system

Abstract

The system according to this embodiment aims to automatically generate a learning plan based on the learner's goals and to provide progress management and individually optimized practice problems. [Solution] The system according to the embodiment comprises a plan generation unit, a progress management unit, a problem provision unit, and a question response unit. The plan generation unit automatically generates a learning plan based on the learner's goal setting. The progress management unit tracks the learning history and visualizes the progress based on the learning plan generated by the plan generation unit. The problem provision unit provides individually optimized practice problems and explanations based on the progress information obtained by the progress management unit. The question response unit responds to questions in real time based on the practice problems provided by the problem provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the automatic generation of a learning plan based on the learner's goals, progress management, and the provision of individually optimized problem exercises have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate a learning plan based on the learner's goals and provide progress management and individually optimized problem exercises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a plan generation unit, a progress management unit, a problem provision unit, and a question response unit. The plan generation unit automatically generates a learning plan based on the learner's goal setting. The progress management unit tracks the learning history and visualizes progress based on the learning plan generated by the plan generation unit. The problem provision unit provides individually optimized practice problems and explanations based on the progress information obtained by the progress management unit. The question response unit responds to questions in real time based on the practice problems provided by the problem provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate a learning plan based on the learner's goals and provide progress management and individually optimized practice problems. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The next-generation support system according to an embodiment of the present invention is a system that utilizes an AI agent to improve learning efficiency in qualification exams and university entrance exam preparation. This next-generation support system provides individually optimized learning plans, real-time feedback and advice, and communication that supports increased motivation in order to solve the challenges faced by learners. For example, when a learner sets a goal, the AI ​​agent automatically generates an optimized learning plan based on that goal. When a candidate for a qualification exam sets a target score, the AI ​​agent creates a learning plan based on that goal. This plan includes the content to be studied and the schedule, and the learner can proceed with their studies accordingly. Next, the AI ​​agent tracks the learner's learning history and visualizes their progress. For example, it can determine how much time the learner has spent studying, which areas they are strong in, and which areas they struggle with. This allows the learner to check their progress and modify their learning plan as needed. Furthermore, the AI ​​agent provides individually optimized practice problems and detailed explanations. For example, for areas where the learner struggles, the AI ​​agent provides problems and explanations specifically tailored to that area. This allows the learner to proceed with their studies efficiently. In addition, the AI ​​agent responds to questions in real time and resolves any points of confusion on the spot. For example, if a learner has a question while solving a problem, they can ask the AI ​​agent and get an immediate answer. This allows learners to progress through their learning smoothly. In this way, the AI ​​agent improves learning efficiency by providing learners with individually optimized learning plans, real-time feedback and advice, and communication that supports increased motivation. For example, candidates taking a certification exam can efficiently progress through their studies by having their current level measured, their weak areas identified, and customized practice problems provided. University entrance exam candidates can also increase their chances of achieving their goals by being supported in balancing schoolwork and exam preparation, and by having their schedules managed based on their passing goals.The introduction of this system allows learners to save time through efficient learning and increase their chances of achieving their goals. Educational institutions can also reduce individual tutoring costs and improve the quality of education. Furthermore, by setting key performance indicators (KPIs) such as the rate of reduction in learning time, improvement in pass rates, and user satisfaction, and by refining the model based on user feedback, the system can be continuously improved. This allows the next-generation support system to improve learners' learning efficiency.

[0029] The next-generation support system according to this embodiment comprises a plan generation unit, a progress management unit, a problem provision unit, and a question response unit. The plan generation unit automatically generates a learning plan based on the learner's goal setting. For example, when a learner sets a target score for a qualification exam, the plan generation unit creates a learning plan based on that goal. The plan generation unit can automatically generate a learning plan that includes learning content and schedule. The progress management unit tracks the learning history and visualizes progress based on the learning plan generated by the plan generation unit. For example, the progress management unit can understand how much time the learner has spent studying, which areas they are good at, and which areas they are weak in. The progress management unit can check the learner's progress and modify the learning plan as needed. The problem provision unit provides individually optimized practice problems and explanations based on the progress information obtained by the progress management unit. For example, the problem provision unit provides problems and explanations specifically tailored to areas where the learner is weak. The problem provision unit enables the learner to proceed with learning efficiently. The question response unit responds to questions in real time based on the practice problems provided by the problem provision unit. The question support unit provides immediate answers to questions, for example, if a learner has a question while solving a problem. The question support unit enables learners to proceed with their learning smoothly. As a result, the next-generation support system according to this embodiment can improve learning efficiency by automatically generating a learning plan based on the learner's goal setting, visualizing progress, providing individually optimized practice problems and explanations, and responding to questions in real time.

[0030] The plan generation unit automatically generates learning plans based on the learner's goal setting. Specifically, when a learner sets a target score for a qualification exam, the unit creates a learning plan based on that goal. The plan generation unit considers the learner's past learning history and current learning status to propose the optimal learning content and schedule. For example, it analyzes what subjects the learner has studied in the past and their level of understanding, and customizes the learning plan based on the results. Furthermore, the plan generation unit can appropriately allocate learning time and break times to match the learner's lifestyle and learning style. For example, it proposes a schedule that allows night owls to concentrate on learning at night, and creates a plan that starts early in the morning for early morning learners. In addition, the plan generation unit can set regular goal setting and progress check timings to maintain the learner's motivation. This allows learners to learn at their own pace without undue pressure and to efficiently progress toward achieving their goals. Furthermore, the plan generation unit can use AI to analyze the learner's learning patterns and propose the optimal learning method. For example, the AI ​​determines which learning method is most effective based on the learner's past learning data and reflects the results in the learning plan. This allows the plan generation unit to provide a learning plan optimized for each individual learner, maximizing learning efficiency.

[0031] The progress management unit tracks learning history and visualizes progress based on the learning plan generated by the plan generation unit. Specifically, it can understand how much time learners spend studying, which areas they excel in, and which areas they struggle with. The progress management unit monitors in real time whether learners are progressing according to the learning plan and visually displays the progress using graphs and charts. For example, it can display how much a learner has studied in a week using a bar graph and show their level of understanding for each subject using a radar chart. This allows learners to grasp their learning status at a glance and decide which areas they should focus on. Furthermore, the progress management unit can automatically adjust the learning plan according to the learner's progress. For example, if a learner is ahead of schedule, the next learning content will be provided ahead of schedule; conversely, if they are behind schedule, the learning plan will be adjusted to ensure they are studying at a manageable pace. In addition, the progress management unit can provide feedback and rewards based on achievement to maintain learner motivation. For example, badges or points can be awarded when certain goals are achieved, making learning more enjoyable and encouraging learners to continue studying. This allows the progress management department to accurately grasp the learners' learning progress and provide appropriate support, thereby improving learning efficiency.

[0032] The Problem Provisioning Department provides individually optimized practice problems and explanations based on progress information obtained by the Progress Management Department. Specifically, it provides problems and explanations tailored to areas where learners struggle. The Problem Provisioning Department uses AI to analyze learners' understanding and select the most suitable problems. For example, if a learner has difficulty in a particular area of ​​mathematics, it will provide a concentrated supply of problems related to that area and detailed explanations to deepen their understanding. The Problem Provisioning Department can also adjust the difficulty level of the problems according to the learner's progress. For example, once a learner has mastered basic problems, it will then provide applied and advanced problems, gradually increasing the difficulty. Conversely, if a learner is struggling with a problem, it will return to basic problems and encourage them to study again. Furthermore, based on the learner's answer history, the Problem Provisioning Department can identify points where learners tend to make mistakes or areas where their understanding is insufficient, and provide focused explanations. This allows learners to overcome their weaknesses and progress efficiently in their learning. The Problem Provisioning Department can also provide real-time feedback to learners as they solve problems. For example, immediately after a learner solves a problem, the system determines whether the answer is correct or incorrect. If correct, the learner moves on to the next problem; if incorrect, an explanation is displayed to deepen their understanding. This allows the problem-providing unit to support learners in efficiently progressing through their studies and maximize learning effectiveness.

[0033] The question support unit responds to questions in real time based on the practice problems provided by the problem-providing unit. Specifically, if a learner has a question while solving a problem, it will provide an immediate answer. The question support unit uses AI to analyze the content of the learner's question and generate an appropriate answer. For example, if a learner doesn't know how to use a particular formula while solving a math problem, it will show how to use that formula and provide related example problems. In addition, the question support unit can refer to frequently asked questions and past questions based on the learner's question history to provide quick and accurate answers. Furthermore, the question support unit improves usability by designing the interface for learners to input questions. For example, it introduces voice input and chat-style question support to allow learners to ask questions smoothly. The question support unit can also present relevant materials and references when learners ask questions to promote deeper understanding. This allows learners to proceed with their learning while resolving their doubts, improving learning efficiency. Furthermore, the question support unit can analyze the content of learners' questions to identify common doubts and areas of insufficient understanding, and reflect this in future learning plans. This allows the question-answering department to support learners so they can progress through their studies smoothly, maximizing learning effectiveness.

[0034] The plan generation unit can automatically generate an optimized learning plan based on the learner's goal setting. For example, if a learner sets a target score for a qualification exam, the plan generation unit will create a learning plan based on that goal. The plan generation unit can automatically generate a learning plan that includes learning content and schedule. Some or all of the above-described processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the learner's goal setting into a generation AI, and the generation AI can generate an optimized learning plan. This allows the system to provide learners with the most suitable learning plan by automatically generating an optimized learning plan based on their goal setting.

[0035] The progress management unit can track learners' learning history and visualize their progress. For example, the progress management unit can understand how much time learners spend studying, which areas they are strong in, and which areas they struggle with. The progress management unit can check learners' progress and revise their learning plans as needed. Some or all of the above processes in the progress management unit may be performed using or without a generative AI. For example, the progress management unit can input learners' learning history into a generative AI, which can then visualize their progress. This allows learners to track their learning history and visualize their progress, enabling them to check their progress and revise their learning plans.

[0036] The problem-providing unit can provide individually optimized practice problems and detailed explanations. For example, the problem-providing unit can provide problems and explanations specifically tailored to areas where the learner struggles. The problem-providing unit enables learners to progress through their learning efficiently. Some or all of the above-described processes in the problem-providing unit may be performed using a generative AI, or they may not. For example, the problem-providing unit can input the learner's areas of difficulty into a generative AI, which can then generate problems and provide explanations tailored to those areas. This allows learners to progress through their learning efficiently by providing individually optimized practice problems and detailed explanations.

[0037] The question-answering unit responds to questions in real time, resolving any points of confusion on the spot. For example, if a learner has a question while solving a problem, the question-answering unit will provide an immediate answer. The question-answering unit ensures that learners can progress smoothly through their learning. Some or all of the above-described processes in the question-answering unit may be performed using a generative AI, or they may not. For example, the question-answering unit can input the learner's question into a generative AI, which can immediately generate an answer. This allows for real-time responses to questions and immediate resolution of points of confusion, enabling learners to progress smoothly through their learning.

[0038] The plan generation unit can automatically generate study plans based on goals for qualification exams or university entrance exams. For example, when a learner sets a target score for a qualification exam, the plan generation unit creates a study plan based on that goal. The plan generation unit can automatically generate a study plan that includes study content and schedule. Some or all of the above-described processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the goals for qualification exams or university entrance exams into a generation AI, which can then generate an optimized study plan. This allows the system to provide learners with the most suitable study plan by automatically generating a study plan based on their goals for qualification exams or university entrance exams.

[0039] The progress management unit can identify learners' strengths and weaknesses and revise their learning plans accordingly. For example, the progress management unit can determine which subjects learners excel in and which they struggle with. The progress management unit can monitor learners' progress and revise their learning plans as needed. Some or all of the above processes in the progress management unit may be performed using a generative AI, or they may not. For example, the progress management unit can input learners' strengths and weaknesses into a generative AI, which can then revise the learning plan. This allows the system to identify learners' strengths and weaknesses and revise the learning plan accordingly, thereby providing learners with the most suitable learning plan.

[0040] The problem-providing unit can provide problems and explanations tailored to the learner's weak areas. For example, the problem-providing unit can provide problems and explanations tailored to a learner's weak areas. The problem-providing unit enables learners to progress through their learning efficiently. Some or all of the above processing in the problem-providing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the problem-providing unit can input the learner's weak areas into a generative AI, which can then generate problems tailored to those areas and provide explanations. This allows learners to progress through their learning efficiently by providing problems and explanations tailored to their weak areas.

[0041] The plan generation unit can analyze the learner's past learning history and select the optimal learning method. For example, the plan generation unit can propose a similar method based on the learner's past successful learning methods. The plan generation unit can avoid learning methods that the learner has failed at in the past and propose a new method. The plan generation unit can select the most effective learning method from the learner's past learning history. Some or all of the above processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the learner's past learning history into a generation AI, which can then select the optimal learning method. This allows the plan generation unit to provide the learner with the most suitable learning method by analyzing their past learning history and selecting the optimal learning method.

[0042] The plan generation unit can optimize study time based on the learner's daily rhythm and schedule when generating a study plan. For example, the plan generation unit can suggest the optimal study time in accordance with the learner's daily rhythm. The plan generation unit can adjust the study time considering the learner's schedule. The plan generation unit can set an efficient study time based on the learner's daily rhythm and schedule. Some or all of the above processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the learner's daily rhythm and schedule into a generation AI, which can then optimize the study time. This allows the learner to be provided with the optimal study time by optimizing the study time based on their daily rhythm and schedule.

[0043] The plan generation unit can prioritize incorporating highly relevant learning content by considering the learner's geographical location when generating a learning plan. For example, if the learner is in a specific region, the plan generation unit can prioritize incorporating learning content related to that region. If the learner is traveling, the plan generation unit can suggest learning content related to the travel destination. If the learner is in a specific location, the plan generation unit can prioritize providing learning content related to that location. Some or all of the above processing in the plan generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the plan generation unit can input the learner's geographical location information into the generation AI, which can then prioritize incorporating highly relevant learning content. This allows the plan generation unit to provide learners with optimal learning content by prioritizing highly relevant learning content while considering the learner's geographical location.

[0044] The learning plan generation unit can analyze the learner's social media activity and incorporate relevant learning content when generating a learning plan. For example, the learning plan generation unit can adjust the learning plan based on the content the learner has shown interest in on social media. The learning plan generation unit can provide learning content related to topics the learner follows on social media. The learning plan generation unit can analyze the learner's social media activity and select the most suitable learning content. Some or all of the above processes in the learning plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the learning plan generation unit can input the learner's social media activity into a generation AI, which can then incorporate relevant learning content. This allows the learning plan generation unit to provide the learner with the most suitable learning content by analyzing the learner's social media activity and incorporating relevant learning content.

[0045] The progress management unit can predict progress by referring to the learner's past progress data during progress management. For example, the progress management unit predicts future progress based on the learner's past progress data. The progress management unit can analyze the learner's past progress data and make the optimal progress prediction. The progress management unit can predict progress by referring to the learner's past progress data. Some or all of the above processes in the progress management unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the progress management unit can input the learner's past progress data into a generative AI, and the generative AI can make a progress prediction. This allows the learner to be provided with the optimal progress prediction by referring to the learner's past progress data.

[0046] The progress management unit can analyze learners' strengths and weaknesses in detail and visualize their progress during progress management. For example, the progress management unit analyzes learners' strengths and weaknesses and visualizes their progress. The progress management unit can visualize progress based on learners' strengths and weaknesses. The progress management unit can analyze learners' strengths and weaknesses in detail and visualize their progress. Some or all of the above processing in the progress management unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the progress management unit can input learners' strengths and weaknesses into a generation AI, and the generation AI can visualize the progress. This allows for a detailed analysis of learners' strengths and weaknesses and visualization of their progress, thereby providing learners with the most optimal progress display.

[0047] The progress management unit can evaluate progress based on the content of assignments submitted by learners during progress management. For example, the progress management unit can analyze the content of assignments submitted by learners and evaluate progress. The progress management unit can evaluate progress based on the content of assignments submitted by learners. The progress management unit can analyze the content of assignments submitted by learners in detail and evaluate progress. Some or all of the above processes in the progress management unit may be performed using a generative AI, or not using a generative AI. For example, the progress management unit can input the content of assignments submitted by learners into a generative AI, and the generative AI can evaluate progress. This allows for the provision of the most appropriate progress evaluation to learners by evaluating progress based on the content of assignments submitted by learners.

[0048] The progress management unit can analyze progress by referring to learner-related market data during progress management. For example, the progress management unit can analyze progress by referring to learner-related market data. The progress management unit can analyze progress based on learner-related market data. The progress management unit can analyze progress by referring to learner-related market data in detail. Some or all of the above processes in the progress management unit may be performed using a generative AI, or not using a generative AI. For example, the progress management unit can input learner-related market data into a generative AI, and the generative AI can perform progress analysis. This allows for the provision of optimal progress analysis to learners by analyzing progress by referring to learner-related market data.

[0049] The problem-providing unit can analyze the learner's past answer history to select the most suitable problem when providing a problem. For example, the problem-providing unit can select the most suitable problem based on the learner's past answer history. The problem-providing unit can analyze the learner's past answer history and provide the most suitable problem. The problem-providing unit can refer to the learner's past answer history to select the most suitable problem. Some or all of the above processing in the problem-providing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the problem-providing unit can input the learner's past answer history into a generative AI, which can then select the most suitable problem. This allows the system to provide the learner with the most suitable problem by analyzing the learner's past answer history and selecting the most suitable problem.

[0050] The question provider can adjust the frequency of questions presented according to the learner's strengths and weaknesses when providing questions. For example, the question provider can set a lower frequency for questions in the learner's strengths. The question provider can set a higher frequency for questions in the learner's weaknesses. The question provider can adjust the frequency of questions based on the learner's strengths and weaknesses. Some or all of the above processing in the question provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the question provider can input the learner's strengths and weaknesses into a generative AI, and the generative AI can adjust the frequency of questions. This allows the question provider to provide learners with the most suitable questions by adjusting the frequency of questions according to their strengths and weaknesses.

[0051] The problem-providing unit can prioritize providing highly relevant problems by considering the learner's geographical location when providing problems. For example, if the learner is in a specific region, the problem-providing unit can prioritize providing problems related to that region. If the learner is traveling, the problem-providing unit can provide problems related to their travel destination. If the learner is in a specific location, the problem-providing unit can prioritize providing problems related to that location. Some or all of the above processing in the problem-providing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the problem-providing unit can input the learner's geographical location information into a generative AI, which can then prioritize providing highly relevant problems. This allows the system to provide learners with the most relevant problems by prioritizing the provision of problems that consider their geographical location.

[0052] The question provider can analyze the learner's social media activity and provide relevant questions when providing questions. For example, the question provider can provide questions based on the content the learner has shown interest in on social media. The question provider can provide questions related to topics the learner follows on social media. The question provider can analyze the learner's social media activity and provide the most suitable questions. Some or all of the above processing in the question provider may be performed using a generative AI, or not. For example, the question provider can input the learner's social media activity into a generative AI, which can then provide relevant questions. This allows the system to provide the learner with the most suitable questions by analyzing their social media activity and providing relevant questions.

[0053] The question response unit can provide the optimal answer by referring to the learner's past question history when responding to a question. For example, the question response unit can provide the optimal answer based on the learner's past question history. The question response unit can analyze the learner's past question history and provide the optimal answer. The question response unit can provide the optimal answer by referring to the learner's past question history. Some or all of the above processing in the question response unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question response unit can input the learner's past question history into a generative AI, and the generative AI can provide the optimal answer. In this way, by referring to the learner's past question history and providing the optimal answer, the learner can be provided with the best possible answer.

[0054] The question-answering unit can adjust the level of detail in its answers according to the learner's strengths and weaknesses. For example, it can provide a concise answer to a learner's strengths, and a detailed answer to a learner's weaknesses. The question-answering unit can adjust the level of detail in its answers based on the learner's strengths and weaknesses. Some or all of the above processing in the question-answering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question-answering unit can input the learner's strengths and weaknesses into a generative AI, which can then adjust the level of detail in its answers. This allows the system to provide learners with the most appropriate answers by adjusting the level of detail according to their strengths and weaknesses.

[0055] The question-answering unit can prioritize providing highly relevant answers by considering the learner's geographical location when answering questions. For example, if the learner is in a specific region, the question-answering unit can prioritize providing answers related to that region. If the learner is traveling, the question-answering unit can provide answers related to their travel destination. If the learner is in a specific location, the question-answering unit can prioritize providing answers related to that location. Some or all of the above processing in the question-answering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question-answering unit can input the learner's geographical location information into a generative AI, which can then prioritize providing highly relevant answers. This allows the learner to receive the most appropriate answers by prioritizing highly relevant answers while considering their geographical location.

[0056] The question-answering unit can analyze the learner's social media activity and provide relevant answers when answering questions. For example, the question-answering unit can provide answers based on the content the learner has shown interest in on social media. The question-answering unit can provide answers related to topics the learner follows on social media. The question-answering unit can analyze the learner's social media activity and provide the most appropriate answer. Some or all of the above processing in the question-answering unit may be performed using generative AI, or not. For example, the question-answering unit can input the learner's social media activity into a generative AI, which can then provide relevant answers. This allows the system to provide the learner with the most appropriate answer by analyzing their social media activity and providing relevant answers.

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

[0058] The next-generation support system can customize learning plans based on the learner's learning style. For example, visual learners can be provided with materials that make extensive use of diagrams and graphs. Auditory learners can be provided with audio explanations and podcast-style materials. Furthermore, tactile learners can be provided with interactive simulations and practical exercises. This allows for the provision of an optimal learning plan tailored to each learner's learning style.

[0059] The next-generation support system can adjust learning plans by utilizing learners' health data. For example, it can suggest optimal study times based on learners' sleep data. Based on learners' exercise data, it can provide learning plans that incorporate appropriate breaks and exercise. Furthermore, based on learners' dietary data, it can suggest learning plans that consider nutritional balance. This allows for the provision of optimal learning plans tailored to each learner's health condition.

[0060] The next-generation support system can analyze learners' learning histories and suggest the most suitable learning partners. For example, it can match learners with similar goals to promote collaborative learning. It can also match learners with different areas of expertise to promote complementary learning. Furthermore, it can suggest compatible learning partners based on the learner's personality and learning style. This allows for improved learning efficiency through collaboration among learners.

[0061] The next-generation support system can predict learning progress based on the learner's learning history and provide reminders at the appropriate time. For example, if a learner is not progressing according to plan, it can send a reminder to encourage them to continue learning. If the learner is progressing according to plan, it can provide a reminder to check their progress. Furthermore, if the learner is approaching their goal, it can provide a reminder to give them a sense of accomplishment. In this way, it can provide appropriate reminders according to the learner's progress.

[0062] The next-generation support system can provide a dashboard to visualize learning progress based on the learner's learning history. For example, it can display a graph showing how much time a learner has spent studying. It can also color-code and display the learner's strengths and weaknesses. Furthermore, it can display the learner's progress toward their goals in real time. This allows learners to see their progress at a glance, which helps them revise their learning plans and maintain their motivation.

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

[0064] Step 1: The plan generation unit automatically generates a learning plan based on the learner's goal setting. For example, if a learner sets a target score for a certification exam, the unit will create a learning plan based on that goal. The plan generation unit can automatically generate a learning plan that includes learning content and schedule. Step 2: The progress management unit tracks learning history and visualizes progress based on the learning plan generated by the plan generation unit. For example, it can understand how much time learners spend studying, which areas they are strong in, and which areas they struggle with. The progress management unit can check learners' progress and revise the learning plan as needed. Step 3: The Problem Provisioning Department provides individually optimized practice problems and explanations based on the progress information obtained by the Progress Management Department. For example, for areas where learners struggle, the Problem Provisioning Department provides problems and explanations specifically tailored to those areas. The Problem Provisioning Department ensures that learners can progress through their studies efficiently. Step 4: The question support unit responds to questions in real time based on the practice problems provided by the problem-providing unit. For example, if a learner has a question while solving a problem, the unit will provide an immediate answer. The question support unit ensures that learners can progress through their learning smoothly.

[0065] (Example of form 2) The next-generation support system according to an embodiment of the present invention is a system that utilizes an AI agent to improve learning efficiency in qualification exams and university entrance exam preparation. This next-generation support system provides individually optimized learning plans, real-time feedback and advice, and communication that supports increased motivation in order to solve the challenges faced by learners. For example, when a learner sets a goal, the AI ​​agent automatically generates an optimized learning plan based on that goal. When a candidate for a qualification exam sets a target score, the AI ​​agent creates a learning plan based on that goal. This plan includes the content to be studied and the schedule, and the learner can proceed with their studies accordingly. Next, the AI ​​agent tracks the learner's learning history and visualizes their progress. For example, it can determine how much time the learner has spent studying, which areas they are strong in, and which areas they struggle with. This allows the learner to check their progress and modify their learning plan as needed. Furthermore, the AI ​​agent provides individually optimized practice problems and detailed explanations. For example, for areas where the learner struggles, the AI ​​agent provides problems and explanations specifically tailored to that area. This allows the learner to proceed with their studies efficiently. In addition, the AI ​​agent responds to questions in real time and resolves any points of confusion on the spot. For example, if a learner has a question while solving a problem, they can ask the AI ​​agent and get an immediate answer. This allows learners to progress through their learning smoothly. In this way, the AI ​​agent improves learning efficiency by providing learners with individually optimized learning plans, real-time feedback and advice, and communication that supports increased motivation. For example, candidates taking a certification exam can efficiently progress through their studies by having their current level measured, their weak areas identified, and customized practice problems provided. University entrance exam candidates can also increase their chances of achieving their goals by being supported in balancing schoolwork and exam preparation, and by having their schedules managed based on their passing goals.The introduction of this system allows learners to save time through efficient learning and increase their chances of achieving their goals. Educational institutions can also reduce individual tutoring costs and improve the quality of education. Furthermore, by setting key performance indicators (KPIs) such as the rate of reduction in learning time, improvement in pass rates, and user satisfaction, and by refining the model based on user feedback, the system can be continuously improved. This allows the next-generation support system to improve learners' learning efficiency.

[0066] The next-generation support system according to this embodiment comprises a plan generation unit, a progress management unit, a problem provision unit, and a question response unit. The plan generation unit automatically generates a learning plan based on the learner's goal setting. For example, when a learner sets a target score for a qualification exam, the plan generation unit creates a learning plan based on that goal. The plan generation unit can automatically generate a learning plan that includes learning content and schedule. The progress management unit tracks the learning history and visualizes progress based on the learning plan generated by the plan generation unit. For example, the progress management unit can understand how much time the learner has spent studying, which areas they are good at, and which areas they are weak in. The progress management unit can check the learner's progress and modify the learning plan as needed. The problem provision unit provides individually optimized practice problems and explanations based on the progress information obtained by the progress management unit. For example, the problem provision unit provides problems and explanations specifically tailored to areas where the learner is weak. The problem provision unit enables the learner to proceed with learning efficiently. The question response unit responds to questions in real time based on the practice problems provided by the problem provision unit. The question support unit provides immediate answers to questions, for example, if a learner has a question while solving a problem. The question support unit enables learners to proceed with their learning smoothly. As a result, the next-generation support system according to this embodiment can improve learning efficiency by automatically generating a learning plan based on the learner's goal setting, visualizing progress, providing individually optimized practice problems and explanations, and responding to questions in real time.

[0067] The plan generation unit automatically generates learning plans based on the learner's goal setting. Specifically, when a learner sets a target score for a qualification exam, the unit creates a learning plan based on that goal. The plan generation unit considers the learner's past learning history and current learning status to propose the optimal learning content and schedule. For example, it analyzes what subjects the learner has studied in the past and their level of understanding, and customizes the learning plan based on the results. Furthermore, the plan generation unit can appropriately allocate learning time and break times to match the learner's lifestyle and learning style. For example, it proposes a schedule that allows night owls to concentrate on learning at night, and creates a plan that starts early in the morning for early morning learners. In addition, the plan generation unit can set regular goal setting and progress check timings to maintain the learner's motivation. This allows learners to learn at their own pace without undue pressure and to efficiently progress toward achieving their goals. Furthermore, the plan generation unit can use AI to analyze the learner's learning patterns and propose the optimal learning method. For example, the AI ​​determines which learning method is most effective based on the learner's past learning data and reflects the results in the learning plan. This allows the plan generation unit to provide a learning plan optimized for each individual learner, maximizing learning efficiency.

[0068] The progress management unit tracks learning history and visualizes progress based on the learning plan generated by the plan generation unit. Specifically, it can understand how much time learners spend studying, which areas they excel in, and which areas they struggle with. The progress management unit monitors in real time whether learners are progressing according to the learning plan and visually displays the progress using graphs and charts. For example, it can display how much a learner has studied in a week using a bar graph and show their level of understanding for each subject using a radar chart. This allows learners to grasp their learning status at a glance and decide which areas they should focus on. Furthermore, the progress management unit can automatically adjust the learning plan according to the learner's progress. For example, if a learner is ahead of schedule, the next learning content will be provided ahead of schedule; conversely, if they are behind schedule, the learning plan will be adjusted to ensure they are studying at a manageable pace. In addition, the progress management unit can provide feedback and rewards based on achievement to maintain learner motivation. For example, badges or points can be awarded when certain goals are achieved, making learning more enjoyable and encouraging learners to continue studying. This allows the progress management department to accurately grasp the learners' learning progress and provide appropriate support, thereby improving learning efficiency.

[0069] The Problem Provisioning Department provides individually optimized practice problems and explanations based on progress information obtained by the Progress Management Department. Specifically, it provides problems and explanations tailored to areas where learners struggle. The Problem Provisioning Department uses AI to analyze learners' understanding and select the most suitable problems. For example, if a learner has difficulty in a particular area of ​​mathematics, it will provide a concentrated supply of problems related to that area and detailed explanations to deepen their understanding. The Problem Provisioning Department can also adjust the difficulty level of the problems according to the learner's progress. For example, once a learner has mastered basic problems, it will then provide applied and advanced problems, gradually increasing the difficulty. Conversely, if a learner is struggling with a problem, it will return to basic problems and encourage them to study again. Furthermore, based on the learner's answer history, the Problem Provisioning Department can identify points where learners tend to make mistakes or areas where their understanding is insufficient, and provide focused explanations. This allows learners to overcome their weaknesses and progress efficiently in their learning. The Problem Provisioning Department can also provide real-time feedback to learners as they solve problems. For example, immediately after a learner solves a problem, the system determines whether the answer is correct or incorrect. If correct, the learner moves on to the next problem; if incorrect, an explanation is displayed to deepen their understanding. This allows the problem-providing unit to support learners in efficiently progressing through their studies and maximize learning effectiveness.

[0070] The question support unit responds to questions in real time based on the practice problems provided by the problem-providing unit. Specifically, if a learner has a question while solving a problem, it will provide an immediate answer. The question support unit uses AI to analyze the content of the learner's question and generate an appropriate answer. For example, if a learner doesn't know how to use a particular formula while solving a math problem, it will show how to use that formula and provide related example problems. In addition, the question support unit can refer to frequently asked questions and past questions based on the learner's question history to provide quick and accurate answers. Furthermore, the question support unit improves usability by designing the interface for learners to input questions. For example, it introduces voice input and chat-style question support to allow learners to ask questions smoothly. The question support unit can also present relevant materials and references when learners ask questions to promote deeper understanding. This allows learners to proceed with their learning while resolving their doubts, improving learning efficiency. Furthermore, the question support unit can analyze the content of learners' questions to identify common doubts and areas of insufficient understanding, and reflect this in future learning plans. This allows the question-answering department to support learners so they can progress through their studies smoothly, maximizing learning effectiveness.

[0071] The plan generation unit can automatically generate an optimized learning plan based on the learner's goal setting. For example, if a learner sets a target score for a qualification exam, the plan generation unit will create a learning plan based on that goal. The plan generation unit can automatically generate a learning plan that includes learning content and schedule. Some or all of the above-described processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the learner's goal setting into a generation AI, and the generation AI can generate an optimized learning plan. This allows the system to provide learners with the most suitable learning plan by automatically generating an optimized learning plan based on their goal setting.

[0072] The progress management unit can track learners' learning history and visualize their progress. For example, the progress management unit can understand how much time learners spend studying, which areas they are strong in, and which areas they struggle with. The progress management unit can check learners' progress and revise their learning plans as needed. Some or all of the above processes in the progress management unit may be performed using or without a generative AI. For example, the progress management unit can input learners' learning history into a generative AI, which can then visualize their progress. This allows learners to track their learning history and visualize their progress, enabling them to check their progress and revise their learning plans.

[0073] The problem-providing unit can provide individually optimized practice problems and detailed explanations. For example, the problem-providing unit can provide problems and explanations specifically tailored to areas where the learner struggles. The problem-providing unit enables learners to progress through their learning efficiently. Some or all of the above-described processes in the problem-providing unit may be performed using a generative AI, or they may not. For example, the problem-providing unit can input the learner's areas of difficulty into a generative AI, which can then generate problems and provide explanations tailored to those areas. This allows learners to progress through their learning efficiently by providing individually optimized practice problems and detailed explanations.

[0074] The question-answering unit responds to questions in real time, resolving any points of confusion on the spot. For example, if a learner has a question while solving a problem, the question-answering unit will provide an immediate answer. The question-answering unit ensures that learners can progress smoothly through their learning. Some or all of the above-described processes in the question-answering unit may be performed using a generative AI, or they may not. For example, the question-answering unit can input the learner's question into a generative AI, which can immediately generate an answer. This allows for real-time responses to questions and immediate resolution of points of confusion, enabling learners to progress smoothly through their learning.

[0075] The plan generation unit can automatically generate study plans based on goals for qualification exams or university entrance exams. For example, when a learner sets a target score for a qualification exam, the plan generation unit creates a study plan based on that goal. The plan generation unit can automatically generate a study plan that includes study content and schedule. Some or all of the above-described processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the goals for qualification exams or university entrance exams into a generation AI, which can then generate an optimized study plan. This allows the system to provide learners with the most suitable study plan by automatically generating a study plan based on their goals for qualification exams or university entrance exams.

[0076] The progress management unit can identify learners' strengths and weaknesses and revise their learning plans accordingly. For example, the progress management unit can determine which subjects learners excel in and which they struggle with. The progress management unit can monitor learners' progress and revise their learning plans as needed. Some or all of the above processes in the progress management unit may be performed using a generative AI, or they may not. For example, the progress management unit can input learners' strengths and weaknesses into a generative AI, which can then revise the learning plan. This allows the system to identify learners' strengths and weaknesses and revise the learning plan accordingly, thereby providing learners with the most suitable learning plan.

[0077] The problem-providing unit can provide problems and explanations tailored to the learner's weak areas. For example, the problem-providing unit can provide problems and explanations tailored to a learner's weak areas. The problem-providing unit enables learners to progress through their learning efficiently. Some or all of the above processing in the problem-providing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the problem-providing unit can input the learner's weak areas into a generative AI, which can then generate problems tailored to those areas and provide explanations. This allows learners to progress through their learning efficiently by providing problems and explanations tailored to their weak areas.

[0078] The learning plan generation unit can estimate the learner's emotions and adjust the difficulty level of the learning plan based on the estimated emotions. For example, if the learner is stressed, the learning plan generation unit can generate a learning plan with a lower difficulty level. If the learner is relaxed, the learning plan generation unit can generate a learning plan with a higher difficulty level. If the learner is highly motivated, the learning plan generation unit can generate a challenging learning plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning plan generation unit may be performed using the generative AI or not. For example, the learning plan generation unit can input learner emotion data into the generative AI, which can then adjust the difficulty level of the learning plan. This allows for the provision of an optimal learning plan to the learner by adjusting the difficulty level of the learning plan based on the learner's emotions.

[0079] The plan generation unit can analyze the learner's past learning history and select the optimal learning method. For example, the plan generation unit can propose a similar method based on the learner's past successful learning methods. The plan generation unit can avoid learning methods that the learner has failed at in the past and propose a new method. The plan generation unit can select the most effective learning method from the learner's past learning history. Some or all of the above processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the learner's past learning history into a generation AI, which can then select the optimal learning method. This allows the plan generation unit to provide the learner with the most suitable learning method by analyzing their past learning history and selecting the optimal learning method.

[0080] The plan generation unit can optimize study time based on the learner's daily rhythm and schedule when generating a study plan. For example, the plan generation unit can suggest the optimal study time in accordance with the learner's daily rhythm. The plan generation unit can adjust the study time considering the learner's schedule. The plan generation unit can set an efficient study time based on the learner's daily rhythm and schedule. Some or all of the above processes in the plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the plan generation unit can input the learner's daily rhythm and schedule into a generation AI, which can then optimize the study time. This allows the learner to be provided with the optimal study time by optimizing the study time based on their daily rhythm and schedule.

[0081] The learning plan generation unit can estimate the learner's emotions and determine the priority of the learning plan based on the estimated emotions. For example, if the learner is stressed, the learning plan generation unit may set a lower priority. If the learner is relaxed, the learning plan generation unit may set a higher priority. If the learner is highly motivated, the learning plan generation unit may prioritize the most important learning items. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning plan generation unit may be performed using the generative AI or not. For example, the learning plan generation unit can input learner emotion data into the generative AI, which can then determine the priority of the learning plan. This allows the learning plan to be provided with the optimal learning plan by determining the priority of the learning plan based on the learner's emotions.

[0082] The plan generation unit can prioritize incorporating highly relevant learning content by considering the learner's geographical location when generating a learning plan. For example, if the learner is in a specific region, the plan generation unit can prioritize incorporating learning content related to that region. If the learner is traveling, the plan generation unit can suggest learning content related to the travel destination. If the learner is in a specific location, the plan generation unit can prioritize providing learning content related to that location. Some or all of the above processing in the plan generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the plan generation unit can input the learner's geographical location information into the generation AI, which can then prioritize incorporating highly relevant learning content. This allows the plan generation unit to provide learners with optimal learning content by prioritizing highly relevant learning content while considering the learner's geographical location.

[0083] The learning plan generation unit can analyze the learner's social media activity and incorporate relevant learning content when generating a learning plan. For example, the learning plan generation unit can adjust the learning plan based on the content the learner has shown interest in on social media. The learning plan generation unit can provide learning content related to topics the learner follows on social media. The learning plan generation unit can analyze the learner's social media activity and select the most suitable learning content. Some or all of the above processes in the learning plan generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the learning plan generation unit can input the learner's social media activity into a generation AI, which can then incorporate relevant learning content. This allows the learning plan generation unit to provide the learner with the most suitable learning content by analyzing the learner's social media activity and incorporating relevant learning content.

[0084] The progress management unit can estimate the learner's emotions and adjust the progress display method based on the estimated learner's emotions. For example, if the learner is feeling stressed, the progress management unit can provide a simple progress display. If the learner is relaxed, the progress management unit can provide a detailed progress display. If the learner is highly motivated, the progress management unit can provide a challenging progress display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using the generative AI or not. For example, the progress management unit can input learner emotion data into the generative AI, and the generative AI can adjust the progress display method. This allows for the provision of the optimal progress display for the learner by adjusting the progress display method based on the learner's emotions.

[0085] The progress management unit can predict progress by referring to the learner's past progress data during progress management. For example, the progress management unit predicts future progress based on the learner's past progress data. The progress management unit can analyze the learner's past progress data and make the optimal progress prediction. The progress management unit can predict progress by referring to the learner's past progress data. Some or all of the above processes in the progress management unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the progress management unit can input the learner's past progress data into a generative AI, and the generative AI can make a progress prediction. This allows the learner to be provided with the optimal progress prediction by referring to the learner's past progress data.

[0086] The progress management unit can analyze learners' strengths and weaknesses in detail and visualize their progress during progress management. For example, the progress management unit analyzes learners' strengths and weaknesses and visualizes their progress. The progress management unit can visualize progress based on learners' strengths and weaknesses. The progress management unit can analyze learners' strengths and weaknesses in detail and visualize their progress. Some or all of the above processing in the progress management unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the progress management unit can input learners' strengths and weaknesses into a generation AI, and the generation AI can visualize the progress. This allows for a detailed analysis of learners' strengths and weaknesses and visualization of their progress, thereby providing learners with the most optimal progress display.

[0087] The progress management unit can estimate the learner's emotions and adjust the importance of progress based on the estimated learner's emotions. For example, if the learner is stressed, the progress management unit can set the importance of progress lower. If the learner is relaxed, the progress management unit can set the importance of progress higher. If the learner is highly motivated, the progress management unit can prioritize the most important progress items. 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 generative AI or not. For example, the progress management unit can input learner emotion data into the generative AI, and the generative AI can adjust the importance of progress. This allows for optimal progress management for learners by adjusting the importance of progress based on their emotions.

[0088] The progress management unit can evaluate progress based on the content of assignments submitted by learners during progress management. For example, the progress management unit can analyze the content of assignments submitted by learners and evaluate progress. The progress management unit can evaluate progress based on the content of assignments submitted by learners. The progress management unit can analyze the content of assignments submitted by learners in detail and evaluate progress. Some or all of the above processes in the progress management unit may be performed using a generative AI, or not using a generative AI. For example, the progress management unit can input the content of assignments submitted by learners into a generative AI, and the generative AI can evaluate progress. This allows for the provision of the most appropriate progress evaluation to learners by evaluating progress based on the content of assignments submitted by learners.

[0089] The progress management unit can analyze progress by referring to learner-related market data during progress management. For example, the progress management unit can analyze progress by referring to learner-related market data. The progress management unit can analyze progress based on learner-related market data. The progress management unit can analyze progress by referring to learner-related market data in detail. Some or all of the above processes in the progress management unit may be performed using a generative AI, or not using a generative AI. For example, the progress management unit can input learner-related market data into a generative AI, and the generative AI can perform progress analysis. This allows for the provision of optimal progress analysis to learners by analyzing progress by referring to learner-related market data.

[0090] The question provider can estimate the learner's emotions and adjust the difficulty level of the questions based on the estimated emotions. For example, if the learner is stressed, the question provider can provide questions with a lower difficulty level. If the learner is relaxed, the question provider can provide questions with a higher difficulty level. If the learner is highly motivated, the question provider can provide challenging questions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question provider may be performed using the generative AI or not. For example, the question provider can input learner emotion data into the generative AI, which can then adjust the difficulty level of the questions. This allows the question provider to provide learners with the most suitable questions by adjusting the difficulty level based on their emotions.

[0091] The problem-providing unit can analyze the learner's past answer history to select the most suitable problem when providing a problem. For example, the problem-providing unit can select the most suitable problem based on the learner's past answer history. The problem-providing unit can analyze the learner's past answer history and provide the most suitable problem. The problem-providing unit can refer to the learner's past answer history to select the most suitable problem. Some or all of the above processing in the problem-providing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the problem-providing unit can input the learner's past answer history into a generative AI, which can then select the most suitable problem. This allows the system to provide the learner with the most suitable problem by analyzing the learner's past answer history and selecting the most suitable problem.

[0092] The question provider can adjust the frequency of questions presented according to the learner's strengths and weaknesses when providing questions. For example, the question provider can set a lower frequency for questions in the learner's strengths. The question provider can set a higher frequency for questions in the learner's weaknesses. The question provider can adjust the frequency of questions based on the learner's strengths and weaknesses. Some or all of the above processing in the question provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the question provider can input the learner's strengths and weaknesses into a generative AI, and the generative AI can adjust the frequency of questions. This allows the question provider to provide learners with the most suitable questions by adjusting the frequency of questions according to their strengths and weaknesses.

[0093] The question provider can estimate the learner's emotions and adjust the way the questions are displayed based on the estimated emotions. For example, if the learner is stressed, the question provider can provide a simple display method. If the learner is relaxed, the question provider can provide a detailed display method. If the learner is highly motivated, the question provider can provide a challenging display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question provider may be performed using the generative AI or not. For example, the question provider can input learner emotion data into the generative AI, which can then adjust the way the questions are displayed. This allows the question provider to provide the learner with the most suitable question display by adjusting the display method based on the learner's emotions.

[0094] The problem-providing unit can prioritize providing highly relevant problems by considering the learner's geographical location when providing problems. For example, if the learner is in a specific region, the problem-providing unit can prioritize providing problems related to that region. If the learner is traveling, the problem-providing unit can provide problems related to their travel destination. If the learner is in a specific location, the problem-providing unit can prioritize providing problems related to that location. Some or all of the above processing in the problem-providing unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the problem-providing unit can input the learner's geographical location information into a generative AI, which can then prioritize providing highly relevant problems. This allows the system to provide learners with the most relevant problems by prioritizing the provision of problems that consider their geographical location.

[0095] The question provider can analyze the learner's social media activity and provide relevant questions when providing questions. For example, the question provider can provide questions based on the content the learner has shown interest in on social media. The question provider can provide questions related to topics the learner follows on social media. The question provider can analyze the learner's social media activity and provide the most suitable questions. Some or all of the above processing in the question provider may be performed using a generative AI, or not. For example, the question provider can input the learner's social media activity into a generative AI, which can then provide relevant questions. This allows the system to provide the learner with the most suitable questions by analyzing their social media activity and providing relevant questions.

[0096] The question-answering unit can estimate the learner's emotions and adjust the way it answers questions based on those emotions. For example, if the learner is stressed, the unit can provide a simple answer. If the learner is relaxed, the unit can provide a detailed answer. If the learner is highly motivated, the unit can provide a challenging answer. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question-answering unit may be performed using the generative AI or not. For example, the question-answering unit can input learner emotion data into the generative AI, which can then adjust the way it answers questions. This allows the system to provide learners with the most appropriate answers by adjusting the way it answers questions based on their emotions.

[0097] The question response unit can provide the optimal answer by referring to the learner's past question history when responding to a question. For example, the question response unit can provide the optimal answer based on the learner's past question history. The question response unit can analyze the learner's past question history and provide the optimal answer. The question response unit can provide the optimal answer by referring to the learner's past question history. Some or all of the above processing in the question response unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question response unit can input the learner's past question history into a generative AI, and the generative AI can provide the optimal answer. In this way, by referring to the learner's past question history and providing the optimal answer, the learner can be provided with the best possible answer.

[0098] The question-answering unit can adjust the level of detail in its answers according to the learner's strengths and weaknesses. For example, it can provide a concise answer to a learner's strengths, and a detailed answer to a learner's weaknesses. The question-answering unit can adjust the level of detail in its answers based on the learner's strengths and weaknesses. Some or all of the above processing in the question-answering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question-answering unit can input the learner's strengths and weaknesses into a generative AI, which can then adjust the level of detail in its answers. This allows the system to provide learners with the most appropriate answers by adjusting the level of detail according to their strengths and weaknesses.

[0099] The question response unit can estimate the learner's emotions and determine the priority of questions based on the estimated emotions. For example, if the learner is stressed, the question response unit may set a lower priority for the question. If the learner is relaxed, the question response unit may set a higher priority for the question. If the learner is highly motivated, the question response unit may prioritize the most important questions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question response unit may be performed using the generative AI or not. For example, the question response unit can input learner emotion data into the generative AI, which can then determine the priority of the questions. This allows the system to provide learners with the most appropriate question response by determining the priority of questions based on their emotions.

[0100] The question-answering unit can prioritize providing highly relevant answers by considering the learner's geographical location when answering questions. For example, if the learner is in a specific region, the question-answering unit can prioritize providing answers related to that region. If the learner is traveling, the question-answering unit can provide answers related to their travel destination. If the learner is in a specific location, the question-answering unit can prioritize providing answers related to that location. Some or all of the above processing in the question-answering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the question-answering unit can input the learner's geographical location information into a generative AI, which can then prioritize providing highly relevant answers. This allows the learner to receive the most appropriate answers by prioritizing highly relevant answers while considering their geographical location.

[0101] The question-answering unit can analyze the learner's social media activity and provide relevant answers when answering questions. For example, the question-answering unit can provide answers based on the content the learner has shown interest in on social media. The question-answering unit can provide answers related to topics the learner follows on social media. The question-answering unit can analyze the learner's social media activity and provide the most appropriate answer. Some or all of the above processing in the question-answering unit may be performed using generative AI, or not. For example, the question-answering unit can input the learner's social media activity into a generative AI, which can then provide relevant answers. This allows the system to provide the learner with the most appropriate answer by analyzing their social media activity and providing relevant answers.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The next-generation support system can customize learning plans based on the learner's learning style. For example, visual learners can be provided with materials that make extensive use of diagrams and graphs. Auditory learners can be provided with audio explanations and podcast-style materials. Furthermore, tactile learners can be provided with interactive simulations and practical exercises. This allows for the provision of an optimal learning plan tailored to each learner's learning style.

[0104] The next-generation support system can estimate a learner's emotions and adjust the learning progress based on those emotions. For example, if a learner is tired, the learning burden can be reduced by slowing down the pace. If a learner is focused, the pace can be accelerated to allow for more efficient learning. Furthermore, if a learner has lost motivation, the progress can be temporarily paused to provide time for them to refresh. This enables flexible learning progress management that responds to the learner's emotions.

[0105] The next-generation support system can adjust learning plans by utilizing learners' health data. For example, it can suggest optimal study times based on learners' sleep data. Based on learners' exercise data, it can provide learning plans that incorporate appropriate breaks and exercise. Furthermore, based on learners' dietary data, it can suggest learning plans that consider nutritional balance. This allows for the provision of optimal learning plans tailored to each learner's health condition.

[0106] Next-generation support systems can estimate learners' emotions and adjust learning feedback based on those estimated emotions. For example, if a learner is feeling anxious, providing more positive feedback can provide reassurance. If a learner is confident, providing challenging feedback can encourage further growth. Furthermore, if a learner is feeling down, providing encouraging feedback can restore their motivation. This allows for the provision of appropriate feedback tailored to the learner's emotions.

[0107] The next-generation support system can analyze learners' learning histories and suggest the most suitable learning partners. For example, it can match learners with similar goals to promote collaborative learning. It can also match learners with different areas of expertise to promote complementary learning. Furthermore, it can suggest compatible learning partners based on the learner's personality and learning style. This allows for improved learning efficiency through collaboration among learners.

[0108] The next-generation support system can estimate a learner's emotions and adjust the learning environment based on those emotions. For example, if a learner is lacking concentration, ambient sounds or music can be provided to enhance their focus. If a learner is relaxed, a quiet environment can be provided to improve the quality of their learning. Furthermore, if a learner is stressed, a relaxing environment can be provided to reduce their stress. This allows for the provision of an optimal learning environment tailored to the learner's emotions.

[0109] The next-generation support system can predict learning progress based on the learner's learning history and provide reminders at the appropriate time. For example, if a learner is not progressing according to plan, it can send a reminder to encourage them to continue learning. If the learner is progressing according to plan, it can provide a reminder to check their progress. Furthermore, if the learner is approaching their goal, it can provide a reminder to give them a sense of accomplishment. In this way, it can provide appropriate reminders according to the learner's progress.

[0110] Next-generation support systems can estimate learners' emotions and provide content to maintain their motivation based on those estimated emotions. For example, if a learner has lost motivation, encouraging messages and success stories can help restore it. If a learner is motivated, challenging goals can be presented to encourage further growth. Furthermore, if a learner is tired, refreshing content can be provided to support their continued learning. In this way, content can be provided to maintain motivation according to the learner's emotions.

[0111] The next-generation support system can provide a dashboard to visualize learning progress based on the learner's learning history. For example, it can display a graph showing how much time a learner has spent studying. It can also color-code and display the learner's strengths and weaknesses. Furthermore, it can display the learner's progress toward their goals in real time. This allows learners to see their progress at a glance, which helps them revise their learning plans and maintain their motivation.

[0112] Next-generation support systems can estimate learners' emotions and adjust how learning progress is evaluated based on those estimated emotions. For example, if a learner is stressed, the pressure can be reduced by making progress evaluations more lenient. If a learner is relaxed, the quality of learning can be improved by providing detailed progress evaluations. Furthermore, if a learner is highly motivated, challenging progress evaluations can be provided to encourage further growth. This allows for the provision of optimal progress evaluations tailored to the learner's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The plan generation unit automatically generates a learning plan based on the learner's goal setting. For example, if a learner sets a target score for a certification exam, the unit will create a learning plan based on that goal. The plan generation unit can automatically generate a learning plan that includes learning content and schedule. Step 2: The progress management unit tracks learning history and visualizes progress based on the learning plan generated by the plan generation unit. For example, it can understand how much time learners spend studying, which areas they are strong in, and which areas they struggle with. The progress management unit can check learners' progress and revise the learning plan as needed. Step 3: The Problem Provisioning Department provides individually optimized practice problems and explanations based on the progress information obtained by the Progress Management Department. For example, for areas where learners struggle, the Problem Provisioning Department provides problems and explanations specifically tailored to those areas. The Problem Provisioning Department ensures that learners can progress through their studies efficiently. Step 4: The question support unit responds to questions in real time based on the practice problems provided by the problem-providing unit. For example, if a learner has a question while solving a problem, the unit will provide an immediate answer. The question support unit ensures that learners can progress through their learning smoothly.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the plan generation unit, progress management unit, problem provision unit, and question response unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the plan generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates a learning plan based on the learner's goal setting. The progress management unit is implemented by the specific processing unit 290 of the data processing device 12 and tracks the learning history and visualizes the progress. The problem provision unit is implemented by the control unit 46A of the smart device 14 and provides individually optimized problem exercises and explanations. The question response unit is implemented by the specific processing unit 290 of the data processing device 12 and responds to questions in real time. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the plan generation unit, progress management unit, problem provision unit, and question response unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the plan generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates a learning plan based on the learner's goal setting. The progress management unit is implemented by the specific processing unit 290 of the data processing device 12 and tracks the learning history and visualizes the progress. The problem provision unit is implemented by the control unit 46A of the smart glasses 214 and provides individually optimized problem exercises and explanations. The question response unit is implemented by the specific processing unit 290 of the data processing device 12 and responds to questions in real time. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the plan generation unit, progress management unit, problem provision unit, and question response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the plan generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates a learning plan based on the learner's goal setting. The progress management unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the learning history and visualizes the progress. The problem provision unit is implemented by the control unit 46A of the headset terminal 314 and provides individually optimized problem exercises and explanations. The question response unit is implemented by the specific processing unit 290 of the data processing unit 12 and responds to questions in real time. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the plan generation unit, progress management unit, problem provision unit, and question response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the plan generation unit is implemented by the control unit 46A of the robot 414 and automatically generates a learning plan based on the learner's goal setting. The progress management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and tracks the learning history and visualizes the progress. The problem provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides individually optimized problem exercises and explanations. The question response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and responds to questions in real time. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A plan generation unit that automatically generates a learning plan based on the learner's goal setting, A progress management unit tracks the learning history and visualizes the progress based on the learning plan generated by the aforementioned plan generation unit, A problem provision unit provides individually optimized problem exercises and explanations based on progress information obtained by the aforementioned progress management unit, The system includes a question response unit that responds to questions in real time based on the practice problems provided by the aforementioned problem-providing unit. A system characterized by the following features. (Note 2) The aforementioned plan generation unit, Automatically generates an optimized learning plan based on the learner's goal setting. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned progress management unit, Track learners' learning history and visualize their progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned problem-providing section, Provides individually optimized practice problems and detailed explanations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned question handling unit is: We respond to questions in real time and resolve any uncertainties on the spot. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned plan generation unit, Automatically generates study plans based on goals such as qualification exams and university entrance exams. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress management unit, Identify the learner's strengths and weaknesses, and revise the learning plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned problem-providing section, We provide problems and explanations specifically tailored to the learner's weak areas. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned plan generation unit, The system estimates the learner's emotions and adjusts the difficulty level of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned plan generation unit, Analyze the learner's past learning history and select the optimal learning method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned plan generation unit, When generating a study plan, the study time is optimized based on the learner's daily routine and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned plan generation unit, The system estimates learners' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan generation unit, When generating a learning plan, the system prioritizes incorporating highly relevant learning content by considering the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned plan generation unit, When generating learning plans, analyze learners' social media activity and incorporate relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned progress management unit, The system estimates the learner's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned progress management unit, When managing progress, predict progress by referring to the learner's past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned progress management unit, During progress management, we conduct a detailed analysis of learners' strengths and weaknesses and visualize their progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress management unit, The system estimates learners' emotions and adjusts the importance of progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress management unit, During progress management, the progress is evaluated based on the content of the assignments submitted by the learners. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress management unit, When managing progress, analyze progress by referring to relevant market data for learners. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned problem-providing section, The system estimates the learner's emotions and adjusts the difficulty level of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned problem-providing section, When providing questions, the system analyzes the learner's past answer history to select the most suitable questions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned problem-providing section, When providing questions, adjust the frequency of questions based on the learner's strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned problem-providing section, The system estimates the learner's emotions and adjusts how the questions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned problem-providing section, When providing problems, we prioritize providing highly relevant problems by taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned problem-providing section, When providing questions, we analyze learners' social media activity and provide relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question handling unit is: The system estimates the learner's emotions and adjusts how they answer questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question handling unit is: When answering questions, refer to the learner's past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned question handling unit is: When answering questions, adjust the level of detail in the answers according to the learner's strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned question handling unit is: The system estimates the learner's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned question handling unit is: When answering questions, we prioritize providing highly relevant answers by considering the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned question handling unit is: When answering questions, analyze the learner's social media activity to provide relevant answers. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A plan generation unit that automatically generates a learning plan based on the learner's goal setting, A progress management unit tracks the learning history and visualizes the progress based on the learning plan generated by the aforementioned plan generation unit, A problem provision unit provides individually optimized problem exercises and explanations based on progress information obtained by the aforementioned progress management unit, The system includes a question response unit that responds to questions in real time based on the practice problems provided by the aforementioned problem-providing unit. A system characterized by the following features.

2. The aforementioned plan generation unit, Automatically generates an optimized learning plan based on the learner's goal setting. The system according to feature 1.

3. The aforementioned progress management unit, Track learners' learning history and visualize their progress. The system according to feature 1.

4. The aforementioned problem-providing section, Provides individually optimized practice problems and detailed explanations. The system according to feature 1.

5. The aforementioned question handling unit is: We respond to questions in real time and resolve any uncertainties on the spot. The system according to feature 1.

6. The aforementioned plan generation unit, Automatically generates study plans based on goals such as qualification exams and university entrance exams. The system according to feature 1.

7. The aforementioned progress management unit, Identify the learner's strengths and weaknesses, and revise the learning plan accordingly. The system according to feature 1.

8. The aforementioned problem-providing section, We provide problems and explanations specifically tailored to the learner's weak areas. The system according to feature 1.

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