system

Abstract

The system according to this embodiment aims to analyze the learner's strengths and weaknesses and provide an optimal learning program. [Solution] The system according to the embodiment comprises an analysis unit, a learning program construction unit, a visual support unit, a feedback unit, and a progress management unit. The analysis unit analyzes the learner's strengths and weaknesses. The learning program construction unit constructs a learning program suitable for the learner based on the results analyzed by the analysis unit. The visual support unit creates visual diagrams and images based on the learning program constructed by the learning program construction unit. The feedback unit provides immediate feedback based on the visual diagrams and images created by the visual support unit. The progress management unit performs autonomous progress management based on the feedback provided by the feedback unit.

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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the strengths and weaknesses of learners have not been sufficiently analyzed to provide an optimal learning program, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the strengths and weaknesses of learners and provide an optimal learning program.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a learning program construction unit, a visual support unit, a feedback unit, and a progress management unit. The analysis unit analyzes the learner's strengths and weaknesses. The learning program construction unit constructs a learning program suitable for the learner based on the results analyzed by the analysis unit. The visual support unit creates visual diagrams and images based on the learning program constructed by the learning program construction unit. The feedback unit provides immediate feedback based on the visual diagrams and images created by the visual support unit. The progress management unit performs autonomous progress management based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the learner's strengths and weaknesses and provide an optimal learning program. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a learning support system that supports a wide range of subjects such as English, mathematics, physics, chemistry, and history, as well as qualification learning for working adults (such as IT Passport and administrative scrivener). In this learning support system, the learner selects the subject or qualification they wish to study. Next, the AI ​​analyzes the learner's past learning data and test results to identify the learner's strengths and weaknesses. For example, if the learner is good at English grammar but weak in listening, the system will create a learning program that focuses on listening. The generating AI automatically creates diagrams and images for particularly difficult parts to understand, providing support to deepen visual understanding. For example, when explaining the concept of mechanics in physics, it generates a vector diagram of forces to allow the learner to understand intuitively. Furthermore, a real-time feedback function checks the correctness of the learner's answers immediately after they solve a problem and provides detailed explanations for incorrect answers. This allows the learner to deepen their understanding on the spot. The autonomous progress management function constantly monitors the learner's progress and adjusts the learning plan as needed. For example, if progress is behind schedule, the learning plan is reviewed and advice is provided to help the learner proceed with learning more efficiently. This system targets all students, from elementary school to university, especially those preparing for exams where independent study is crucial, and working adults aiming to acquire qualifications. Learners can create an environment where they can study at their own pace, deepening their understanding of specific subjects and qualifications. Furthermore, it aims to bridge the educational gap by providing 24 / 7 support for home learning for users who find individual tutoring too expensive to continue. The learning support system analyzes learners' strengths and weaknesses, constructs optimal learning programs, facilitates visual understanding, and maximizes learning effectiveness through real-time feedback and autonomous progress management.

[0029] The learning support system according to this embodiment comprises an analysis unit, a learning program construction unit, a visual support unit, a feedback unit, and a progress management unit. The analysis unit analyzes the learner's strengths and weaknesses. For example, the analysis unit collects the learner's past learning data and test results and identifies the learner's strengths and weaknesses based on them. For example, the analysis unit can identify the learner's proficiency in English grammar and their weakness in listening comprehension. The learning program construction unit constructs a learning program suitable for the learner based on the results analyzed by the analysis unit. For example, the learning program construction unit can construct a learning program that focuses on listening comprehension. The learning program construction unit can also design a program that leverages the learner's strengths while compensating for their weaknesses. The visual support unit creates visual diagrams and images based on the learning program constructed by the learning program construction unit. For example, the visual support unit can generate a vector diagram of forces when explaining the concept of mechanics in physics. The visual support unit can also automatically create diagrams and images for particularly difficult-to-understand parts using a generation AI. The feedback unit provides immediate feedback based on visual diagrams and images created by the visual support unit. For example, the feedback unit can check the correctness of a learner's answer immediately after they solve a problem and provide a detailed explanation of any mistakes. The feedback unit also features real-time feedback capabilities, allowing learners to deepen their understanding on the spot. The progress management unit performs autonomous progress management based on the feedback provided by the feedback unit. For example, the progress management unit can constantly monitor the learner's progress and adjust the learning plan as needed. Furthermore, if progress is behind schedule, the progress management unit can revise the learning plan and provide advice to help learners proceed more efficiently. As a result, the learning support system according to this embodiment can analyze the learner's strengths and weaknesses, construct an optimal learning program, support visual understanding, and maximize learning effectiveness through real-time feedback and autonomous progress management.

[0030] The analytics department analyzes learners' strengths and weaknesses. For example, it collects learners' past learning data and test results, and uses this information to identify their strengths and weaknesses. Specifically, it retrieves data such as the learner's past test scores, learning history, and assignment submission status from a database, and statistically analyzes this data. For example, if a learner scores highly on English grammar questions but low on listening comprehension questions, the analytics department can identify the learner's strengths in grammar and weaknesses in listening comprehension. Furthermore, the analytics department can use AI to analyze learners' learning patterns and identify what learning methods are most effective for them. For example, if a learner tends to perform better when using visual learning materials, the analytics department can provide feedback to the learning program development department based on this information. This allows the analytics department to gain a detailed understanding of learners' strengths and weaknesses and contribute to the creation of individually optimized learning programs.

[0031] The Learning Program Development Department constructs learning programs tailored to learners based on the results analyzed by the Analysis Department. For example, the Learning Program Development Department can create a learning program that focuses on listening comprehension. It can also design programs that leverage learners' strengths while addressing their weaknesses. Specifically, to improve a learner's listening ability, it might create a curriculum with a large amount of listening material, along with supplementary materials to strengthen grammar and vocabulary. Furthermore, the Learning Program Development Department can use AI to analyze learners' progress and feedback in real time, dynamically adjusting the learning program. For instance, if a learner successfully completes a listening task, it might provide more challenging listening material as the next step; conversely, if they are struggling, it might add basic listening practice. In this way, the Learning Program Development Department can provide flexible learning programs that meet learners' needs, maximizing learning effectiveness.

[0032] The Visual Support Unit creates visual diagrams and images based on the learning programs built by the Learning Program Construction Unit. For example, the Visual Support Unit can generate force vector diagrams when explaining concepts in physics mechanics. Furthermore, the Visual Support Unit can automatically create diagrams and images for particularly difficult-to-understand sections using a generation AI. Specifically, by inputting a prompt such as "Create a force vector diagram" to the generation AI, the AI ​​automatically generates an appropriate diagram. In addition, the Visual Support Unit can adjust the level of detail of diagrams and images according to the learner's level of understanding. For example, it can provide simple diagrams for beginners and detailed diagrams for advanced learners. The Visual Support Unit can also create interactive diagrams and animations to make learning easier for learners to understand visually. This allows the Visual Support Unit to provide effective visual learning materials to deepen learners' understanding and improve learning effectiveness.

[0033] The feedback unit provides immediate feedback based on visual diagrams and images created by the visual support unit. For example, the feedback unit can check the correctness of a learner's answer immediately after they complete a problem and provide a detailed explanation of the incorrect parts. Specifically, when a learner submits an answer, the feedback unit analyzes it and immediately determines whether it is correct or incorrect. If it is incorrect, the feedback unit provides a detailed explanation of which parts were wrong, what the correct answer is, and why. The feedback unit also has a real-time feedback function, allowing learners to deepen their understanding on the spot. For example, it can provide additional practice problems for questions the learner answered incorrectly and allow them to practice repeatedly until they understand better. Furthermore, the feedback unit can record the learner's feedback history and track changes in the learner's level of understanding. This allows the feedback unit to support learners in effectively progressing with their learning and maximize learning effectiveness.

[0034] The Progress Management Department performs autonomous progress management based on feedback provided by the Feedback Department. For example, the Progress Management Department can continuously monitor learners' progress and adjust learning plans as needed. Specifically, it analyzes learners' learning history and feedback data to evaluate the level of progress they have achieved. For example, if a learner is behind schedule, the Progress Management Department can revise the learning plan and provide advice on how to learn more efficiently. Furthermore, the Progress Management Department can introduce achievement goals and reward systems to maintain learner motivation. For example, it can award badges or points when specific learning goals are achieved to encourage learners to work enthusiastically towards their next goals. The Progress Management Department also visualizes learners' progress, allowing learners to check their own progress. In this way, the Progress Management Department can provide support for learners to learn autonomously and maximize learning effectiveness.

[0035] The analysis unit can analyze learners' past learning data and test results to identify their strengths and weaknesses. For example, the analysis unit can collect learners' past test results and use them to identify their areas of strength and weakness. The analysis unit can also analyze learners' homework submission status and learning logs to understand their learning habits and speed. Furthermore, the analysis unit can analyze learners' error patterns to identify their weaknesses in specific areas or question formats. In this way, strengths and weaknesses can be identified by analyzing learners' past learning data and test results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input learners' past learning data into an AI, which can then analyze the data to identify strengths and weaknesses.

[0036] The learning program construction unit can construct an optimal learning program for a learner based on the strengths and weaknesses identified by the analysis unit. For example, if a learner has difficulty with listening, the learning program construction unit will construct a learning program that focuses on listening. The learning program construction unit can also design a program that leverages the learner's strengths while compensating for their weaknesses. Furthermore, the learning program construction unit can flexibly adjust the program according to the learner's learning speed and habits. This allows for improved learning effectiveness by constructing an optimal learning program based on the learner's strengths and weaknesses. Some or all of the above processing in the learning program construction unit may be performed using AI, for example, or without AI. For example, the learning program construction unit can input the strengths and weaknesses identified by the analysis unit into an AI, which can then generate an optimal learning program.

[0037] The visual support unit can automatically create diagrams and images to help learners understand difficult concepts visually. For example, when explaining the concept of mechanics in physics, the visual support unit can generate a vector diagram of forces. It can also generate 3D models of molecules when explaining molecular structures in chemistry. Furthermore, it can generate timelines and maps when explaining historical events. By automatically creating diagrams and images, it can deepen learners' visual understanding. Some or all of the above processes in the visual support unit are performed using a generative AI. For example, the visual support unit can input difficult concepts into the generative AI, which can then generate appropriate diagrams and images.

[0038] The feedback unit can check the correctness of a learner's answer immediately after they complete a problem and provide detailed explanations for any mistakes. For example, the feedback unit can instantly determine the correctness of a problem the learner has solved and, if correct, instruct them to proceed to the next problem. If the learner makes a mistake, the feedback unit can also provide detailed explanations for the mistakes and supplementary information to deepen their understanding. Furthermore, the feedback unit can re-present similar problems to those the learner answered incorrectly, checking their level of understanding. This provides real-time feedback, allowing learners to deepen their understanding on the spot. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the learner's answer into an AI, which can then determine its correctness and generate a detailed explanation.

[0039] The progress management unit can constantly monitor learners' progress and adjust learning plans as needed. For example, the progress management unit can record learners' study time and content to understand their progress. Furthermore, if a learner's progress is behind schedule, the progress management unit can revise the learning plan and provide advice to help them learn more efficiently. Additionally, if a learner's progress is on track, the progress management unit can provide instructions for moving on to the next step. This allows for efficient learning by monitoring learners' progress and adjusting learning plans. Some or all of the above processes in the progress management unit may be performed using AI, or not. For example, the progress management unit can input learner progress data into an AI, which can then analyze the progress and adjust the learning plan.

[0040] The analysis unit can identify strengths and weaknesses with greater accuracy by analyzing learners' lifestyles and learning environments in addition to their past learning data. For example, the analysis unit can analyze learners' sleep patterns to identify optimal learning times. It can also analyze learners' learning environments (quiet places, noisy places, etc.) and suggest optimal learning environments. Furthermore, the analysis unit can analyze learners' eating habits and provide dietary advice to improve concentration. In this way, by analyzing lifestyles and learning environments, strengths and weaknesses can be identified with greater accuracy. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input learners' lifestyle data into AI, which can then analyze the data to identify strengths and weaknesses.

[0041] The analysis unit can analyze a learner's learning style and propose the optimal learning method. For example, if a learner has a visual learning style, the analysis unit can propose a learning method that makes extensive use of diagrams and images. If a learner has an auditory learning style, the analysis unit can also propose a learning method that makes extensive use of audio materials. Furthermore, if a learner has an experiential learning style, the analysis unit can propose a learning method that makes extensive use of experiments and practical exercises. By proposing the optimal learning method based on the learning style, the learning effect can be enhanced. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input learner's learning style data into AI, and the AI ​​can analyze the data and propose the optimal learning method.

[0042] The analytics unit can analyze learners' social media activity to identify learning-related interests and concerns. For example, it can analyze what learners frequently share on social media to identify areas of interest. It can also analyze the accounts learners follow to identify topics of interest. Furthermore, it can analyze the groups learners participate in on social media to identify learning-related interests and concerns. In this way, learning-related interests and concerns can be identified by analyzing social media activity. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input learners' social media data into an AI, which can then analyze the data to identify interests and concerns.

[0043] The analysis unit can analyze region-specific learning trends by considering the learner's geographical location. For example, the analysis unit can analyze the educational level of the area where the learner lives and propose an optimal learning program. The analysis unit can also adjust the learning content by considering the culture and customs of the area where the learner lives. Furthermore, the analysis unit can adjust the learning schedule by considering the climate and season of the area where the learner lives. In this way, region-specific learning trends can be analyzed by considering geographical location. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the learner's geographical location data into AI, and the AI ​​can analyze the data to identify region-specific learning trends.

[0044] The learning program construction unit can optimize the learning program schedule by considering the learner's learning pace and attention span. For example, the learning program construction unit can analyze how long a learner's attention span lasts and construct a schedule that allows them to complete their learning within that time. It can also analyze the learner's learning pace and construct a manageable schedule. Furthermore, the learning program construction unit can construct an efficient learning schedule by considering the learner's break times. In this way, by considering the learning pace and attention span, it can provide a manageable learning schedule. Some or all of the above processing in the learning program construction unit may be performed using AI or not. For example, the learning program construction unit can input learner's learning pace data into AI, and the AI ​​can analyze the data to construct an optimal schedule.

[0045] The learning program construction unit can construct customized learning programs based on the learner's goals and desired career path. For example, the learning program construction unit can identify the skills necessary for the learner's desired career path and construct a learning program based on those skills. It can also clarify the steps towards achieving the learner's goals and construct a learning program based on those steps. Furthermore, the learning program construction unit can construct learning programs that include preparation for qualification exams related to the learner's career path. This allows the unit to support learners in achieving their goals by providing customized learning programs based on their goals and desired career path. Some or all of the above-described processes in the learning program construction unit may be performed using AI or not. For example, the learning program construction unit can input the learner's goal data into AI, which can then analyze the data and construct a customized learning program.

[0046] The learning program construction unit can propose a learning program based on the learner's past learning history, referencing successful cases of similar learners. For example, the learning program construction unit can analyze the learner's past learning history and propose a learning program that has been successful for similar learners. Furthermore, the learning program construction unit can identify successful learning methods from the learner's past learning history and propose a learning program based on those methods. In addition, the learning program construction unit can propose an optimal learning program based on the learner's past learning history. This allows the learning program to be provided with the most suitable learning program by referencing successful cases based on past learning history. Some or all of the above processing in the learning program construction unit may be performed using AI, or not. For example, the learning program construction unit can input the learner's past learning history data into an AI, which can then analyze the data and propose a learning program referencing successful cases.

[0047] The learning program construction unit can provide an optimal learning program by taking into account the learner's device information. For example, if the learner is using a smartphone, the learning program construction unit can provide a learning program optimized for the smartphone. Furthermore, if the learner is using a tablet, the learning program construction unit can also provide a learning program optimized for the tablet. In addition, if the learner is using a personal computer, the learning program construction unit can also provide a learning program optimized for the personal computer. This allows the learning program to be provided as optimal as possible by considering device information. Some or all of the above-described processes in the learning program construction unit may be performed using AI, or not. For example, the learning program construction unit can input the learner's device information into the AI, which can then analyze the data to provide an optimal learning program.

[0048] The visual support unit can adjust the level of detail in diagrams and images according to the learner's level of understanding. For example, if the learner's level of understanding is low, the visual support unit's generating AI can create a diagram containing simple and basic information. If the learner's level of understanding is moderate, the visual support unit's generating AI can also create a diagram containing detailed information. Furthermore, if the learner's level of understanding is high, the visual support unit's generating AI can also create a diagram containing complex information. By adjusting the level of detail in diagrams and images according to the learner's level of understanding, more effective visual support becomes possible. Some or all of the above processing in the visual support unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the visual support unit can input learner understanding data into the generating AI, and the generating AI can analyze the data to adjust the level of detail in diagrams and images.

[0049] The visual support unit can provide different forms of visual support depending on the learner's learning style. For example, if a learner has a visual learning style, the visual support unit can provide visual support that makes extensive use of diagrams and images. Furthermore, if a learner has an auditory learning style, the visual support unit can provide videos with audio. In addition, if a learner has a tactile learning style, the visual support unit can provide interactive animations. By providing different forms of visual support according to the learner's learning style, learning effectiveness can be enhanced. Some or all of the above processing in the visual support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visual support unit can input learner learning style data into a generative AI, which can then analyze the data to provide the optimal visual support method.

[0050] The visual support unit can select the most effective visual support method based on the learner's past learning history. For example, the visual support unit can analyze the learner's past learning history and provide the most effective diagrams or images. It can also provide the most effective videos or animations based on the learner's past learning history. Furthermore, the visual support unit can select the optimal visual support method by referring to the learner's past learning history. This allows for improved learning effectiveness by selecting the most effective visual support method based on past learning history. Some or all of the above processing in the visual support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visual support unit can input the learner's past learning history data into a generative AI, which can then analyze the data and select the most effective visual support method.

[0051] The visual support unit can provide the optimal visual support method by taking into account the learner's device information. For example, if the learner is using a smartphone, the visual support unit can provide a visual support method optimized for smartphones. Furthermore, if the learner is using a tablet, the visual support unit can provide a visual support method optimized for tablets. In addition, if the learner is using a personal computer, the visual support unit can provide a visual support method optimized for personal computers. This allows the system to provide the optimal visual support method for the learner by considering device information. Some or all of the above processing in the visual support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visual support unit can input the learner's device information into a generative AI, which can then analyze the data to provide the optimal visual support method.

[0052] The feedback unit can adjust the level of detail of the feedback according to the learner's level of understanding. For example, if the learner's level of understanding is low, the feedback unit will provide basic feedback. If the learner's level of understanding is moderate, the feedback unit can also provide detailed feedback. Furthermore, if the learner's level of understanding is high, the feedback unit can provide feedback that includes specific areas for improvement. By adjusting the level of detail of the feedback according to the learner's level of understanding, more effective feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner understanding data into AI, and the AI ​​can analyze the data to adjust the level of detail of the feedback.

[0053] The feedback unit can provide different forms of feedback depending on the learner's learning style. For example, if the learner has a visual learning style, the feedback unit can provide text-based feedback. If the learner has an auditory learning style, the feedback unit can also provide audio-based feedback. Furthermore, if the learner has a tactile learning style, the feedback unit can provide interactive feedback. This allows for improved learning effectiveness by providing different forms of feedback according to the learner's learning style. Some or all of the processing described above in the feedback unit may be performed using AI, or not. For example, the feedback unit can input learner learning style data into an AI, which can then analyze the data to provide the optimal feedback method.

[0054] The feedback unit can select the most effective feedback method based on the learner's past learning history. For example, the feedback unit can analyze the learner's past learning history and provide the most effective feedback method. The feedback unit can also provide the most effective feedback format based on the learner's past learning history. Furthermore, the feedback unit can select the optimal feedback method by referring to the learner's past learning history. This allows for improved learning effectiveness by selecting the most effective feedback method based on past learning history. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the learner's past learning history data into an AI, which can then analyze the data and select the most effective feedback method.

[0055] The feedback unit can provide the optimal feedback method by taking into account the learner's device information. For example, if the learner is using a smartphone, the feedback unit can provide a feedback method optimized for smartphones. Furthermore, if the learner is using a tablet, the feedback unit can provide a feedback method optimized for tablets. In addition, if the learner is using a personal computer, the feedback unit can provide a feedback method optimized for personal computers. This allows the feedback unit to provide the optimal feedback method for the learner by considering device information. Some or all of the above processing in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input the learner's device information into an AI, which can then analyze the data to provide the optimal feedback method.

[0056] The progress management unit can optimize the progress management schedule by considering the learner's learning pace and attention span. For example, the progress management unit can analyze how long a learner's attention span lasts and construct a schedule for progress management within that time. It can also analyze the learner's learning pace and construct a manageable progress management schedule. Furthermore, the progress management unit can construct an efficient progress management schedule by considering the learner's break time. In this way, a manageable progress management schedule can be provided by considering the learning pace and attention span. Some or all of the above processes in the progress management unit may be performed using AI or not. For example, the progress management unit can input learner learning pace data into AI, and the AI ​​can analyze the data and construct an optimal schedule.

[0057] The Progress Management Unit can provide customized progress management methods based on learners' goals and desired career paths. For example, the Progress Management Unit can identify the skills necessary for a learner's desired career path and provide a progress management method based on those skills. It can also clarify the steps towards achieving the learner's goals and provide a progress management method based on those steps. Furthermore, the Progress Management Unit can provide a progress management method that includes preparation for qualification exams related to the learner's career path. This allows the unit to support learners in achieving their goals by providing customized progress management methods based on their goals and desired career paths. Some or all of the above processes in the Progress Management Unit may be performed using AI or not. For example, the Progress Management Unit can input learner goal data into AI, which can then analyze the data and provide a customized progress management method.

[0058] The progress management unit can propose progress management methods based on the learner's past learning history, referencing successful cases of similar learners. For example, the progress management unit can analyze the learner's past learning history and propose progress management methods that have worked for similar learners. Furthermore, the progress management unit can identify successful progress management methods from the learner's past learning history and propose a progress management method based on those. In addition, the progress management unit can propose the optimal progress management method based on the learner's past learning history. This allows the system to provide learners with the most suitable progress management method by referencing successful cases based on their past learning history. Some or all of the above processes in the progress management unit may be performed using AI, or not. For example, the progress management unit can input the learner's past learning history data into an AI, which can then analyze the data and propose a progress management method referencing successful cases.

[0059] The progress management unit can provide an optimal progress management method by taking into account the learner's device information. For example, if the learner is using a smartphone, the progress management unit can provide a progress management method optimized for smartphones. Furthermore, if the learner is using a tablet, the progress management unit can also provide a progress management method optimized for tablets. In addition, if the learner is using a personal computer, the progress management unit can also provide a progress management method optimized for personal computers. This allows the system to provide the learner with the most suitable progress management method by considering device information. Some or all of the above processing in the progress management unit may be performed using AI, or not. For example, the progress management unit can input the learner's device information into an AI, which can then analyze the data to provide the optimal progress management method.

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

[0061] Learning support systems can also incorporate gamification features to maintain learner motivation. For example, learners could earn points for completing specific tasks and use those points to purchase virtual items. Ranking features could also be provided, allowing learners to compete with each other and whose rankings change according to their learning progress. Furthermore, features could be added to award badges or titles to learners upon achieving goals. This can increase learner motivation and encourage continued learning.

[0062] The learning support system can also include health management features that monitor the learner's health and adjust their learning plan accordingly. For example, it can collect the learner's heart rate and sleep data and prompt them to take a break if fatigue is accumulating. It can also analyze the learner's diet and exercise data and provide advice to maintain healthy lifestyle habits. Furthermore, if the learner is experiencing stress, it can provide guidance on relaxation exercises or meditation. This can support the learner's health and enhance learning effectiveness.

[0063] Learning support systems can further provide different formats of learning content according to the learner's learning style. For example, learners with a visual learning style can be provided with content that makes extensive use of diagrams and images. Learners with an auditory learning style can be provided with audio materials and podcasts. Furthermore, learners with an experiential learning style can be provided with interactive content, including experiments and practical exercises. This allows for the provision of optimal learning support tailored to each learner's learning style.

[0064] Learning support systems can also include a dashboard function to visualize learners' progress. For example, they can display learners' progress in graphs and charts, allowing users to see at a glance which subjects or areas they are lagging behind in. They can also display the degree of achievement against the goals set by the learners and provide feedback to maintain motivation. Furthermore, they can display a list of problems and test results that learners have previously solved, allowing them to identify areas that need review. This enables learners to understand their own progress and study efficiently.

[0065] The learning support system can further analyze the learner's learning environment and suggest the optimal learning environment. For example, if the learner's learning environment is noisy, it can suggest studying in a quiet place. Similarly, if the learner's learning environment is dark, it can suggest studying in a brighter place. Furthermore, if the learner's learning environment is uncomfortable, it can provide advice on maintaining a comfortable temperature and humidity. This allows learners to study efficiently in an optimal learning environment.

[0066] The learning support system can further analyze the learner's learning history and suggest the most suitable learning method for them. For example, it can identify the most effective learning method based on past learning history and provide a learning program based on that. It can also identify areas where the learner has struggled in the past and provide a learning program that focuses on those areas. Furthermore, it can suggest an optimal learning schedule based on the learner's learning history. This allows learners to efficiently progress in their studies using methods best suited to them.

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

[0068] Step 1: The analysis department analyzes the learner's strengths and weaknesses. For example, they collect the learner's past learning data and test results, and use this to identify the learner's strengths and weaknesses. Step 2: The learning program development department constructs a learning program suitable for the learner based on the results of the analysis conducted by the analysis department. For example, they might develop a learning program that focuses on listening, designing a program that leverages the learner's strengths while addressing their weaknesses. Step 3: The Visual Support Unit creates visual diagrams and images based on the learning program built by the Learning Program Construction Unit. For example, when explaining the concept of mechanics in physics, it generates a vector diagram of forces. It can also automatically create diagrams and images for particularly difficult-to-understand parts using generation AI. Step 4: The feedback unit provides immediate feedback based on visual diagrams and images created by the visual support unit. For example, it checks whether a learner has solved a problem and provides detailed explanations for any mistakes. It also features real-time feedback functionality, allowing learners to deepen their understanding on the spot. Step 5: The progress management department performs autonomous progress management based on the feedback provided by the feedback department. For example, it continuously monitors learners' progress and adjusts the learning plan as needed. If progress is behind schedule, it reviews the learning plan and provides advice on how to proceed with learning more efficiently.

[0069] (Example of form 2) The learning support system according to an embodiment of the present invention is a learning support system that supports a wide range of subjects such as English, mathematics, physics, chemistry, and history, as well as qualification learning for working adults (such as IT Passport and administrative scrivener). In this learning support system, the learner selects the subject or qualification they wish to study. Next, the AI ​​analyzes the learner's past learning data and test results to identify the learner's strengths and weaknesses. For example, if the learner is good at English grammar but weak in listening, the system will create a learning program that focuses on listening. The generating AI automatically creates diagrams and images for particularly difficult parts to understand, providing support to deepen visual understanding. For example, when explaining the concept of mechanics in physics, it generates a vector diagram of forces to allow the learner to understand intuitively. Furthermore, a real-time feedback function checks the correctness of the learner's answers immediately after they solve a problem and provides detailed explanations for incorrect answers. This allows the learner to deepen their understanding on the spot. The autonomous progress management function constantly monitors the learner's progress and adjusts the learning plan as needed. For example, if progress is behind schedule, the learning plan is reviewed and advice is provided to help the learner proceed with learning more efficiently. This system targets all students, from elementary school to university, especially those preparing for exams where independent study is crucial, and working adults aiming to acquire qualifications. Learners can create an environment where they can study at their own pace, deepening their understanding of specific subjects and qualifications. Furthermore, it aims to bridge the educational gap by providing 24 / 7 support for home learning for users who find individual tutoring too expensive to continue. The learning support system analyzes learners' strengths and weaknesses, constructs optimal learning programs, facilitates visual understanding, and maximizes learning effectiveness through real-time feedback and autonomous progress management.

[0070] The learning support system according to this embodiment comprises an analysis unit, a learning program construction unit, a visual support unit, a feedback unit, and a progress management unit. The analysis unit analyzes the learner's strengths and weaknesses. For example, the analysis unit collects the learner's past learning data and test results and identifies the learner's strengths and weaknesses based on them. For example, the analysis unit can identify the learner's proficiency in English grammar and their weakness in listening comprehension. The learning program construction unit constructs a learning program suitable for the learner based on the results analyzed by the analysis unit. For example, the learning program construction unit can construct a learning program that focuses on listening comprehension. The learning program construction unit can also design a program that leverages the learner's strengths while compensating for their weaknesses. The visual support unit creates visual diagrams and images based on the learning program constructed by the learning program construction unit. For example, the visual support unit can generate a vector diagram of forces when explaining the concept of mechanics in physics. The visual support unit can also automatically create diagrams and images for particularly difficult-to-understand parts using a generation AI. The feedback unit provides immediate feedback based on visual diagrams and images created by the visual support unit. For example, the feedback unit can check the correctness of a learner's answer immediately after they solve a problem and provide a detailed explanation of any mistakes. The feedback unit also features real-time feedback capabilities, allowing learners to deepen their understanding on the spot. The progress management unit performs autonomous progress management based on the feedback provided by the feedback unit. For example, the progress management unit can constantly monitor the learner's progress and adjust the learning plan as needed. Furthermore, if progress is behind schedule, the progress management unit can revise the learning plan and provide advice to help learners proceed more efficiently. As a result, the learning support system according to this embodiment can analyze the learner's strengths and weaknesses, construct an optimal learning program, support visual understanding, and maximize learning effectiveness through real-time feedback and autonomous progress management.

[0071] The analytics department analyzes learners' strengths and weaknesses. For example, it collects learners' past learning data and test results, and uses this information to identify their strengths and weaknesses. Specifically, it retrieves data such as the learner's past test scores, learning history, and assignment submission status from a database, and statistically analyzes this data. For example, if a learner scores highly on English grammar questions but low on listening comprehension questions, the analytics department can identify the learner's strengths in grammar and weaknesses in listening comprehension. Furthermore, the analytics department can use AI to analyze learners' learning patterns and identify what learning methods are most effective for them. For example, if a learner tends to perform better when using visual learning materials, the analytics department can provide feedback to the learning program development department based on this information. This allows the analytics department to gain a detailed understanding of learners' strengths and weaknesses and contribute to the creation of individually optimized learning programs.

[0072] The Learning Program Development Department constructs learning programs tailored to learners based on the results analyzed by the Analysis Department. For example, the Learning Program Development Department can create a learning program that focuses on listening comprehension. It can also design programs that leverage learners' strengths while addressing their weaknesses. Specifically, to improve a learner's listening ability, it might create a curriculum with a large amount of listening material, along with supplementary materials to strengthen grammar and vocabulary. Furthermore, the Learning Program Development Department can use AI to analyze learners' progress and feedback in real time, dynamically adjusting the learning program. For instance, if a learner successfully completes a listening task, it might provide more challenging listening material as the next step; conversely, if they are struggling, it might add basic listening practice. In this way, the Learning Program Development Department can provide flexible learning programs that meet learners' needs, maximizing learning effectiveness.

[0073] The Visual Support Unit creates visual diagrams and images based on the learning programs built by the Learning Program Construction Unit. For example, the Visual Support Unit can generate force vector diagrams when explaining concepts in physics mechanics. Furthermore, the Visual Support Unit can automatically create diagrams and images for particularly difficult-to-understand sections using a generation AI. Specifically, by inputting a prompt such as "Create a force vector diagram" to the generation AI, the AI ​​automatically generates an appropriate diagram. In addition, the Visual Support Unit can adjust the level of detail of diagrams and images according to the learner's level of understanding. For example, it can provide simple diagrams for beginners and detailed diagrams for advanced learners. The Visual Support Unit can also create interactive diagrams and animations to make learning easier for learners to understand visually. This allows the Visual Support Unit to provide effective visual learning materials to deepen learners' understanding and improve learning effectiveness.

[0074] The feedback unit provides immediate feedback based on visual diagrams and images created by the visual support unit. For example, the feedback unit can check the correctness of a learner's answer immediately after they complete a problem and provide a detailed explanation of the incorrect parts. Specifically, when a learner submits an answer, the feedback unit analyzes it and immediately determines whether it is correct or incorrect. If it is incorrect, the feedback unit provides a detailed explanation of which parts were wrong, what the correct answer is, and why. The feedback unit also has a real-time feedback function, allowing learners to deepen their understanding on the spot. For example, it can provide additional practice problems for questions the learner answered incorrectly and allow them to practice repeatedly until they understand better. Furthermore, the feedback unit can record the learner's feedback history and track changes in the learner's level of understanding. This allows the feedback unit to support learners in effectively progressing with their learning and maximize learning effectiveness.

[0075] The Progress Management Department performs autonomous progress management based on feedback provided by the Feedback Department. For example, the Progress Management Department can continuously monitor learners' progress and adjust learning plans as needed. Specifically, it analyzes learners' learning history and feedback data to evaluate the level of progress they have achieved. For example, if a learner is behind schedule, the Progress Management Department can revise the learning plan and provide advice on how to learn more efficiently. Furthermore, the Progress Management Department can introduce achievement goals and reward systems to maintain learner motivation. For example, it can award badges or points when specific learning goals are achieved to encourage learners to work enthusiastically towards their next goals. The Progress Management Department also visualizes learners' progress, allowing learners to check their own progress. In this way, the Progress Management Department can provide support for learners to learn autonomously and maximize learning effectiveness.

[0076] The analysis unit can analyze learners' past learning data and test results to identify their strengths and weaknesses. For example, the analysis unit can collect learners' past test results and use them to identify their areas of strength and weakness. The analysis unit can also analyze learners' homework submission status and learning logs to understand their learning habits and speed. Furthermore, the analysis unit can analyze learners' error patterns to identify their weaknesses in specific areas or question formats. In this way, strengths and weaknesses can be identified by analyzing learners' past learning data and test results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input learners' past learning data into an AI, which can then analyze the data to identify strengths and weaknesses.

[0077] The learning program construction unit can construct an optimal learning program for a learner based on the strengths and weaknesses identified by the analysis unit. For example, if a learner has difficulty with listening, the learning program construction unit will construct a learning program that focuses on listening. The learning program construction unit can also design a program that leverages the learner's strengths while compensating for their weaknesses. Furthermore, the learning program construction unit can flexibly adjust the program according to the learner's learning speed and habits. This allows for improved learning effectiveness by constructing an optimal learning program based on the learner's strengths and weaknesses. Some or all of the above processing in the learning program construction unit may be performed using AI, for example, or without AI. For example, the learning program construction unit can input the strengths and weaknesses identified by the analysis unit into an AI, which can then generate an optimal learning program.

[0078] The visual support unit can automatically create diagrams and images to help learners understand difficult concepts visually. For example, when explaining the concept of mechanics in physics, the visual support unit can generate a vector diagram of forces. It can also generate 3D models of molecules when explaining molecular structures in chemistry. Furthermore, it can generate timelines and maps when explaining historical events. By automatically creating diagrams and images, it can deepen learners' visual understanding. Some or all of the above processes in the visual support unit are performed using a generative AI. For example, the visual support unit can input difficult concepts into the generative AI, which can then generate appropriate diagrams and images.

[0079] The feedback unit can check the correctness of a learner's answer immediately after they complete a problem and provide detailed explanations for any mistakes. For example, the feedback unit can instantly determine the correctness of a problem the learner has solved and, if correct, instruct them to proceed to the next problem. If the learner makes a mistake, the feedback unit can also provide detailed explanations for the mistakes and supplementary information to deepen their understanding. Furthermore, the feedback unit can re-present similar problems to those the learner answered incorrectly, checking their level of understanding. This provides real-time feedback, allowing learners to deepen their understanding on the spot. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the learner's answer into an AI, which can then determine its correctness and generate a detailed explanation.

[0080] The progress management unit can constantly monitor learners' progress and adjust learning plans as needed. For example, the progress management unit can record learners' study time and content to understand their progress. Furthermore, if a learner's progress is behind schedule, the progress management unit can revise the learning plan and provide advice to help them learn more efficiently. Additionally, if a learner's progress is on track, the progress management unit can provide instructions for moving on to the next step. This allows for efficient learning by monitoring learners' progress and adjusting learning plans. Some or all of the above processes in the progress management unit may be performed using AI, or not. For example, the progress management unit can input learner progress data into an AI, which can then analyze the progress and adjust the learning plan.

[0081] The analysis unit can estimate the learner's emotions and adjust the analysis method of the learning data based on the estimated learner's emotions. For example, if the learner is stressed, the analysis unit can adjust the analysis method so that the AI ​​starts with easy questions. Also, if the learner is relaxed, the analysis unit can have the AI ​​perform an analysis that includes more difficult questions. Furthermore, if the learner is focused, the analysis unit can have the AI ​​perform a detailed analysis to more accurately identify the learner's strengths and weaknesses. This allows for more appropriate analysis by adjusting the analysis method based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input learner emotion data into the AI, which can estimate the emotions and adjust the analysis method.

[0082] The analysis unit can identify strengths and weaknesses with greater accuracy by analyzing learners' lifestyles and learning environments in addition to their past learning data. For example, the analysis unit can analyze learners' sleep patterns to identify optimal learning times. It can also analyze learners' learning environments (quiet places, noisy places, etc.) and suggest optimal learning environments. Furthermore, the analysis unit can analyze learners' eating habits and provide dietary advice to improve concentration. In this way, by analyzing lifestyles and learning environments, strengths and weaknesses can be identified with greater accuracy. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input learners' lifestyle data into AI, which can then analyze the data to identify strengths and weaknesses.

[0083] The analysis unit can analyze a learner's learning style and propose the optimal learning method. For example, if a learner has a visual learning style, the analysis unit can propose a learning method that makes extensive use of diagrams and images. If a learner has an auditory learning style, the analysis unit can also propose a learning method that makes extensive use of audio materials. Furthermore, if a learner has an experiential learning style, the analysis unit can propose a learning method that makes extensive use of experiments and practical exercises. By proposing the optimal learning method based on the learning style, the learning effect can be enhanced. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input learner's learning style data into AI, and the AI ​​can analyze the data and propose the optimal learning method.

[0084] The analysis unit can estimate the learner's emotions and prioritize the analysis results based on the estimated emotions. For example, if the learner is stressed, the AI ​​may prioritize presenting analysis results for easier problems. Similarly, if the learner is relaxed, the AI ​​may prioritize presenting analysis results for more difficult problems. Furthermore, if the learner is focused, the AI ​​may prioritize presenting detailed analysis results. This allows for more appropriate learning support by prioritizing analysis results based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input learner emotion data into an AI, which can estimate emotions and determine the priority of analysis results.

[0085] The analytics unit can analyze learners' social media activity to identify learning-related interests and concerns. For example, it can analyze what learners frequently share on social media to identify areas of interest. It can also analyze the accounts learners follow to identify topics of interest. Furthermore, it can analyze the groups learners participate in on social media to identify learning-related interests and concerns. In this way, learning-related interests and concerns can be identified by analyzing social media activity. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, the analytics unit can input learners' social media data into an AI, which can then analyze the data to identify interests and concerns.

[0086] The analysis unit can analyze region-specific learning trends by considering the learner's geographical location. For example, the analysis unit can analyze the educational level of the area where the learner lives and propose an optimal learning program. The analysis unit can also adjust the learning content by considering the culture and customs of the area where the learner lives. Furthermore, the analysis unit can adjust the learning schedule by considering the climate and season of the area where the learner lives. In this way, region-specific learning trends can be analyzed by considering geographical location. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the learner's geographical location data into AI, and the AI ​​can analyze the data to identify region-specific learning trends.

[0087] The learning program construction unit can estimate the learner's emotions and adjust the content of the learning program based on the estimated emotions. For example, if the learner is stressed, the AI ​​can construct a learning program that starts with easy content. The learning program construction unit can also construct a learning program that includes more difficult content if the learner is relaxed. Furthermore, if the learner is focused, the AI ​​can construct a learning program that includes detailed content. This allows for the provision of a more appropriate learning program by adjusting the content based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning program construction unit may be performed using AI or not. For example, the learning program construction unit can input learner emotion data into the AI, which can estimate the emotions and adjust the content of the learning program.

[0088] The learning program construction unit can optimize the learning program schedule by considering the learner's learning pace and attention span. For example, the learning program construction unit can analyze how long a learner's attention span lasts and construct a schedule that allows them to complete their learning within that time. It can also analyze the learner's learning pace and construct a manageable schedule. Furthermore, the learning program construction unit can construct an efficient learning schedule by considering the learner's break times. In this way, by considering the learning pace and attention span, it can provide a manageable learning schedule. Some or all of the above processing in the learning program construction unit may be performed using AI or not. For example, the learning program construction unit can input learner's learning pace data into AI, and the AI ​​can analyze the data to construct an optimal schedule.

[0089] The learning program construction unit can construct customized learning programs based on the learner's goals and desired career path. For example, the learning program construction unit can identify the skills necessary for the learner's desired career path and construct a learning program based on those skills. It can also clarify the steps towards achieving the learner's goals and construct a learning program based on those steps. Furthermore, the learning program construction unit can construct learning programs that include preparation for qualification exams related to the learner's career path. This allows the unit to support learners in achieving their goals by providing customized learning programs based on their goals and desired career path. Some or all of the above-described processes in the learning program construction unit may be performed using AI or not. For example, the learning program construction unit can input the learner's goal data into AI, which can then analyze the data and construct a customized learning program.

[0090] The learning program construction unit can estimate the learner's emotions and determine the priority of the learning program based on the estimated emotions. For example, if the learner is stressed, the learning program construction unit can construct a learning program in which the AI ​​prioritizes easy content. Furthermore, if the learner is relaxed, the learning program construction unit can construct a learning program in which the AI ​​prioritizes more difficult content. Additionally, if the learner is focused, the learning program construction unit can construct a learning program in which the AI ​​prioritizes detailed content. This allows for more appropriate learning support by determining the priority of the learning program based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 learning program construction unit may be performed using AI or not. For example, the learning program construction unit can input learner emotion data into the AI, which can estimate the emotions and determine the priority of the learning program.

[0091] The learning program construction unit can propose a learning program based on the learner's past learning history, referencing successful cases of similar learners. For example, the learning program construction unit can analyze the learner's past learning history and propose a learning program that has been successful for similar learners. Furthermore, the learning program construction unit can identify successful learning methods from the learner's past learning history and propose a learning program based on those methods. In addition, the learning program construction unit can propose an optimal learning program based on the learner's past learning history. This allows the learning program to be provided with the most suitable learning program by referencing successful cases based on past learning history. Some or all of the above processing in the learning program construction unit may be performed using AI, or not. For example, the learning program construction unit can input the learner's past learning history data into an AI, which can then analyze the data and propose a learning program referencing successful cases.

[0092] The learning program construction unit can provide an optimal learning program by taking into account the learner's device information. For example, if the learner is using a smartphone, the learning program construction unit can provide a learning program optimized for the smartphone. Furthermore, if the learner is using a tablet, the learning program construction unit can also provide a learning program optimized for the tablet. In addition, if the learner is using a personal computer, the learning program construction unit can also provide a learning program optimized for the personal computer. This allows the learning program to be provided as optimal as possible by considering device information. Some or all of the above-described processes in the learning program construction unit may be performed using AI, or not. For example, the learning program construction unit can input the learner's device information into the AI, which can then analyze the data to provide an optimal learning program.

[0093] The visual support unit can estimate the learner's emotions and adjust the representation of diagrams and images based on the estimated emotions. For example, if the learner is stressed, the visual support unit's generating AI can create a simple, highly visible diagram. If the learner is relaxed, the visual support unit's generating AI can create a diagram containing detailed information. Furthermore, if the learner is focused, the visual support unit's generating AI can create a diagram containing complex information. This allows for more effective visual support by adjusting the representation of diagrams and images based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating 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 visual support unit may be performed using or without a generating AI. For example, the visual support unit can input learner emotion data into a generating AI, which can estimate the emotion and adjust the representation of diagrams and images.

[0094] The visual support unit can adjust the level of detail in diagrams and images according to the learner's level of understanding. For example, if the learner's level of understanding is low, the visual support unit's generating AI can create a diagram containing simple and basic information. If the learner's level of understanding is moderate, the visual support unit's generating AI can also create a diagram containing detailed information. Furthermore, if the learner's level of understanding is high, the visual support unit's generating AI can also create a diagram containing complex information. By adjusting the level of detail in diagrams and images according to the learner's level of understanding, more effective visual support becomes possible. Some or all of the above processing in the visual support unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the visual support unit can input learner understanding data into the generating AI, and the generating AI can analyze the data to adjust the level of detail in diagrams and images.

[0095] The visual support unit can provide different forms of visual support depending on the learner's learning style. For example, if a learner has a visual learning style, the visual support unit can provide visual support that makes extensive use of diagrams and images. Furthermore, if a learner has an auditory learning style, the visual support unit can provide videos with audio. In addition, if a learner has a tactile learning style, the visual support unit can provide interactive animations. By providing different forms of visual support according to the learner's learning style, learning effectiveness can be enhanced. Some or all of the above processing in the visual support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visual support unit can input learner learning style data into a generative AI, which can then analyze the data to provide the optimal visual support method.

[0096] The visual support unit can estimate the learner's emotions and adjust the display order of diagrams and images based on the estimated emotions. For example, if the learner is stressed, the visual support unit can display simpler diagrams first. If the learner is relaxed, it can display more detailed diagrams first. Furthermore, if the learner is focused, it can display more complex diagrams first. By adjusting the display order of diagrams and images based on the learner's emotions, more effective visual support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visual support unit may be performed using or without a generative AI. For example, the visual support unit can input learner emotion data into a generative AI, which can estimate the emotion and adjust the display order of diagrams and images.

[0097] The visual support unit can select the most effective visual support method based on the learner's past learning history. For example, the visual support unit can analyze the learner's past learning history and provide the most effective diagrams or images. It can also provide the most effective videos or animations based on the learner's past learning history. Furthermore, the visual support unit can select the optimal visual support method by referring to the learner's past learning history. This allows for improved learning effectiveness by selecting the most effective visual support method based on past learning history. Some or all of the above processing in the visual support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visual support unit can input the learner's past learning history data into a generative AI, which can then analyze the data and select the most effective visual support method.

[0098] The visual support unit can provide the optimal visual support method by taking into account the learner's device information. For example, if the learner is using a smartphone, the visual support unit can provide a visual support method optimized for smartphones. Furthermore, if the learner is using a tablet, the visual support unit can provide a visual support method optimized for tablets. In addition, if the learner is using a personal computer, the visual support unit can provide a visual support method optimized for personal computers. This allows the system to provide the optimal visual support method for the learner by considering device information. Some or all of the above processing in the visual support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the visual support unit can input the learner's device information into a generative AI, which can then analyze the data to provide the optimal visual support method.

[0099] The feedback unit can estimate the learner's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the learner is stressed, the feedback unit can provide feedback in gentle language. If the learner is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the learner is focused, the feedback unit can provide feedback that includes specific areas for improvement. This allows for more effective feedback by adjusting the way feedback is expressed based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner emotion data into an AI, which can estimate the emotions and adjust the way feedback is expressed.

[0100] The feedback unit can adjust the level of detail of the feedback according to the learner's level of understanding. For example, if the learner's level of understanding is low, the feedback unit will provide basic feedback. If the learner's level of understanding is moderate, the feedback unit can also provide detailed feedback. Furthermore, if the learner's level of understanding is high, the feedback unit can provide feedback that includes specific areas for improvement. By adjusting the level of detail of the feedback according to the learner's level of understanding, more effective feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner understanding data into AI, and the AI ​​can analyze the data to adjust the level of detail of the feedback.

[0101] The feedback unit can provide different forms of feedback depending on the learner's learning style. For example, if the learner has a visual learning style, the feedback unit can provide text-based feedback. If the learner has an auditory learning style, the feedback unit can also provide audio-based feedback. Furthermore, if the learner has a tactile learning style, the feedback unit can provide interactive feedback. This allows for improved learning effectiveness by providing different forms of feedback according to the learner's learning style. Some or all of the processing described above in the feedback unit may be performed using AI, or not. For example, the feedback unit can input learner learning style data into an AI, which can then analyze the data to provide the optimal feedback method.

[0102] The feedback unit can estimate the learner's emotions and prioritize feedback based on the estimated emotions. For example, if the learner is stressed, the feedback unit may prioritize providing simple feedback. If the learner is relaxed, the feedback unit may also prioritize providing detailed feedback. Furthermore, if the learner is focused, the feedback unit may prioritize providing feedback that includes specific areas for improvement. This allows for more appropriate feedback by prioritizing feedback based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input learner emotion data into an AI, which can estimate the emotions and determine the priority of feedback.

[0103] The feedback unit can select the most effective feedback method based on the learner's past learning history. For example, the feedback unit can analyze the learner's past learning history and provide the most effective feedback method. The feedback unit can also provide the most effective feedback format based on the learner's past learning history. Furthermore, the feedback unit can select the optimal feedback method by referring to the learner's past learning history. This allows for improved learning effectiveness by selecting the most effective feedback method based on past learning history. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the learner's past learning history data into an AI, which can then analyze the data and select the most effective feedback method.

[0104] The feedback unit can provide the optimal feedback method by taking into account the learner's device information. For example, if the learner is using a smartphone, the feedback unit can provide a feedback method optimized for smartphones. Furthermore, if the learner is using a tablet, the feedback unit can provide a feedback method optimized for tablets. In addition, if the learner is using a personal computer, the feedback unit can provide a feedback method optimized for personal computers. This allows the feedback unit to provide the optimal feedback method for the learner by considering device information. Some or all of the above processing in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input the learner's device information into an AI, which can then analyze the data to provide the optimal feedback method.

[0105] The progress management unit can estimate the learner's emotions and adjust the progress management method based on the estimated learner's emotions. For example, if the learner is stressed, the progress management unit can reduce the frequency of progress management to give the learner more breathing room. Conversely, if the learner is relaxed, the progress management unit can increase the frequency of progress management to maintain the learner's motivation. Furthermore, if the learner is focused, the progress management unit can perform detailed progress management and closely monitor the learner's progress. This allows for more appropriate progress management by adjusting the progress management method based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI or not. For example, the progress management unit can input learner emotion data into AI, which can estimate emotions and adjust the progress management method.

[0106] The progress management unit can optimize the progress management schedule by considering the learner's learning pace and attention span. For example, the progress management unit can analyze how long a learner's attention span lasts and construct a schedule for progress management within that time. It can also analyze the learner's learning pace and construct a manageable progress management schedule. Furthermore, the progress management unit can construct an efficient progress management schedule by considering the learner's break time. In this way, a manageable progress management schedule can be provided by considering the learning pace and attention span. Some or all of the above processes in the progress management unit may be performed using AI or not. For example, the progress management unit can input learner learning pace data into AI, and the AI ​​can analyze the data and construct an optimal schedule.

[0107] The Progress Management Unit can provide customized progress management methods based on learners' goals and desired career paths. For example, the Progress Management Unit can identify the skills necessary for a learner's desired career path and provide a progress management method based on those skills. It can also clarify the steps towards achieving the learner's goals and provide a progress management method based on those steps. Furthermore, the Progress Management Unit can provide a progress management method that includes preparation for qualification exams related to the learner's career path. This allows the unit to support learners in achieving their goals by providing customized progress management methods based on their goals and desired career paths. Some or all of the above processes in the Progress Management Unit may be performed using AI or not. For example, the Progress Management Unit can input learner goal data into AI, which can then analyze the data and provide a customized progress management method.

[0108] The progress management unit can estimate the learner's emotions and determine the priority of progress management based on the estimated emotions. For example, if the learner is stressed, the progress management unit may prioritize providing simple progress management. It may also prioritize providing detailed progress management if the learner is relaxed. Furthermore, it may prioritize providing specific progress management if the learner is focused. This allows for more appropriate progress management by prioritizing progress management based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI or not. For example, the progress management unit can input learner emotion data into an AI, which can estimate emotions and determine the priority of progress management.

[0109] The progress management unit can propose progress management methods based on the learner's past learning history, referencing successful cases of similar learners. For example, the progress management unit can analyze the learner's past learning history and propose progress management methods that have worked for similar learners. Furthermore, the progress management unit can identify successful progress management methods from the learner's past learning history and propose a progress management method based on those. In addition, the progress management unit can propose the optimal progress management method based on the learner's past learning history. This allows the system to provide learners with the most suitable progress management method by referencing successful cases based on their past learning history. Some or all of the above processes in the progress management unit may be performed using AI, or not. For example, the progress management unit can input the learner's past learning history data into an AI, which can then analyze the data and propose a progress management method referencing successful cases.

[0110] The progress management unit can provide an optimal progress management method by taking into account the learner's device information. For example, if the learner is using a smartphone, the progress management unit can provide a progress management method optimized for smartphones. Furthermore, if the learner is using a tablet, the progress management unit can also provide a progress management method optimized for tablets. In addition, if the learner is using a personal computer, the progress management unit can also provide a progress management method optimized for personal computers. This allows the system to provide the learner with the most suitable progress management method by considering device information. Some or all of the above processing in the progress management unit may be performed using AI, or not. For example, the progress management unit can input the learner's device information into an AI, which can then analyze the data to provide the optimal progress management method.

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

[0112] Learning support systems can also incorporate gamification features to maintain learner motivation. For example, learners could earn points for completing specific tasks and use those points to purchase virtual items. Ranking features could also be provided, allowing learners to compete with each other and whose rankings change according to their learning progress. Furthermore, features could be added to award badges or titles to learners upon achieving goals. This can increase learner motivation and encourage continued learning.

[0113] The learning support system can also include health management features that monitor the learner's health and adjust their learning plan accordingly. For example, it can collect the learner's heart rate and sleep data and prompt them to take a break if fatigue is accumulating. It can also analyze the learner's diet and exercise data and provide advice to maintain healthy lifestyle habits. Furthermore, if the learner is experiencing stress, it can provide guidance on relaxation exercises or meditation. This can support the learner's health and enhance learning effectiveness.

[0114] The learning support system can further estimate the learner's emotions and adjust the learning content based on those emotions. For example, if a learner is feeling stressed, it can provide content that helps them relax. If a learner is excited, it can provide content that enhances their concentration. Furthermore, if a learner is feeling down, it can display encouraging messages to boost their motivation. This allows the system to provide appropriate learning support tailored to the learner's emotions.

[0115] Learning support systems can further provide different formats of learning content according to the learner's learning style. For example, learners with a visual learning style can be provided with content that makes extensive use of diagrams and images. Learners with an auditory learning style can be provided with audio materials and podcasts. Furthermore, learners with an experiential learning style can be provided with interactive content, including experiments and practical exercises. This allows for the provision of optimal learning support tailored to each learner's learning style.

[0116] The learning support system can further estimate the learner's emotions and adjust the content of the feedback based on those estimates. For example, if the learner is stressed, it can provide feedback in gentle language. If the learner is relaxed, it can provide detailed feedback. Furthermore, if the learner is focused, it can provide feedback that includes specific areas for improvement. This allows for the provision of appropriate feedback tailored to the learner's emotions.

[0117] Learning support systems can also include a dashboard function to visualize learners' progress. For example, they can display learners' progress in graphs and charts, allowing users to see at a glance which subjects or areas they are lagging behind in. They can also display the degree of achievement against the goals set by the learners and provide feedback to maintain motivation. Furthermore, they can display a list of problems and test results that learners have previously solved, allowing them to identify areas that need review. This enables learners to understand their own progress and study efficiently.

[0118] The learning support system can further estimate the learner's emotions and adjust the learning program schedule based on those emotions. For example, if the learner is stressed, the learning time can be shortened and breaks increased. If the learner is relaxed, the learning time can be extended and content designed to enhance concentration can be provided. Furthermore, if the learner is focused, a learning program including more challenging content can be provided. This allows for the provision of an appropriate learning schedule tailored to the learner's emotions.

[0119] The learning support system can further analyze the learner's learning environment and suggest the optimal learning environment. For example, if the learner's learning environment is noisy, it can suggest studying in a quiet place. Similarly, if the learner's learning environment is dark, it can suggest studying in a brighter place. Furthermore, if the learner's learning environment is uncomfortable, it can provide advice on maintaining a comfortable temperature and humidity. This allows learners to study efficiently in an optimal learning environment.

[0120] The learning support system can further estimate the learner's emotions and customize the learning program content based on those emotions. For example, if a learner is feeling stressed, it can provide content that helps them relax. If a learner is excited, it can provide content that enhances their concentration. Furthermore, if a learner is feeling down, it can display encouraging messages to boost their motivation. This allows the system to provide appropriate learning support tailored to the learner's emotions.

[0121] The learning support system can further analyze the learner's learning history and suggest the most suitable learning method for them. For example, it can identify the most effective learning method based on past learning history and provide a learning program based on that. It can also identify areas where the learner has struggled in the past and provide a learning program that focuses on those areas. Furthermore, it can suggest an optimal learning schedule based on the learner's learning history. This allows learners to efficiently progress in their studies using methods best suited to them.

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

[0123] Step 1: The analysis department analyzes the learner's strengths and weaknesses. For example, they collect the learner's past learning data and test results, and use this to identify the learner's strengths and weaknesses. Step 2: The learning program development department constructs a learning program suitable for the learner based on the results of the analysis conducted by the analysis department. For example, they might develop a learning program that focuses on listening, designing a program that leverages the learner's strengths while addressing their weaknesses. Step 3: The Visual Support Unit creates visual diagrams and images based on the learning program built by the Learning Program Construction Unit. For example, when explaining the concept of mechanics in physics, it generates a vector diagram of forces. It can also automatically create diagrams and images for particularly difficult-to-understand parts using generation AI. Step 4: The feedback unit provides immediate feedback based on visual diagrams and images created by the visual support unit. For example, it checks whether a learner has solved a problem and provides detailed explanations for any mistakes. It also features real-time feedback functionality, allowing learners to deepen their understanding on the spot. Step 5: The progress management department performs autonomous progress management based on the feedback provided by the feedback department. For example, it continuously monitors learners' progress and adjusts the learning plan as needed. If progress is behind schedule, it reviews the learning plan and provides advice on how to proceed with learning more efficiently.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the analysis unit, learning program construction unit, visual support unit, feedback unit, and progress management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects the learner's past learning data and test results and identifies their strengths and weaknesses. The learning program construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs a learning program based on the analysis results. The visual support unit is implemented by the control unit 46A of the smart device 14, which creates visual diagrams and image diagrams using generating AI. The feedback unit is implemented by the control unit 46A of the smart device 14, which checks whether a problem is correct or incorrect immediately after it is solved and provides a detailed explanation. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the learner's progress and adjusts the learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the analysis unit, learning program construction unit, visual support unit, feedback unit, and progress management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects the learner's past learning data and test results and identifies their strengths and weaknesses. The learning program construction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which constructs a learning program based on the analysis results. The visual support unit is implemented, for example, by the control unit 46A of the smart glasses 214, which creates visual diagrams and image diagrams using generative AI. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214, which checks whether a problem is correct or incorrect immediately after solving it and provides a detailed explanation. The progress management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the learner's progress and adjusts the learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the analysis unit, learning program construction unit, visual support unit, feedback unit, and progress management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects the learner's past learning data and test results and identifies their strengths and weaknesses. The learning program construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs a learning program based on the analysis results. The visual support unit is implemented by the control unit 46A of the headset terminal 314, which creates visual diagrams and image diagrams using generative AI. The feedback unit is implemented by the control unit 46A of the headset terminal 314, which checks whether a problem is correct or incorrect immediately after solving it and provides a detailed explanation. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the learner's progress and adjusts the learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the analysis unit, learning program construction unit, visual support unit, feedback unit, and progress management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects the learner's past learning data and test results and identifies their strengths and weaknesses. The learning program construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs a learning program based on the analysis results. The visual support unit is implemented by the control unit 46A of the robot 414, which creates visual diagrams and image diagrams using generative AI. The feedback unit is implemented by the control unit 46A of the robot 414, which checks whether a problem is solved correctly or incorrectly immediately after the problem is solved and provides a detailed explanation. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors the learner's progress and adjusts the learning plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The analysis department analyzes the strengths and weaknesses of learners, A learning program construction unit constructs a learning program suitable for learners based on the results of the analysis performed by the aforementioned analysis unit, A visual support unit creates visual diagrams and image diagrams based on the learning program constructed by the aforementioned learning program construction unit, A feedback unit provides immediate feedback based on visual diagrams and images created by the aforementioned visual support unit, The system includes a progress management unit that performs autonomous progress management based on feedback provided by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze learners' past learning data and test results to identify their strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning program construction unit, Based on the strengths and weaknesses identified by the aforementioned analysis unit, we construct a learning program that is optimal for the learner. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visual support unit is To help users understand difficult concepts, the system automatically generates diagrams and images to enhance their visual comprehension. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is The system checks the correctness of the answers immediately after the learner completes the problem and provides detailed explanations for any mistakes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned progress management unit, We constantly monitor learners' progress and adjust their learning plans as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate learners' emotions and adjust the analysis method of the learning data based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is In addition to learners' past learning data, we analyze learners' lifestyles and learning environments to identify their strengths and weaknesses with greater accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We analyze learners' learning styles and propose the optimal learning methods. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates learners' emotions and prioritizes analysis results based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Analyze learners' social media activity to identify learning-related interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze learning trends specific to a region, taking into account the learners' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning program construction unit, The system estimates learners' emotions and adjusts the content of the learning program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning program construction unit, Optimize the learning program schedule by taking into account the learner's learning pace and attention span. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning program construction unit, We create customized learning programs based on learners' goals and desired career paths. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning program construction unit, The system estimates learners' emotions and prioritizes learning programs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning program construction unit, Based on the learner's past learning history, we propose a learning program that references the success stories of similar learners. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning program construction unit, We provide the optimal learning program by taking into account the learner's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visual support unit is The system estimates the learner's emotions and adjusts the representation of diagrams and images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visual support unit is Adjust the level of detail in diagrams and illustrations according to the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visual support unit is Provide different forms of visual support according to the learner's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visual support unit is The system estimates the learner's emotions and adjusts the display order of diagrams and images based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visual support unit is Select the most effective visual support method based on the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visual support unit is Provide the optimal visual support method, taking into account the learner's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is Adjust the level of detail in the feedback according to the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is Provide different forms of feedback depending on the learner's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates the learner's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is Select the most effective feedback method based on the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is Provide the optimal feedback method, taking into account the learner's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned progress management unit, The system estimates learners' emotions and adjusts progress management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned progress management unit, Optimize the progress management schedule by taking into account the learner's learning pace and attention span. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned sanitation management unit, We provide a customized progress management method based on the learner's goals and desired career path. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned progress management unit, The system estimates learners' emotions and prioritizes progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned progress management unit, Based on learners' past learning history, we propose a progress management method that references the success stories of similar learners. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned progress management unit, We provide the optimal progress management method, taking into account the learner's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the strengths and weaknesses of learners, A learning program construction unit constructs a learning program suitable for learners based on the results of the analysis performed by the aforementioned analysis unit, A visual support unit creates visual diagrams and image diagrams based on the learning program constructed by the aforementioned learning program construction unit, A feedback unit provides immediate feedback based on visual diagrams and images created by the aforementioned visual support unit, The system includes a progress management unit that performs autonomous progress management based on feedback provided by the aforementioned feedback unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze learners' past learning data and test results to identify their strengths and weaknesses. The system according to feature 1.

3. The aforementioned learning program construction unit, Based on the strengths and weaknesses identified by the aforementioned analysis unit, we construct a learning program that is optimal for the learner. The system according to feature 1.

4. The aforementioned visual support unit is To help users understand difficult concepts, the system automatically generates diagrams and images to enhance their visual comprehension. The system according to feature 1.

5. The aforementioned feedback unit is The system checks the correctness of the answers immediately after the learner completes the problem and provides detailed explanations for any mistakes. The system according to feature 1.

6. The aforementioned progress management unit, We constantly monitor learners' progress and adjust their learning plans as needed. The system according to feature 1.

7. The aforementioned analysis unit is We estimate learners' emotions and adjust the analysis method of the learning data based on the estimated learners' emotions. The system according to feature 1.

8. The aforementioned analysis unit is In addition to learners' past learning data, we analyze learners' lifestyles and learning environments to identify their strengths and weaknesses with greater accuracy. The system according to feature 1.

Images