system

Abstract

The system according to this embodiment aims to provide customized learning materials and study plans tailored to each student's learning style and progress. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects students' learning styles and progress. The analysis unit analyzes the information collected by the collection unit. The generation unit creates customized learning materials and learning plans based on the information analyzed by the analysis unit. The monitoring unit monitors progress in real time based on the learning materials and learning plans created by the generation unit and provides adaptive feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to provide customized teaching materials and learning plans according to the learning styles and progress of each individual student, and there is room for improvement.

[0005] The system according to the embodiment aims to provide teaching materials and learning plans customized according to the learning styles and progress of each individual student.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects information on students' learning styles and progress. The analysis unit analyzes the information collected by the collection unit. The generation unit creates customized learning materials and learning plans based on the information analyzed by the analysis unit. The monitoring unit monitors progress in real time based on the learning materials and learning plans created by the generation unit and provides adaptive feedback. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized learning materials and study plans tailored to each student's learning style and progress. [Brief explanation of the drawing]

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

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

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

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

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

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that utilizes generative AI to create and provide customized learning materials and learning plans tailored to each student's learning style and progress. The learning support system collects the student's learning style and progress and inputs it into the generative AI. The generative AI analyzes the collected information and creates optimal learning materials and learning plans for each student. The generated learning materials and learning plans are provided to the student, and the student proceeds with their learning accordingly. Furthermore, the learning support system monitors the student's learning progress in real time and provides adaptive feedback. For example, the learning support system collects the student's learning style and progress. For example, the learning support system collects the student's learning style and progress and inputs it into the generative AI. The generative AI analyzes the collected information and creates optimal learning materials and learning plans for each student. The generated learning materials and learning plans are provided to the student, and the student proceeds with their learning accordingly. Furthermore, the learning support system monitors the student's learning progress in real time and provides adaptive feedback. As a result, the learning support system provides an effective learning environment tailored to the needs of each student, improving the quality of education and eliminating educational disparities. Furthermore, the learning support system also provides operational support tools for teachers, reducing their workload. In addition, the learning support system offers a 24 / 7 AI tutor function, allowing students to receive learning support anytime. This enables the learning support system to provide customized learning materials and plans tailored to each student's learning style and progress.

[0029] The learning support system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects students' learning styles and progress. The collection unit can, for example, use questionnaires or sensor data to collect students' learning styles and progress. The collection unit can, for example, classify students' learning styles into visual, auditory, experiential, etc., and collect information based on that. The collection unit can, for example, evaluate students' progress using test scores or assignment submission status, and collect information based on that. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, performs analysis based on the collected information to create optimal learning materials and learning plans for each individual student. The analysis unit can, for example, use AI to analyze the collected information and evaluate students' learning styles and progress. The analysis unit can, for example, evaluate students' learning styles and progress based on the collected information and create customized learning materials and learning plans based on that. The generation unit creates customized learning materials and learning plans based on the information analyzed by the analysis unit. The generation unit can, for example, use generation AI to create optimal learning materials and study plans for each individual student. The generation unit can, for example, use generation AI to create customized learning materials and study plans based on the student's learning style and progress. The generation unit can, for example, use generation AI to create customized learning materials and study plans based on the student's learning style and progress. The monitoring unit monitors progress in real time based on the learning materials and study plans created by the generation unit and provides adaptive feedback. The monitoring unit can, for example, use AI to monitor the student's learning progress in real time and provide adaptive feedback. The monitoring unit can, for example, use AI to monitor the student's learning progress in real time and provide adaptive feedback.As a result, the learning support system according to this embodiment can provide customized learning materials and learning plans tailored to each student's learning style and progress.

[0030] The data collection unit collects information on students' learning styles and progress. For example, the unit can use questionnaires and sensor data to gather information on students' learning styles and progress. Specifically, questionnaires are conducted via online forms or mobile apps, allowing for detailed inquiries about students' learning habits, preferred learning methods, and motivation. Sensor data is acquired, for example, from cameras, microphones, and accelerometers installed on devices used by students. This allows for real-time monitoring of students' posture, concentration levels, and speech during learning. The data collection unit integrates this data and classifies students' learning styles into categories such as visual, auditory, and experiential. Visual learners benefit from materials that heavily utilize videos and diagrams, while auditory learners benefit from audio commentaries and podcasts. Experiential learners are suited to interactive materials that include experiments and simulations. Furthermore, the data collection unit evaluates students' progress based on test scores and assignment submission status. For example, it can automatically collect online test results and assignment submission status to understand students' comprehension and progress in real time. This allows the data collection unit to collect detailed data on each student's learning style and progress, and provide it to the analysis and generation units. The data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes the information collected by the data collection department. For example, the analysis department uses the collected information to create optimal learning materials and study plans for each student. Specifically, it uses AI to analyze the collected information and evaluate students' learning styles and progress. The AI ​​uses machine learning algorithms to analyze students' learning patterns and tendencies and identify the optimal learning methods. For example, it can be found that visually oriented students are effectively taught using materials that heavily utilize videos and diagrams. Auditory oriented students benefit from audio explanations and podcasts, while experiential students are suited to interactive materials that include experiments and simulations. Furthermore, the analysis department evaluates students' progress based on the collected information and creates customized learning materials and study plans accordingly. For example, it analyzes test scores and assignment submission status to evaluate students' understanding and progress. This allows the analysis department to provide optimal learning materials and study plans tailored to each student's learning style and progress. In addition, the analysis department can utilize past data and statistical information to conduct long-term learning effectiveness and trend analysis. For example, the analysis unit can evaluate the effectiveness of a particular learning method based on past learning data, and use this information to improve future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual learning patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to evaluate long-term learning effectiveness and detect anomalies, thereby improving the overall reliability and effectiveness of the system.

[0032] The generation unit creates customized learning materials and study plans based on the information analyzed by the analysis unit. For example, the generation unit can use a generation AI to create optimal learning materials and study plans for each individual student. Specifically, the generation AI uses natural language processing technology to automatically generate learning materials based on each student's learning style and progress. For example, it generates materials with many diagrams and illustrations for visually-oriented students, and materials including audio explanations and podcasts for auditory-oriented students. It generates interactive materials including experiments and simulations for experiential-oriented students. Furthermore, the generation unit uses the generation AI to create customized study plans based on each student's progress. For example, it creates study plans tailored to each student's understanding and progress based on test scores and assignment submission status. This allows the generation unit to provide optimal learning materials and study plans tailored to each student's learning style and progress. Additionally, the generation unit can collect feedback from students and use it as learning data for the generation AI to continuously improve the generated learning materials and study plans. For example, it can collect feedback from students after they use the materials and use the results to improve the learning materials and study plans. Furthermore, the generation unit can combine multiple generation AIs to create more advanced teaching materials and learning plans. This allows the generation unit to consistently provide high-quality teaching materials and learning plans based on the latest information, maximizing students' learning effectiveness.

[0033] The monitoring unit monitors progress in real time based on the learning materials and learning plans created by the generation unit and provides adaptive feedback. Specifically, it uses AI to monitor students' learning progress in real time and provide adaptive feedback. For example, while students are using the learning materials, the AI ​​evaluates their learning progress and understanding in real time and provides feedback as needed. For instance, if a student repeatedly makes mistakes on a particular problem, the AI ​​provides additional learning materials or hints related to that problem. Also, if a student demonstrates a high level of understanding on a particular topic, the AI ​​provides more advanced learning materials as the next step. Furthermore, the monitoring unit can continuously track students' learning progress and evaluate long-term learning effectiveness. For example, it can analyze students' learning patterns and progress trends based on past learning data to help improve future learning plans. The monitoring unit can also use anomaly detection algorithms to detect unusual learning patterns or abnormal data and issue early warnings. This allows the monitoring unit to not only grasp the situation in real time but also to evaluate long-term learning effectiveness and detect anomalies, improving the reliability and effectiveness of the entire system. In addition, the monitoring unit can collect feedback from students and improve the entire system based on the content of that feedback. For example, the monitoring department can analyze how students responded to the feedback they received and adjust the content and timing of the feedback based on the results. This allows the monitoring department to provide optimal feedback to each student and maximize learning effectiveness.

[0034] The support department provides work support tools for teachers. For example, the support department can provide tools to assist teachers with schedule management and grade management. For example, the support department can provide work support tools using AI to reduce teachers' workload. For example, the support department can provide work support tools using AI to reduce teachers' workload. This can reduce teachers' workload.

[0035] The tutoring department provides a 24-hour AI tutoring function. For example, the tutoring department can provide a 24-hour AI tutoring function so that students can receive learning support at any time. For example, the tutoring department can use AI to provide a 24-hour AI tutoring function so that students can receive learning support at any time. For example, the tutoring department can use AI to provide a 24-hour AI tutoring function so that students can receive learning support at any time. This allows students to receive learning support at any time.

[0036] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize data collection methods that have been effective for the student in the past. For example, the data collection unit can select a data collection method that is effective for a specific time period based on the student's past learning history. For example, the data collection unit can analyze a student's past learning history and select the most efficient data collection method. This enables efficient information collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0037] The data collection unit can filter the collected learning style and progress based on the student's current learning environment and areas of interest. For example, the data collection unit can filter the information collected based on the environment in which the student is currently learning (home, library, etc.). For example, the data collection unit can prioritize the collection of relevant information based on the student's areas of interest. For example, the data collection unit can collect the most relevant information by considering the student's learning environment and areas of interest. This allows for the collection of highly relevant information by filtering information based on the learning environment and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's learning environment data into a generating AI and have the generating AI perform the information filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the student's geographical location when collecting learning style and progress. For example, if a student is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can collect optimal learning resources based on the student's geographical location. For example, the data collection unit can collect highly relevant information by considering the student's geographical location. This allows for the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location data into a generating AI and have the generating AI perform information filtering.

[0039] The data collection unit can analyze students' social media activity and collect relevant information when collecting learning styles and progress. For example, the data collection unit can identify topics of interest from students' social media activity and collect relevant information. For example, the data collection unit can analyze students' social media activity and collect optimal learning resources. For example, the data collection unit can collect highly relevant information based on students' social media activity. This means that highly relevant information can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input students' social media data into a generating AI and have the generating AI perform information filtering.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning style and progress during the analysis. For example, if the learning style requires detailed analysis, the analysis unit can provide detailed analysis results. For example, if the learning style prefers concise analysis, the analysis unit can provide concise analysis results. For example, if progress is important, the analysis unit can provide detailed analysis results. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis based on importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms during analysis depending on the learning style and progress category. For example, the analysis unit can select an appropriate analysis algorithm if the learning style is different. For example, the analysis unit can select an appropriate analysis algorithm if the progress is different. For example, the analysis unit can apply the optimal analysis algorithm depending on the learning style and progress category. This allows for the provision of highly accurate analysis results by applying an appropriate analysis algorithm according to the category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI select an analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on learning style and the submission timing of progress during the analysis. For example, the analysis unit will prioritize analysis when the submission deadline is approaching. For example, the analysis unit can prioritize analysis when learning style depends on a specific submission deadline. For example, the analysis unit can prioritize analysis when progress affects the submission deadline. By determining priorities based on submission deadlines, the analysis unit can provide analysis results at the appropriate time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of learning style and progress during the analysis. For example, the analysis unit can prioritize analysis when learning style is relevant. For example, the analysis unit can prioritize analysis when progress is relevant. For example, the analysis unit can perform analysis in the optimal order based on the relevance of learning style and progress. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The generation unit can adjust the level of detail generated when creating learning materials and study plans based on the importance of the learning style and progress. For example, if the learning style requires detailed materials, the generation unit can provide detailed materials. For example, if the learning style prefers concise materials, the generation unit can provide concise materials. For example, if progress is important, the generation unit can provide detailed materials. In this way, by adjusting the level of detail generated based on importance, appropriate learning materials and study plans can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI perform the adjustment of the level of detail generated.

[0045] The generation unit can apply different generation algorithms depending on the learning style and progress category when generating learning materials and study plans. For example, the generation unit can select an appropriate generation algorithm when the learning style is different. For example, the generation unit can select an appropriate generation algorithm when the progress is different. For example, the generation unit can apply the optimal generation algorithm depending on the learning style and progress category. This allows for the provision of highly accurate learning materials and study plans by applying an appropriate generation algorithm according to the category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI select a generation algorithm.

[0046] The generation unit can determine the generation priority of learning materials and study plans based on learning style and the submission timing of progress. For example, the generation unit can prioritize generating learning materials and study plans when the submission deadline is approaching. For example, the generation unit can prioritize generating learning materials and study plans when the learning style depends on a specific submission deadline. For example, the generation unit can prioritize generating learning materials and study plans when progress affects the submission deadline. By determining priorities based on submission deadlines, learning materials and study plans can be provided at the appropriate time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI determine the generation priority.

[0047] The generation unit can adjust the generation order of learning materials and study plans based on the relevance of learning style and progress. For example, the generation unit can prioritize generating learning materials and study plans when learning style is relevant. For example, the generation unit can prioritize generating learning materials and study plans when progress is relevant. For example, the generation unit can generate learning materials and study plans in the optimal order based on the relevance of learning style and progress. This makes it possible to provide learning materials and study plans efficiently by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0048] The monitoring unit can improve the accuracy of monitoring by considering the interrelationship between learning style and progress during monitoring. For example, the monitoring unit can analyze the interrelationship between learning style and progress and set optimal monitoring criteria. The monitoring unit can improve the accuracy of monitoring by considering the interrelationship between learning style and progress. For example, the monitoring unit can select the optimal monitoring method based on the interrelationship between learning style and progress. This improves the accuracy of monitoring by considering the interrelationship. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student learning data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0049] The monitoring unit can perform monitoring while considering the attribute information of the person submitting the learning style and progress. For example, the monitoring unit can perform monitoring while considering attribute information such as the student's age and gender. For example, the monitoring unit can perform monitoring based on attribute information such as the student's learning history and grades. For example, the monitoring unit can select the optimal monitoring method by considering the student's attribute information. This makes it possible to perform more appropriate monitoring by considering attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input student attribute data into a generating AI and have the generating AI perform adjustments to the monitoring.

[0050] The monitoring unit can perform monitoring while considering the geographical distribution of learning styles and progress. For example, the monitoring unit can select the optimal monitoring method by considering the geographical distribution of students. For example, the monitoring unit can provide highly relevant monitoring results based on the geographical distribution of students. For example, the monitoring unit can improve the accuracy of monitoring by considering the geographical distribution of students. This makes it possible to perform highly relevant monitoring by considering geographical distribution. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input students' geographical data into a generating AI and have the generating AI perform adjustments to the monitoring.

[0051] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on learning style and progress during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature related to learning style and progress. For example, the monitoring unit can select the optimal monitoring method based on literature related to learning style and progress. For example, the monitoring unit can improve the accuracy of monitoring by considering literature related to learning style and progress. As a result, the accuracy of monitoring is improved by referring to relevant literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data into a generating AI and have the generating AI perform adjustments to the monitoring.

[0052] The support department can select the optimal support method by referring to the teacher's past work history when providing work support tools. For example, the support department can select the optimal support method based on the teacher's past work history. For example, the support department can select an effective support method from the teacher's past work history. For example, the support department can provide the optimal work support tool by referring to the teacher's past work history. This allows the optimal support method to be selected by referring to past work history. Some or all of the above processes in the support department may be performed using AI, for example, or without using AI. For example, the support department can input the teacher's work history data into a generating AI and have the generating AI perform the selection of a support method.

[0053] The support department can select the optimal support method by considering the teacher's device information when providing work support tools. For example, if the teacher is using a smartphone, the support department can provide work support tools that are sized to fit the screen. For example, if the teacher is using a tablet, the support department can provide work support tools optimized for a larger screen. For example, if the teacher is using a desktop computer, the support department can provide detailed work support tools. This allows the optimal support method to be selected by considering the device information. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input the teacher's device information into a generating AI and have the generating AI select the support method.

[0054] The tutoring department can select the optimal support method by referring to the student's past learning history when providing tutoring functions. For example, the tutoring department can select the optimal support method based on the student's past learning history. For example, the tutoring department can select an effective support method from the student's past learning history. For example, the tutoring department can provide the optimal tutoring function by referring to the student's past learning history. This allows for the selection of the optimal support method by referring to past learning history. Some or all of the above processing in the tutoring department may be performed using AI, for example, or without AI. For example, the tutoring department can input the student's learning history data into a generating AI and have the generating AI perform the selection of a support method.

[0055] The tutoring department can select the optimal support method by considering the student's device information when providing tutoring functions. For example, if a student is using a smartphone, the tutoring department can provide tutoring functions that are adapted to the screen size. For example, if a student is using a tablet, the tutoring department can provide tutoring functions optimized for a larger screen. For example, if a student is using a desktop, the tutoring department can provide detailed tutoring functions. This allows the selection of the optimal support method by considering device information. Some or all of the above processing in the tutoring department may be performed using AI, for example, or without AI. For example, the tutoring department can input the student's device information into a generating AI and have the generating AI select the support method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0058] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0059] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0060] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0061] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0062] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0063] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0064] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0065] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0066] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

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

[0068] Step 1: The data collection unit collects information on students' learning styles and progress. The data collection unit can collect information on students' learning styles and progress using, for example, questionnaires or sensor data. The data collection unit can classify students' learning styles into categories such as visual, auditory, and experiential, and collect information based on these classifications. It can also evaluate students' progress using test scores, assignment submission status, etc., and collect information based on these evaluations. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected information and performs analysis to create optimal learning materials and study plans for each student. This allows for the evaluation of students' learning styles and progress. Step 3: The generation unit creates customized learning materials and study plans based on the information analyzed by the analysis unit. For example, the generation unit can use generation AI to create optimal learning materials and study plans for each individual student. This allows for the provision of customized learning materials and study plans based on each student's learning style and progress. Step 4: The monitoring unit monitors progress in real time based on the learning materials and learning plans created by the generation unit and provides adaptive feedback. The monitoring unit can, for example, use AI to monitor students' learning progress in real time and provide adaptive feedback.

[0069] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that utilizes generative AI to create and provide customized learning materials and learning plans tailored to each student's learning style and progress. The learning support system collects the student's learning style and progress and inputs it into the generative AI. The generative AI analyzes the collected information and creates optimal learning materials and learning plans for each student. The generated learning materials and learning plans are provided to the student, and the student proceeds with their learning accordingly. Furthermore, the learning support system monitors the student's learning progress in real time and provides adaptive feedback. For example, the learning support system collects the student's learning style and progress. For example, the learning support system collects the student's learning style and progress and inputs it into the generative AI. The generative AI analyzes the collected information and creates optimal learning materials and learning plans for each student. The generated learning materials and learning plans are provided to the student, and the student proceeds with their learning accordingly. Furthermore, the learning support system monitors the student's learning progress in real time and provides adaptive feedback. As a result, the learning support system provides an effective learning environment tailored to the needs of each student, improving the quality of education and eliminating educational disparities. Furthermore, the learning support system also provides operational support tools for teachers, reducing their workload. In addition, the learning support system offers a 24 / 7 AI tutor function, allowing students to receive learning support anytime. This enables the learning support system to provide customized learning materials and plans tailored to each student's learning style and progress.

[0070] The learning support system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a monitoring unit. The collection unit collects students' learning styles and progress. The collection unit can, for example, use questionnaires or sensor data to collect students' learning styles and progress. The collection unit can, for example, classify students' learning styles into visual, auditory, experiential, etc., and collect information based on that. The collection unit can, for example, evaluate students' progress using test scores or assignment submission status, and collect information based on that. The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, performs analysis based on the collected information to create optimal learning materials and learning plans for each individual student. The analysis unit can, for example, use AI to analyze the collected information and evaluate students' learning styles and progress. The analysis unit can, for example, evaluate students' learning styles and progress based on the collected information and create customized learning materials and learning plans based on that. The generation unit creates customized learning materials and learning plans based on the information analyzed by the analysis unit. The generation unit can, for example, use generation AI to create optimal learning materials and study plans for each individual student. The generation unit can, for example, use generation AI to create customized learning materials and study plans based on the student's learning style and progress. The generation unit can, for example, use generation AI to create customized learning materials and study plans based on the student's learning style and progress. The monitoring unit monitors progress in real time based on the learning materials and study plans created by the generation unit and provides adaptive feedback. The monitoring unit can, for example, use AI to monitor the student's learning progress in real time and provide adaptive feedback. The monitoring unit can, for example, use AI to monitor the student's learning progress in real time and provide adaptive feedback.As a result, the learning support system according to this embodiment can provide customized learning materials and learning plans tailored to each student's learning style and progress.

[0071] The data collection unit collects information on students' learning styles and progress. For example, the unit can use questionnaires and sensor data to gather information on students' learning styles and progress. Specifically, questionnaires are conducted via online forms or mobile apps, allowing for detailed inquiries about students' learning habits, preferred learning methods, and motivation. Sensor data is acquired, for example, from cameras, microphones, and accelerometers installed on devices used by students. This allows for real-time monitoring of students' posture, concentration levels, and speech during learning. The data collection unit integrates this data and classifies students' learning styles into categories such as visual, auditory, and experiential. Visual learners benefit from materials that heavily utilize videos and diagrams, while auditory learners benefit from audio commentaries and podcasts. Experiential learners are suited to interactive materials that include experiments and simulations. Furthermore, the data collection unit evaluates students' progress based on test scores and assignment submission status. For example, it can automatically collect online test results and assignment submission status to understand students' comprehension and progress in real time. This allows the data collection unit to collect detailed data on each student's learning style and progress, and provide it to the analysis and generation units. The data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0072] The analysis department analyzes the information collected by the data collection department. For example, the analysis department uses the collected information to create optimal learning materials and study plans for each student. Specifically, it uses AI to analyze the collected information and evaluate students' learning styles and progress. The AI ​​uses machine learning algorithms to analyze students' learning patterns and tendencies and identify the optimal learning methods. For example, it can be found that visually oriented students are effectively taught using materials that heavily utilize videos and diagrams. Auditory oriented students benefit from audio explanations and podcasts, while experiential students are suited to interactive materials that include experiments and simulations. Furthermore, the analysis department evaluates students' progress based on the collected information and creates customized learning materials and study plans accordingly. For example, it analyzes test scores and assignment submission status to evaluate students' understanding and progress. This allows the analysis department to provide optimal learning materials and study plans tailored to each student's learning style and progress. In addition, the analysis department can utilize past data and statistical information to conduct long-term learning effectiveness and trend analysis. For example, the analysis unit can evaluate the effectiveness of a particular learning method based on past learning data, and use this information to improve future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual learning patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to evaluate long-term learning effectiveness and detect anomalies, thereby improving the overall reliability and effectiveness of the system.

[0073] The generation unit creates customized learning materials and study plans based on the information analyzed by the analysis unit. For example, the generation unit can use a generation AI to create optimal learning materials and study plans for each individual student. Specifically, the generation AI uses natural language processing technology to automatically generate learning materials based on each student's learning style and progress. For example, it generates materials with many diagrams and illustrations for visually-oriented students, and materials including audio explanations and podcasts for auditory-oriented students. It generates interactive materials including experiments and simulations for experiential-oriented students. Furthermore, the generation unit uses the generation AI to create customized study plans based on each student's progress. For example, it creates study plans tailored to each student's understanding and progress based on test scores and assignment submission status. This allows the generation unit to provide optimal learning materials and study plans tailored to each student's learning style and progress. Additionally, the generation unit can collect feedback from students and use it as learning data for the generation AI to continuously improve the generated learning materials and study plans. For example, it can collect feedback from students after they use the materials and use the results to improve the learning materials and study plans. Furthermore, the generation unit can combine multiple generation AIs to create more advanced teaching materials and learning plans. This allows the generation unit to consistently provide high-quality teaching materials and learning plans based on the latest information, maximizing students' learning effectiveness.

[0074] The monitoring unit monitors progress in real time based on the learning materials and learning plans created by the generation unit and provides adaptive feedback. Specifically, it uses AI to monitor students' learning progress in real time and provide adaptive feedback. For example, while students are using the learning materials, the AI ​​evaluates their learning progress and understanding in real time and provides feedback as needed. For instance, if a student repeatedly makes mistakes on a particular problem, the AI ​​provides additional learning materials or hints related to that problem. Also, if a student demonstrates a high level of understanding on a particular topic, the AI ​​provides more advanced learning materials as the next step. Furthermore, the monitoring unit can continuously track students' learning progress and evaluate long-term learning effectiveness. For example, it can analyze students' learning patterns and progress trends based on past learning data to help improve future learning plans. The monitoring unit can also use anomaly detection algorithms to detect unusual learning patterns or abnormal data and issue early warnings. This allows the monitoring unit to not only grasp the situation in real time but also to evaluate long-term learning effectiveness and detect anomalies, improving the reliability and effectiveness of the entire system. In addition, the monitoring unit can collect feedback from students and improve the entire system based on the content of that feedback. For example, the monitoring department can analyze how students responded to the feedback they received and adjust the content and timing of the feedback based on the results. This allows the monitoring department to provide optimal feedback to each student and maximize learning effectiveness.

[0075] The support department provides work support tools for teachers. For example, the support department can provide tools to assist teachers with schedule management and grade management. For example, the support department can provide work support tools using AI to reduce teachers' workload. For example, the support department can provide work support tools using AI to reduce teachers' workload. This can reduce teachers' workload.

[0076] The tutoring department provides a 24-hour AI tutoring function. For example, the tutoring department can provide a 24-hour AI tutoring function so that students can receive learning support at any time. For example, the tutoring department can use AI to provide a 24-hour AI tutoring function so that students can receive learning support at any time. For example, the tutoring department can use AI to provide a 24-hour AI tutoring function so that students can receive learning support at any time. This allows students to receive learning support at any time.

[0077] The data collection unit can estimate a student's emotions and adjust the timing of data collection regarding their learning style and progress based on the estimated emotions. For example, if a student is stressed, the data collection unit can delay the collection timing to collect information when the student is relaxed. For example, if a student is focused, the data collection unit can advance the collection timing to collect information when the student is highly focused. For example, if a student is tired, the data collection unit can adjust the collection timing to collect information after a break. By adjusting the collection timing according to the student's emotions, more appropriate information can be collected. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize data collection methods that have been effective for the student in the past. For example, the data collection unit can select a data collection method that is effective for a specific time period based on the student's past learning history. For example, the data collection unit can analyze a student's past learning history and select the most efficient data collection method. This enables efficient information collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's past learning history data into a generating AI and have the generating AI select the optimal data collection method.

[0079] The data collection unit can filter the collected learning style and progress based on the student's current learning environment and areas of interest. For example, the data collection unit can filter the information collected based on the environment in which the student is currently learning (home, library, etc.). For example, the data collection unit can prioritize the collection of relevant information based on the student's areas of interest. For example, the data collection unit can collect the most relevant information by considering the student's learning environment and areas of interest. This allows for the collection of highly relevant information by filtering information based on the learning environment and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's learning environment data into a generating AI and have the generating AI perform the information filtering.

[0080] The data collection unit can estimate a student's emotions and determine the priority of information to collect based on the estimated emotions. For example, if a student is excited, the data collection unit may prioritize collecting information that is of interest. For example, if a student is relaxed, the data collection unit may prioritize collecting detailed information. For example, if a student is stressed, the data collection unit may prioritize collecting concise information. This allows for more effective information collection by prioritizing information according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can prioritize the collection of highly relevant information by considering the student's geographical location when collecting learning style and progress. For example, if a student is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can collect optimal learning resources based on the student's geographical location. For example, the data collection unit can collect highly relevant information by considering the student's geographical location. This allows for the collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location data into a generating AI and have the generating AI perform information filtering.

[0082] The data collection unit can analyze students' social media activity and collect relevant information when collecting learning styles and progress. For example, the data collection unit can identify topics of interest from students' social media activity and collect relevant information. For example, the data collection unit can analyze students' social media activity and collect optimal learning resources. For example, the data collection unit can collect highly relevant information based on students' social media activity. This means that highly relevant information can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input students' social media data into a generating AI and have the generating AI perform information filtering.

[0083] The analysis unit can estimate the student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the student is relaxed, the analysis unit can provide detailed analysis results. For example, if the student is tense, the analysis unit can provide concise and easily understandable analysis results. For example, if the student is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the student's emotions, more easily understandable analysis results 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the student's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning style and progress during the analysis. For example, if the learning style requires detailed analysis, the analysis unit can provide detailed analysis results. For example, if the learning style prefers concise analysis, the analysis unit can provide concise analysis results. For example, if progress is important, the analysis unit can provide detailed analysis results. In this way, appropriate analysis results can be provided by adjusting the level of detail of the analysis based on importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms during analysis depending on the learning style and progress category. For example, the analysis unit can select an appropriate analysis algorithm if the learning style is different. For example, the analysis unit can select an appropriate analysis algorithm if the progress is different. For example, the analysis unit can apply the optimal analysis algorithm depending on the learning style and progress category. This allows for the provision of highly accurate analysis results by applying an appropriate analysis algorithm according to the category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI select an analysis algorithm.

[0086] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the student is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the student is relaxed, the analysis unit can provide a detailed analysis result. For example, if the student is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the student's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the student's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can determine the priority of analysis based on learning style and the submission timing of progress during the analysis. For example, the analysis unit will prioritize analysis when the submission deadline is approaching. For example, the analysis unit can prioritize analysis when learning style depends on a specific submission deadline. For example, the analysis unit can prioritize analysis when progress affects the submission deadline. By determining priorities based on submission deadlines, the analysis unit can provide analysis results at the appropriate time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI determine the priority of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of learning style and progress during the analysis. For example, the analysis unit can prioritize analysis when learning style is relevant. For example, the analysis unit can prioritize analysis when progress is relevant. For example, the analysis unit can perform analysis in the optimal order based on the relevance of learning style and progress. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student learning data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0089] The generation unit can estimate a student's emotions and adjust the presentation of learning materials and study plans based on the estimated emotions. For example, if a student is relaxed, the generation unit can provide detailed learning materials and study plans. If a student is nervous, the generation unit can provide concise and visually appealing learning materials and study plans. If a student is excited, the generation unit can provide visually stimulating learning materials and study plans. By adjusting the presentation according to the student's emotions, more effective learning materials and study plans can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input student facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The generation unit can adjust the level of detail generated when creating learning materials and study plans based on the importance of the learning style and progress. For example, if the learning style requires detailed materials, the generation unit can provide detailed materials. For example, if the learning style prefers concise materials, the generation unit can provide concise materials. For example, if progress is important, the generation unit can provide detailed materials. In this way, by adjusting the level of detail generated based on importance, appropriate learning materials and study plans can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI perform the adjustment of the level of detail generated.

[0091] The generation unit can apply different generation algorithms depending on the learning style and progress category when generating learning materials and study plans. For example, the generation unit can select an appropriate generation algorithm when the learning style is different. For example, the generation unit can select an appropriate generation algorithm when the progress is different. For example, the generation unit can apply the optimal generation algorithm depending on the learning style and progress category. This allows for the provision of highly accurate learning materials and study plans by applying an appropriate generation algorithm according to the category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI select a generation algorithm.

[0092] The generation unit can estimate a student's emotions and adjust the length of learning materials and study plans based on the estimated emotions. For example, if a student is in a hurry, the generation unit can provide short, concise materials and study plans. For example, if a student is relaxed, the generation unit can provide detailed materials and study plans. For example, if a student is excited, the generation unit can provide visually stimulating materials and study plans. This allows for the provision of appropriate materials and study plans by adjusting the length of materials and study plans according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The generation unit can determine the generation priority of learning materials and study plans based on learning style and the submission timing of progress. For example, the generation unit can prioritize generating learning materials and study plans when the submission deadline is approaching. For example, the generation unit can prioritize generating learning materials and study plans when the learning style depends on a specific submission deadline. For example, the generation unit can prioritize generating learning materials and study plans when progress affects the submission deadline. By determining priorities based on submission deadlines, learning materials and study plans can be provided at the appropriate time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI determine the generation priority.

[0094] The generation unit can adjust the generation order of learning materials and study plans based on the relevance of learning style and progress. For example, the generation unit can prioritize generating learning materials and study plans when learning style is relevant. For example, the generation unit can prioritize generating learning materials and study plans when progress is relevant. For example, the generation unit can generate learning materials and study plans in the optimal order based on the relevance of learning style and progress. This makes it possible to provide learning materials and study plans efficiently by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0095] The monitoring unit can estimate the student's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the student is relaxed, the monitoring unit can set detailed monitoring criteria. For example, if the student is tense, the monitoring unit can set concise monitoring criteria. For example, if the student is excited, the monitoring unit can set visually stimulating monitoring criteria. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The monitoring unit can improve the accuracy of monitoring by considering the interrelationship between learning style and progress during monitoring. For example, the monitoring unit can analyze the interrelationship between learning style and progress and set optimal monitoring criteria. The monitoring unit can improve the accuracy of monitoring by considering the interrelationship between learning style and progress. For example, the monitoring unit can select the optimal monitoring method based on the interrelationship between learning style and progress. This improves the accuracy of monitoring by considering the interrelationship. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input student learning data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0097] The monitoring unit can perform monitoring while considering the attribute information of the person submitting the learning style and progress. For example, the monitoring unit can perform monitoring while considering attribute information such as the student's age and gender. For example, the monitoring unit can perform monitoring based on attribute information such as the student's learning history and grades. For example, the monitoring unit can select the optimal monitoring method by considering the student's attribute information. This makes it possible to perform more appropriate monitoring by considering attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input student attribute data into a generating AI and have the generating AI perform adjustments to the monitoring.

[0098] The monitoring unit can estimate the student's emotions and adjust the order in which the monitoring results are displayed based on the estimated emotions. For example, if the student is relaxed, the monitoring unit may prioritize displaying detailed monitoring results. For example, if the student is tense, the monitoring unit may prioritize displaying concise monitoring results. For example, if the student is excited, the monitoring unit may prioritize displaying visually stimulating monitoring results. By adjusting the display order according to the student's emotions, more effective monitoring results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the student's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0099] The monitoring unit can perform monitoring while considering the geographical distribution of learning styles and progress. For example, the monitoring unit can select the optimal monitoring method by considering the geographical distribution of students. For example, the monitoring unit can provide highly relevant monitoring results based on the geographical distribution of students. For example, the monitoring unit can improve the accuracy of monitoring by considering the geographical distribution of students. This makes it possible to perform highly relevant monitoring by considering geographical distribution. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input students' geographical data into a generating AI and have the generating AI perform adjustments to the monitoring.

[0100] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on learning style and progress during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature related to learning style and progress. For example, the monitoring unit can select the optimal monitoring method based on literature related to learning style and progress. For example, the monitoring unit can improve the accuracy of monitoring by considering literature related to learning style and progress. As a result, the accuracy of monitoring is improved by referring to relevant literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data into a generating AI and have the generating AI perform adjustments to the monitoring.

[0101] The support unit can estimate the teacher's emotions and adjust the way it provides work support tools based on the estimated emotions. For example, if the teacher is stressed, the support unit can provide simple work support tools. For example, if the teacher is relaxed, the support unit can provide detailed work support tools. For example, if the teacher is in a hurry, the support unit can provide work support tools that can be used quickly. This allows for more effective work support by adjusting the delivery method according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input teacher facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The support department can select the optimal support method by referring to the teacher's past work history when providing work support tools. For example, the support department can select the optimal support method based on the teacher's past work history. For example, the support department can select an effective support method from the teacher's past work history. For example, the support department can provide the optimal work support tool by referring to the teacher's past work history. This allows the optimal support method to be selected by referring to past work history. Some or all of the above processes in the support department may be performed using AI, for example, or without using AI. For example, the support department can input the teacher's work history data into a generating AI and have the generating AI perform the selection of a support method.

[0103] The support unit can estimate a teacher's emotions and prioritize work support tools based on the estimated emotions. For example, if a teacher is stressed, the support unit can prioritize providing important work support tools. For example, if a teacher is relaxed, the support unit can prioritize providing detailed work support tools. For example, if a teacher is in a hurry, the support unit can prioritize providing work support tools that can be used quickly. This allows for more effective work support by prioritizing according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input teacher facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The support department can select the optimal support method by considering the teacher's device information when providing work support tools. For example, if the teacher is using a smartphone, the support department can provide work support tools that are sized to fit the screen. For example, if the teacher is using a tablet, the support department can provide work support tools optimized for a larger screen. For example, if the teacher is using a desktop computer, the support department can provide detailed work support tools. This allows the optimal support method to be selected by considering the device information. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input the teacher's device information into a generating AI and have the generating AI select the support method.

[0105] The tutoring unit can estimate a student's emotions and adjust how it provides tutoring functions based on the estimated emotions. For example, if a student is relaxed, the tutoring unit can provide detailed tutoring functions. If a student is nervous, the tutoring unit can provide concise and easy-to-understand tutoring functions. If a student is excited, the tutoring unit can provide visually stimulating tutoring functions. By adjusting the delivery method according to the student's emotions, more effective tutoring functions 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 tutoring unit may be performed using AI, for example, or without AI. For example, the tutoring unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The tutoring department can select the optimal support method by referring to the student's past learning history when providing tutoring functions. For example, the tutoring department can select the optimal support method based on the student's past learning history. For example, the tutoring department can select an effective support method from the student's past learning history. For example, the tutoring department can provide the optimal tutoring function by referring to the student's past learning history. This allows for the selection of the optimal support method by referring to past learning history. Some or all of the above processing in the tutoring department may be performed using AI, for example, or without AI. For example, the tutoring department can input the student's learning history data into a generating AI and have the generating AI perform the selection of a support method.

[0107] The tutoring unit can estimate a student's emotions and prioritize tutoring functions based on the estimated emotions. For example, if a student is relaxed, the tutoring unit can prioritize detailed tutoring functions. If a student is nervous, the tutoring unit can prioritize concise tutoring functions. If a student is excited, the tutoring unit can prioritize visually stimulating tutoring functions. By prioritizing according to the student's emotions, more effective tutoring functions can be provided. 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 tutoring unit may be performed using AI or not. For example, the tutoring unit can input student facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The tutoring department can select the optimal support method by considering the student's device information when providing tutoring functions. For example, if a student is using a smartphone, the tutoring department can provide tutoring functions that are adapted to the screen size. For example, if a student is using a tablet, the tutoring department can provide tutoring functions optimized for a larger screen. For example, if a student is using a desktop, the tutoring department can provide detailed tutoring functions. This allows the selection of the optimal support method by considering device information. Some or all of the above processing in the tutoring department may be performed using AI, for example, or without AI. For example, the tutoring department can input the student's device information into a generating AI and have the generating AI select the support method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0111] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0112] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0113] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0114] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0115] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0116] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0117] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0118] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0119] The learning support system can further incorporate gamification elements to enhance learning motivation based on students' learning styles and progress. For example, the data collection unit collects students' learning styles and progress, and the analysis unit analyzes the collected information to determine gamification elements suitable for each student. The generation unit then creates learning materials and study plans that include game elements tailored to each student based on the analysis results. The monitoring unit can monitor students' learning progress in real time, evaluate the effectiveness of the game elements, and provide feedback as needed. This allows the learning support system to increase students' motivation and provide a more effective learning environment.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects information on students' learning styles and progress. The data collection unit can collect information on students' learning styles and progress using, for example, questionnaires or sensor data. The data collection unit can classify students' learning styles into categories such as visual, auditory, and experiential, and collect information based on these classifications. It can also evaluate students' progress using test scores, assignment submission status, etc., and collect information based on these evaluations. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit, for example, uses AI to analyze the collected information and performs analysis to create optimal learning materials and study plans for each student. This allows for the evaluation of students' learning styles and progress. Step 3: The generation unit creates customized learning materials and study plans based on the information analyzed by the analysis unit. For example, the generation unit can use generation AI to create optimal learning materials and study plans for each individual student. This allows for the provision of customized learning materials and study plans based on each student's learning style and progress. Step 4: The monitoring unit monitors progress in real time based on the learning materials and learning plans created by the generation unit and provides adaptive feedback. The monitoring unit can, for example, use AI to monitor students' learning progress in real time and provide adaptive feedback.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, monitoring unit, support unit, and tutor unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect students' learning styles and progress, and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to create optimal learning materials and learning plans for each student. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates customized learning materials and learning plans based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14, and monitors students' learning progress in real time based on the generated learning materials and learning plans, and provides adaptive feedback. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides work support tools for teachers. The tutor unit is implemented, for example, by the control unit 46A of the smart device 14, and provides an AI tutor function that is available 24 hours a day. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The 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.

[0130] 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.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 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.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the 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.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 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.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, monitoring unit, support unit, and tutor unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect students' learning styles and progress, and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to create optimal learning materials and learning plans for each student. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates customized learning materials and learning plans based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214, and monitors students' learning progress in real time based on the generated learning materials and learning plans, and provides adaptive feedback. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides work support tools for teachers. The tutor unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides a 24-hour AI tutor function. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The 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.

[0146] 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.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, monitoring unit, support unit, and tutor unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect students' learning styles and progress, and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to create optimal learning materials and learning plans for each student. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates customized learning materials and learning plans based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the headset terminal 314, and monitors students' learning progress in real time based on the generated learning materials and learning plans, and provides adaptive feedback. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides work support tools for teachers. The tutor unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and provides an AI tutor function that is available 24 hours a day. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, monitoring unit, support unit, and tutor unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect students' learning styles and progress, and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to create optimal learning materials and learning plans for each student. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates customized learning materials and learning plans based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414, and monitors students' learning progress in real time based on the generated learning materials and learning plans, and provides adaptive feedback. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides work support tools for teachers. The tutor unit is implemented, for example, by the control unit 46A of the robot 414, and provides an AI tutor function that is available 24 hours a day. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The collection department collects information on students' learning styles and progress, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that creates customized teaching materials and learning plans based on the information analyzed by the analysis unit, The system includes a monitoring unit that monitors progress in real time based on the teaching materials and learning plans created by the generation unit and provides adaptive feedback. A system characterized by the following features. (Note 2) The department includes a support division that provides work support tools for teachers. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a tutoring unit that provides 24-hour AI tutoring functionality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of collecting learning styles and progress data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information on learning styles and progress, filter the data based on students' current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information on learning styles and progress, the system prioritizes collecting highly relevant information by considering students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information on learning styles and progress, analyze students' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate the students' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of learning style and progress. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the learning style and progress category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the learning style and the timing of progress submissions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationship between learning style and progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate students' emotions and adjust the presentation of teaching materials and learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating learning materials and study plans, adjust the level of detail based on the importance of learning style and progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating learning materials and study plans, different generation algorithms are applied depending on the learning style and progress category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates students' emotions and adjusts the length of learning materials and study plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating learning materials and study plans, prioritize their creation based on the learning style and the timing of progress updates. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating learning materials and study plans, the generation order is adjusted based on the relevance of learning style and progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, Estimate students' emotions and adjust monitoring criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, we improve the accuracy of monitoring by considering the interrelationship between learning style and progress. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, During monitoring, the system takes into account the learner's learning style and attribute information regarding their progress. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, The system estimates students' emotions and adjusts the order in which monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, consider the geographical distribution of learning styles and progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, refer to relevant literature on learning styles and progress to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, The system estimates teachers' emotions and adjusts how work support tools are provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned support unit, When providing business support tools, the optimal support method is selected by referring to the teacher's past work history. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned support unit, It estimates teachers' emotions and prioritizes work support tools based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned support unit, When providing business support tools, the optimal support method will be selected considering the teacher's device information. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned tutor unit is, The system estimates students' emotions and adjusts how tutoring is provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned tutor unit is, When providing the tutoring function, the system selects the most suitable support method by referring to the student's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned tutor unit is, It estimates students' emotions and prioritizes tutoring functions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned tutor unit is, When providing the tutoring function, the optimal support method will be selected considering the student's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information on students' learning styles and progress, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that creates customized teaching materials and learning plans based on the information analyzed by the analysis unit, The system includes a monitoring unit that monitors progress in real time based on the teaching materials and learning plans created by the generation unit and provides adaptive feedback. A system characterized by the following features.

2. The department includes a support division that provides work support tools for teachers. The system according to feature 1.

3. It features a tutor unit that provides 24-hour AI tutoring functionality. The system according to feature 1.

4. The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of collecting learning styles and progress data based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting information on learning styles and progress, filter the data based on students' current learning environment and areas of interest. The system according to feature 1.

7. The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting information on learning styles and progress, the system prioritizes collecting highly relevant information by considering students' geographical location. The system according to feature 1.

9. The aforementioned collection unit is When collecting information on learning styles and progress, analyze students' social media activity and gather relevant information. The system according to feature 1.

10. The aforementioned analysis unit, We estimate the students' emotions and adjust the representation of the analysis based on the estimated emotions. The system according to feature 1.

Images