms depending on the category of learning content when providing advice. For example, for science-related learning content, the advice unit can provide advice using mathematical formulas and graphs. For example, for humanities-related learning content, the advice unit can provide advice using text and diagrams. Furthermore, for practical-related learning content, the advice unit can provide advice using videos and audio. By applying an advice algorithm appropriate to the category of learning content, more effective advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learning content category data into a generating AI, which can then apply the optimal advice algorithm.

[0083] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice to encourage deeper learning. The advice unit can also provide concise and quick advice if the user is in a hurry. By adjusting the length of the advice according to the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI, which can then adjust the length of the advice.

[0084] The advice unit can prioritize advice based on the submission deadline of the learning material when providing advice. For example, the advice unit can prioritize advice for learning material with an approaching deadline. For example, it can postpone advice for learning material with a distant submission deadline. The advice unit can also adjust the priority of advice according to the submission deadline of the learning material. This allows for more effective learning support by prioritizing advice according to the submission deadline of the learning material. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input learning material submission deadline data into a generating AI, which can then determine the priority of advice.

[0085] The advice unit can adjust the order of advice based on the relevance of the learning content when providing advice. For example, the advice unit can prioritize providing advice on highly relevant learning content. For example, it can postpone providing advice on less relevant learning content. The advice unit can also adjust the order of advice according to the relevance of the learning content. By adjusting the order of advice according to the relevance of the learning content, more effective learning support can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the relevance of the learning content into a generating AI, and the generating AI can adjust the order of advice.

[0086] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is feeling stressed, the matching unit will match them with other users who have similar emotions. If a user is relaxed, the matching unit can match them with other users who are highly motivated to learn. If a user is in a hurry, the matching unit can match them with other users who are more efficient at learning. By adjusting the matching criteria according to the user's emotions, more appropriate users can be matched. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into the generative AI, which can then adjust the matching criteria.

[0087] The matching unit can improve the accuracy of matching by considering the interrelationships of learning content during the matching process. For example, the matching unit can match users who are learning the same content. For example, the matching unit can match users whose learning content complements each other. Furthermore, the matching unit can analyze the interrelationships of learning content and match the most suitable users together. This allows for more accurate matching by considering the interrelationships of learning content. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of learning content into a generating AI, which can then improve the accuracy of the matching.

[0088] The matching unit can perform matching while considering user attribute information. For example, the matching unit can perform matching while considering the user's age and gender. For example, the matching unit can perform matching while considering the user's learning style and interests. Furthermore, the matching unit can also perform matching while considering the user's learning history and performance. In this way, by considering user attribute information, it is possible to match more appropriate users with each other. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input user attribute information data into a generating AI, and the generating AI can perform the matching.

[0089] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is stressed, the matching unit will prioritize displaying the most suitable user. If the user is relaxed, the matching unit can display multiple candidates and provide options. The matching unit can also quickly display matching results if the user is in a hurry. By adjusting the order in which matching results are displayed according to the user's emotions, more appropriate matching results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not using AI. For example, the matching unit can input user emotion data into the generative AI, which can then adjust the order in which matching results are displayed.

[0090] The matching unit can perform matching while considering the geographical distribution of users. For example, the matching unit can match users who live in the same neighborhood. For example, the matching unit can match users who live in the same region. The matching unit can also prioritize matching users who are geographically close to each other. This allows for more appropriate matching of users by considering the geographical distribution of users. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user geographical distribution data into a generating AI, which can then perform the matching.

[0091] The matching unit can improve the accuracy of matching by referring to relevant literature related to the learning content during the matching process. For example, the matching unit can match the most suitable users based on literature related to the learning content. For example, the matching unit can analyze relevant literature related to the learning content and match users who complement each other. The matching unit can also refer to relevant literature related to the learning content and match users with high motivation to learn. This allows for more accurate matching by referring to relevant literature related to the learning content. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on relevant literature related to the learning content into a generating AI, which can then improve the accuracy of the matching.

[0092] The plan suggestion unit can estimate the user's emotions and adjust the method of suggesting learning plans based on the estimated emotions. For example, if the user is feeling stressed, the plan suggestion unit can suggest a simple and less burdensome learning plan. If the user is relaxed, for example, the plan suggestion unit can suggest a detailed learning plan to encourage deeper learning. Also, if the user is in a hurry, the plan suggestion unit can suggest an efficient learning plan. In this way, by adjusting the method of suggesting learning plans according to the user's emotions, a more effective learning plan 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 plan suggestion unit may be performed using AI, for example, or not using AI. For example, the plan suggestion unit can input user emotion data into the generative AI, and the generative AI can adjust the method of suggesting learning plans.

[0093] The plan proposal unit can propose the optimal plan by referring to the user's past learning history when proposing a learning plan. For example, the plan proposal unit can propose the optimal learning plan based on the user's past learning history. For example, the plan proposal unit can analyze the user's past learning content and progress and propose an effective learning plan. In addition, the plan proposal unit can propose an individually customized learning plan by referring to the user's past learning patterns. In this way, the optimal learning plan can be proposed by referring to the user's past learning history. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or without using AI. For example, the plan proposal unit can input the user's past learning history data into a generating AI, and the generating AI can propose the optimal plan.

[0094] The plan suggestion unit can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is stressed, the plan suggestion unit will prioritize suggesting less burdensome learning plans. If the user is relaxed, the plan suggestion unit can prioritize suggesting detailed learning plans. Furthermore, if the user is in a hurry, the plan suggestion unit can prioritize suggesting efficient learning plans. This allows for the provision of more effective learning plans by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the plan suggestion unit may be performed using AI, or not. For example, the plan suggestion unit can input user emotion data into a generative AI, which can then determine the priority of learning plans.

[0095] The plan proposal unit can propose the optimal learning plan by considering the user's geographical location information. For example, if the user is at home, the plan proposal unit can propose a plan suitable for home study. If the user is out, the plan proposal unit can propose a plan suitable for studying while out. Furthermore, if the user is in a specific learning environment, the plan proposal unit can propose a learning plan optimized for that environment. This allows the optimal learning plan to be proposed based on the user's geographical location information. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or without AI. For example, the plan proposal unit can input the user's geographical location information data into a generating AI, which can then propose the optimal plan.

[0096] The question-answering unit can estimate the user's emotions and adjust its question-answering method based on the estimated emotions. For example, if the user is stressed, the question-answering unit can provide answers in gentle language. If the user is relaxed, the question-answering unit can provide detailed answers to encourage deeper learning. If the user is in a hurry, the question-answering unit can also provide concise and to-the-point answers. By adjusting the question-answering method according to the user's emotions, more effective answers 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 question-answering unit may be performed using AI, for example, or not using AI. For example, the question-answering unit can input user emotion data into the generative AI, which can then adjust its question-answering method.

[0097] The question-answering unit can provide the optimal answer by referring to the user's past question history when answering a question. For example, the question-answering unit can provide the optimal answer based on the user's past question history. For example, the question-answering unit can analyze the user's past questions and answers to provide an effective answer. The question-answering unit can also provide individually customized answers by referring to the user's past question patterns. In this way, the optimal answer can be provided by referring to the user's past question history. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's past question history data into a generating AI, and the generating AI can provide the optimal answer.

[0098] The question-answering unit can estimate the user's emotions and determine the priority of question-answering based on the estimated emotions. For example, if the user is stressed, the question-answering unit can provide a quick answer. For example, if the user is relaxed, the question-answering unit can prioritize providing a detailed answer. Also, if the user is in a hurry, the question-answering unit can prioritize providing a concise and quick answer. In this way, by determining the priority of question-answering according to the user's emotions, more effective answers 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 question-answering unit may be performed using AI, for example, or not using AI. For example, the question-answering unit can input user emotion data into a generative AI, and the generative AI can determine the priority of question-answering.

[0099] The question-answering unit can provide the most appropriate answer when answering a question, taking into account the user's device information. For example, if the user is using a smartphone, the question-answering unit can provide an answer that is adapted to the screen size. If the user is using a tablet, the question-answering unit can provide an answer optimized for a larger screen. Furthermore, if the user is using a smartwatch, the question-answering unit can provide a concise and highly visible answer. This allows the system to provide the most appropriate answer based on the user's device information. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the user's device information data into a generating AI, which can then provide the most appropriate answer.

[0100] The motivation maintenance unit can estimate the user's emotions and adjust the method of maintaining motivation based on the estimated user emotions. For example, if the user is feeling stressed, the motivation maintenance unit can maintain motivation in a way that helps them relax. For example, if the user is relaxed, the motivation maintenance unit can maintain motivation in a way that enhances their desire to learn. Furthermore, if the user is in a hurry, the motivation maintenance unit can maintain motivation in a way that allows them to learn efficiently. In this way, by adjusting the method of maintaining motivation according to the user's emotions, more effective motivation maintenance can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's emotion data into the generative AI, and the generative AI can adjust the method of maintaining motivation.

[0101] The motivation maintenance unit can propose the optimal motivation maintenance method by referring to the user's past learning history when maintaining motivation. For example, the motivation maintenance unit can propose the optimal motivation maintenance method based on the user's past learning history. For example, the motivation maintenance unit can analyze the user's past learning content and progress and propose an effective motivation maintenance method. Furthermore, the motivation maintenance unit can propose individually customized motivation maintenance methods by referring to the user's past learning patterns. In this way, the optimal motivation maintenance method can be proposed by referring to the user's past learning history. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI. For example, the motivation maintenance unit can input the user's past learning history data into a generating AI, and the generating AI can propose the optimal maintenance method.

[0102] The motivation maintenance unit can estimate the user's emotions and determine priorities for maintaining motivation based on those emotions. For example, if the user is feeling stressed, the motivation maintenance unit will prioritize suggesting ways to relax. If the user is relaxed, the motivation maintenance unit will prioritize suggesting ways to increase their motivation to learn. Furthermore, if the user is in a hurry, the motivation maintenance unit will prioritize suggesting ways to learn efficiently. By determining priorities for maintaining motivation according to the user's emotions, more effective motivation maintenance can be achieved. 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 motivation maintenance unit may be performed using AI, or not using AI. For example, the motivation maintenance unit can input user emotion data into a generative AI, which can then determine priorities for maintaining motivation.

[0103] The motivation maintenance unit can propose the optimal motivation maintenance method when considering the user's geographical location information. For example, if the user is at home, the motivation maintenance unit can propose a motivation maintenance method suitable for home learning. For example, if the user is out, the motivation maintenance unit can propose a motivation maintenance method suitable for learning while out. Furthermore, if the user is in a specific learning environment, the motivation maintenance unit can propose a motivation maintenance method optimized for that environment. This allows the optimal motivation maintenance method to be proposed based on the user's geographical location information. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without AI. For example, the motivation maintenance unit can input the user's geographical location information data into a generating AI, which can then propose the optimal maintenance method.

[0104] The test evaluation unit can estimate the user's emotions and adjust the test evaluation method based on the estimated user emotions. For example, if the user is stressed, the test evaluation unit can provide an evaluation using gentle language. For example, if the user is relaxed, the test evaluation unit can provide a detailed evaluation to encourage deeper learning. Also, if the user is in a hurry, the test evaluation unit can provide a concise and to-the-point evaluation. In this way, by adjusting the test evaluation method according to the user's emotions, a more effective evaluation 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 test evaluation unit may be performed using AI, for example, or not using AI. For example, the test evaluation unit can input user emotion data into a generative AI, and the generative AI can adjust the test evaluation method.

[0105] The test evaluation unit can propose the optimal evaluation method by referring to the user's past test history during test evaluation. For example, the test evaluation unit can propose the optimal evaluation method based on the user's past test history. For example, the test evaluation unit can propose an effective evaluation method by analyzing the user's past test content and performance. Furthermore, the test evaluation unit can propose an individually customized evaluation method by referring to the user's past test patterns. In this way, the optimal evaluation method can be proposed by referring to the user's past test history. Some or all of the above processing in the test evaluation unit may be performed using AI, for example, or without using AI. For example, the test evaluation unit can input the user's past test history data into a generating AI, and the generating AI can propose the optimal evaluation method.

[0106] The test evaluation unit can estimate the user's emotions and determine the priority of test evaluations based on the estimated user emotions. For example, if the user is stressed, the test evaluation unit can provide a quick evaluation. For example, if the user is relaxed, the test evaluation unit can prioritize providing a detailed evaluation. Also, if the user is in a hurry, the test evaluation unit can prioritize providing a concise and quick evaluation. In this way, by determining the priority of test evaluations according to the user's emotions, a more effective evaluation 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 test evaluation unit may be performed using AI, for example, or not using AI. For example, the test evaluation unit can input user emotion data into a generative AI, and the generative AI can determine the priority of test evaluations.

[0107] The test evaluation unit can propose the optimal evaluation method during test evaluation, taking into account the user's device information. For example, if the user is using a smartphone, the test evaluation unit can provide an evaluation method adapted to the screen size. For example, if the user is using a tablet, the test evaluation unit can provide an evaluation method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the test evaluation unit can provide a concise and highly visible evaluation method. This allows the optimal evaluation method to be proposed based on the user's device information. Some or all of the above processing in the test evaluation unit may be performed using AI, for example, or without AI. For example, the test evaluation unit can input user device information data into a generating AI, which can then propose the optimal evaluation method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The progress tracking unit can predict user learning progress by referencing the user's physiological data. For example, it can predict learning concentration and fatigue levels based on the user's heart rate and sleep data. For example, it can predict learning efficiency during specific time periods based on the user's physiological data and suggest the optimal learning time. Furthermore, by analyzing the user's physiological data and displaying learning progress, the progress tracking unit can provide feedback tailored to the user's physical condition. In this way, by referring to the user's physiological data, it is possible to provide more accurate predictions and support for learning progress.

[0110] The learning plan proposal department can propose learning plans that take into account the user's hobbies and interests when monitoring their learning progress. For example, the department can prioritize suggesting learning content related to areas of interest to the user. By incorporating topics related to the user's hobbies into the learning plan, the department can increase the user's motivation to learn. Furthermore, the department can customize the content and progression of the learning plan based on the user's interests. This allows for the provision of more effective learning plans that take into account the user's hobbies and interests.

[0111] The question-answering unit can adjust its question-answering method to take into account the user's learning environment when understanding the user's learning progress. For example, if the user is learning in a quiet environment, the question-answering unit can provide answers that include detailed explanations. If the user is learning in a noisy environment, for example, the question-answering unit can provide concise and to-the-point answers. Furthermore, the question-answering unit can also adjust the format and content of the answers according to the user's learning environment. This allows for more effective question-answering by taking the user's learning environment into consideration.

[0112] The motivation maintenance unit can suggest methods for maintaining motivation while considering the user's learning goals when monitoring the user's learning progress. For example, if the user has short-term goals, the motivation maintenance unit can provide short-term rewards. For example, if the user has long-term goals, the motivation maintenance unit can provide long-term rewards. Furthermore, the motivation maintenance unit can customize methods for maintaining motivation according to the user's learning goals. This allows for more effective motivation maintenance by considering the user's learning goals.

[0113] The test evaluation unit can adjust its test evaluation methods to take into account the user's learning style when understanding the user's learning progress. For example, if the user is a visual learner, the test evaluation unit can provide evaluations using graphs and charts. If the user is an auditory learner, the test evaluation unit can provide evaluations using audio. Furthermore, if the user is an experiential learner, the test evaluation unit can provide interactive evaluation methods. This allows for more effective test evaluation by taking into account the user's learning style.

[0114] The progress tracking unit can estimate the user's emotions and adjust the feedback method for learning progress based on those emotions. For example, if the user is feeling stressed, the progress tracking unit can provide positive feedback. If the user is relaxed, for example, the progress tracking unit can provide detailed feedback to encourage deeper learning. Furthermore, if the user is in a hurry, the progress tracking unit can provide concise and to-the-point feedback. This allows for more effective learning support by adjusting the feedback method for learning progress according to the user's emotions.

[0115] The advice unit can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is stressed, the advice unit can provide advice at a time when the user is relaxed. If the user is relaxed, the advice unit can provide advice at a time that encourages deeper learning. The advice unit can also provide advice quickly if the user is in a hurry. By adjusting the timing of advice according to the user's emotions, more effective advice can be provided.

[0116] The matching unit can estimate the user's emotions and adjust the matching frequency based on those emotions. For example, if the user is stressed, the matching unit can reduce the matching frequency to alleviate the burden. If the user is relaxed, for example, the matching unit can perform more frequent matches to maintain learning motivation. Furthermore, if the user is in a hurry, the matching unit can perform matches quickly to support efficient learning. In this way, by adjusting the matching frequency according to the user's emotions, more appropriate learning support can be provided.

[0117] The learning plan suggestion unit can estimate the user's emotions and adjust the feedback method of the learning plan based on those emotions. For example, if the user is feeling stressed, the suggestion unit can provide positive feedback. If the user is relaxed, for example, the suggestion unit can provide detailed feedback to encourage deeper learning. Furthermore, if the user is in a hurry, the suggestion unit can provide concise and to-the-point feedback. This allows for more effective learning support by adjusting the feedback method of the learning plan according to the user's emotions.

[0118] The question-answering unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is stressed, the unit can provide positive feedback. If the user is relaxed, the unit can provide detailed feedback to encourage deeper learning. Furthermore, if the user is in a hurry, the unit can provide concise and to-the-point feedback. By adjusting the feedback method according to the user's emotions, more effective learning support can be provided.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The progress tracking unit monitors learning progress in real time. The progress tracking unit monitors the user's learning activities and collects progress data. For example, it can collect data such as learning time, learning content, and learning speed in real time. The progress tracking unit can also refer to the user's learning history and predict future learning progress based on past learning data. Step 2: The advice unit provides appropriate advice based on the progress tracked by the progress tracking unit. The advice unit provides advice on learning methods and content according to the user's learning progress. For example, if learning progress is slow, it can provide advice on how to increase the learning pace, and if learning progress is on track, it can provide advice on how to move on to the next learning step. Step 3: The matching unit matches users with each other based on the advice provided by the advice unit. The matching unit can match users who are learning the same material, or users with similar learning goals and styles. It can also match users whose learning content complements each other. For example, if one user is strong in a particular area but another user struggles with it, the matching unit can match the two.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the progress tracking unit, advice unit, matching unit, plan proposal unit, question answering unit, motivation maintenance unit, and test evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the progress tracking unit monitors the user's learning activities and collects progress data using the control unit 46A of the smart device 14. The advice unit provides appropriate advice based on the progress data using the specific processing unit 290 of the data processing unit 12. The matching unit matches the user with other users using the specific processing unit 290 of the data processing unit 12. The plan proposal unit proposes a personalized learning plan using the specific processing unit 290 of the data processing unit 12. The question answering unit analyzes questions from the user and provides appropriate answers using the control unit 46A of the smart device 14. The motivation maintenance unit enhances the user's motivation to learn using the control unit 46A of the smart device 14. The test evaluation unit evaluates the user's level of understanding using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the progress tracking unit, advice unit, matching unit, plan proposal unit, question answering unit, motivation maintenance unit, and test evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the progress tracking unit monitors the user's learning activities and collects progress data using the control unit 46A of the smart glasses 214. The advice unit provides appropriate advice based on the progress data using the specific processing unit 290 of the data processing unit 12. The matching unit matches the user with other users using the specific processing unit 290 of the data processing unit 12. The plan proposal unit proposes a personalized learning plan using the specific processing unit 290 of the data processing unit 12. The question answering unit analyzes questions from the user and provides appropriate answers using the control unit 46A of the smart glasses 214. The motivation maintenance unit enhances the user's motivation to learn using the control unit 46A of the smart glasses 214. The test evaluation unit evaluates the user's level of understanding using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the progress tracking unit, advice unit, matching unit, plan proposal unit, question answering unit, motivation maintenance unit, and test evaluation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the progress tracking unit monitors the user's learning activities and collects progress data using the control unit 46A of the headset terminal 314. The advice unit provides appropriate advice based on the progress data using the specific processing unit 290 of the data processing unit 12. The matching unit matches the user with other users using the specific processing unit 290 of the data processing unit 12. The plan proposal unit proposes a personalized learning plan using the specific processing unit 290 of the data processing unit 12. The question answering unit analyzes questions from the user and provides appropriate answers using the control unit 46A of the headset terminal 314. The motivation maintenance unit enhances the user's motivation to learn using the control unit 46A of the headset terminal 314. The test evaluation unit evaluates the user's level of understanding using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the progress tracking unit, advice unit, matching unit, plan proposal unit, question answering unit, motivation maintenance unit, and test evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the progress tracking unit monitors the user's learning activities and collects progress data via the control unit 46A of the robot 414. The advice unit provides appropriate advice based on the progress data via the specific processing unit 290 of the data processing unit 12. The matching unit matches the user with other users via the specific processing unit 290 of the data processing unit 12. The plan proposal unit proposes a personalized learning plan via the specific processing unit 290 of the data processing unit 12. The question answering unit analyzes questions from the user and provides appropriate answers via the control unit 46A of the robot 414. The motivation maintenance unit enhances the user's motivation to learn via the control unit 46A of the robot 414. The test evaluation unit evaluates the user's level of understanding via the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A progress tracking unit that monitors learning progress in real time, An advice unit provides appropriate advice based on the progress grasped by the progress tracking unit, The system includes a matching unit that matches other users based on the advice provided by the aforementioned advice unit. A system characterized by the following features. (Note 2) It has a plan proposal department that proposes personalized learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a question answering unit that performs question answering using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a motivation maintenance unit to maintain user motivation. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a test and evaluation unit that conducts tests and evaluations to check the level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 6) The progress tracking unit, It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress tracking unit, When tracking learning progress, the system predicts progress by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The progress tracking unit, When tracking learning progress, different progress tracking algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned progress tracking unit, It estimates the user's emotions and adjusts the frequency of progress tracking based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned progress tracking unit, When tracking learning progress, customize the progress display by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned progress tracking unit, When tracking learning progress, we analyze users' social media activity to predict progress. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the learning material. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, we prioritize the advice based on when the learning materials are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is During the matching process, the accuracy of the matching is improved by considering the interrelationships between the learned content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During the matching process, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, the geographical distribution of users is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature related to the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned plan proposal department, It estimates the user's emotions and adjusts how learning plans are suggested based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned plan proposal department, When proposing a learning plan, the system refers to the user's past learning history to suggest the most suitable plan. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned plan proposal department, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned plan proposal department, When proposing a learning plan, we take the user's geographical location into consideration to suggest the most suitable plan. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned question answering unit is It estimates the user's emotions and adjusts the question-answering method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned question answering unit is When answering questions, the system provides the most appropriate answer by referring to the user's past question history. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned question answering unit is The system estimates the user's emotions and prioritizes question responses based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned question answering unit is When answering questions, the system provides the most appropriate answer by taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned motivation maintenance unit is It estimates the user's emotions and adjusts methods for maintaining motivation based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned motivation maintenance unit is When it comes to maintaining motivation, we refer to the user's past learning history to suggest the most effective way to maintain it. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned motivation maintenance unit is It estimates the user's emotions and determines the priority for maintaining motivation based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned motivation maintenance unit is When it comes to maintaining motivation, we propose the optimal method of maintenance, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned test evaluation unit, We estimate user emotions and adjust the test evaluation method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned test evaluation unit, During test evaluation, we refer to the user's past test history to suggest the optimal evaluation method. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned test evaluation unit, The system estimates user sentiment and determines the priority of test evaluations based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned test evaluation unit, During test evaluation, we propose the optimal evaluation method considering the user's device information. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0193] 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 progress tracking unit that monitors learning progress in real time, An advice unit provides appropriate advice based on the progress grasped by the progress tracking unit, The system includes a matching unit that matches other users based on the advice provided by the aforementioned advice unit. A system characterized by the following features.

2. It has a plan proposal department that proposes personalized learning plans. The system according to feature 1.

3. It includes a question answering unit that performs question answering using natural language processing. The system according to feature 1.

4. Equipped with a motivation maintenance unit to maintain user motivation. The system according to feature 1.

5. It includes a test and evaluation unit that conducts tests and evaluations to check the level of understanding. The system according to feature 1.

6. The aforementioned progress tracking unit, It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system according to feature 1.

7. The aforementioned progress tracking unit, When tracking learning progress, the system predicts progress by referring to the user's past learning history. The system according to feature 1.

8. The aforementioned progress tracking unit, When tracking learning progress, different progress tracking algorithms are applied depending on the user's learning style. The system according to feature 1.

9. The aforementioned progress tracking unit, It estimates the user's emotions and adjusts the frequency of progress tracking based on those estimated emotions. The system according to feature 1.

10. The aforementioned progress tracking unit, When tracking learning progress, customize the progress display by taking into account the user's geographical location. The system according to feature 1.

Images