system

Abstract

The system according to this embodiment aims to measure the user's academic ability, create an efficient learning plan, manage progress, and suggest appropriate learning materials and problems. [Solution] The system according to the embodiment comprises an academic ability measurement unit, a learning plan unit, a progress management unit, a teaching material suggestion unit, and an advice unit. The academic ability measurement unit measures the user's academic ability. The learning plan unit creates a learning plan based on the academic ability measured by the academic ability measurement unit. The progress management unit manages the learning progress based on the learning plan created by the learning plan unit. The teaching material suggestion unit suggests appropriate teaching materials and problems based on the progress managed by the progress management unit. The advice unit provides advice and support on learning methods based on the teaching materials and problems suggested by the teaching material suggestion unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there is a problem that the formulation and progress management of a learning plan for academic improvement are carried out individually, and efficient learning support is not sufficiently provided.

[0005] The system according to the embodiment aims to measure the user's academic ability, formulate an efficient learning plan, manage the progress, and propose appropriate teaching materials and questions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an academic ability measurement unit, a learning plan unit, a progress management unit, a teaching material suggestion unit, and an advice unit. The academic ability measurement unit measures the user's academic ability. The learning plan unit creates a learning plan based on the academic ability measured by the academic ability measurement unit. The progress management unit manages learning progress based on the learning plan created by the learning plan unit. The teaching material suggestion unit suggests appropriate teaching materials and problems based on the progress managed by the progress management unit. The advice unit provides advice and support on learning methods based on the teaching materials and problems suggested by the teaching material suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can measure the user's academic ability, create an efficient learning plan, manage progress, and suggest appropriate learning materials and 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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The academic ability improvement support system according to an embodiment of the present invention is a system that provides a platform and services for junior high school students to measure their academic ability and efficiently improve the necessary academic ability in order to pass the entrance exam for their desired high school. This academic ability improvement support system accurately measures the current academic ability based on the passing criteria of the desired high school and provides the necessary learning steps by working backward from there. The aim is to create a step-by-step learning plan on a monthly and yearly basis and to manage and support the progress of academic ability improvement. Academic ability is measured regularly, appropriate teaching materials and problems are suggested, and advice and assistance on learning methods are provided according to the progress. For example, the user inputs their desired school and measures their current academic ability. The AI ​​agent compares the passing criteria of the desired school with the current academic ability and works backward from there to calculate the necessary steps for improving academic ability. For example, if the student lacks basic knowledge in mathematics, the AI ​​agent suggests learning steps from basic to advanced levels. Next, a step-by-step learning plan is created on a monthly and yearly basis. The AI ​​agent monitors the user's learning progress in real time and adjusts the learning plan as needed. For example, if the student is not progressing according to plan, the AI ​​agent suggests additional study time or advises changing learning methods. Furthermore, academic ability is measured regularly. The AI ​​agent measures the user's academic ability regularly and understands their progress. This allows users to track their academic progress and take necessary steps. It also suggests appropriate learning materials and problems. The AI ​​agent automatically selects and provides learning materials and problems tailored to the user's academic level. For example, if a user lacks foundational math skills, it will suggest materials containing many basic problems. Finally, it provides advice and support on learning methods based on progress. The AI ​​agent analyzes the user's learning progress and provides necessary advice and support. For example, if a learning method is ineffective, it will suggest an alternative or offer advice to boost motivation. This system allows junior high school students to efficiently improve their academic abilities in order to pass their desired schools' entrance exams. Furthermore, it reduces the financial burden on parents, enabling learning support for the entire family. In short, this academic improvement support system allows junior high school students to efficiently improve their academic abilities in order to pass their desired schools' entrance exams.

[0029] The academic ability improvement support system according to this embodiment comprises an academic ability measurement unit, a learning plan unit, a progress management unit, a teaching material suggestion unit, and an advice unit. The academic ability measurement unit measures the user's academic ability. The academic ability measurement unit compares the current academic ability with the passing criteria of the desired school entered by the user, and works backward to calculate the necessary steps for improving academic ability. The academic ability measurement unit measures the user's academic ability using AI, for example, and analyzes the gap with the passing criteria. The academic ability measurement unit periodically measures the user's academic ability and grasps the progress. The learning plan unit creates a learning plan based on the academic ability measured by the academic ability measurement unit. The learning plan unit creates a step-by-step learning plan, for example, on a monthly and yearly basis. The learning plan unit monitors the user's learning progress in real time using AI, for example, and adjusts the learning plan as needed. The learning plan unit suggests additional study time or advises changing the learning method if the plan is not progressing as intended. The progress management unit manages learning progress based on the learning plan created by the learning plan unit. The Progress Management Department, for example, monitors users' learning progress in real time and adjusts the learning plan as needed. The Progress Management Department, for example, analyzes users' learning progress using AI and adjusts the learning plan according to the progress. The Progress Management Department, for example, periodically evaluates users' learning progress and takes necessary measures. The Material Proposal Department proposes appropriate materials and problems based on the progress managed by the Progress Management Department. The Material Proposal Department, for example, automatically selects and provides materials and problems according to the user's academic ability. The Material Proposal Department, for example, uses AI to select and provide materials and problems according to the user's academic ability. The Material Proposal Department, for example, proposes materials that include many basic problems according to the user's academic ability. The Advice Department provides advice and support on learning methods based on the materials and problems proposed by the Material Proposal Department. The Advice Department, for example, analyzes users' learning progress and provides necessary advice and support. The Advice Department, for example, uses AI to analyze users' learning progress and proposes effective learning methods. The Advice Department, for example, if a learning method is not effective, proposes an alternative learning method or provides advice to increase learning motivation.As a result, the academic performance improvement support system according to this embodiment can efficiently measure the user's academic ability, create a learning plan, manage progress, suggest appropriate learning materials and problems, and provide advice and support on learning methods.

[0030] The Academic Ability Measurement Department measures the user's academic ability. For example, it compares the current academic ability with the passing criteria of the user's desired school and calculates the necessary steps for improvement. Specifically, when the user enters the passing criteria for their desired school, the Academic Ability Measurement Department retrieves those criteria from the database and compares them to the user's current academic ability. This comparison uses advanced AI-powered analysis techniques to analyze the user's strengths and weaknesses in detail. For example, if the user has weaknesses in a specific area of ​​mathematics, it suggests learning steps that focus on that area. The Academic Ability Measurement Department regularly measures the user's academic ability and tracks their progress. Regular measurements use online tests and quiz-style questions, and these results are analyzed in real time. The AI ​​analyzes the user's answer patterns and response times to track fluctuations in academic ability. This allows for early detection of improvements or stagnation in the user's academic ability and enables appropriate countermeasures to be taken. Furthermore, the Academic Ability Measurement Department creates an individualized learning profile based on the user's learning history and past test results. This profile meticulously records the user's learning tendencies, strengths, and weaknesses, which can be used to develop future learning plans. This allows the academic ability measurement unit to efficiently and accurately measure the user's academic ability and provide support tailored to their individual learning needs.

[0031] The Learning Planning Department creates learning plans based on academic ability measured by the Academic Ability Measurement Department. For example, the Learning Planning Department creates step-by-step learning plans on a monthly and yearly basis. Specifically, it sets short-term and long-term goals based on the user's academic ability measurement results and creates a corresponding learning schedule. AI is used to monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if a user is not progressing according to plan, the AI ​​analyzes the cause and suggests additional study time or advice on changing learning methods. The Learning Planning Department provides flexible learning plans tailored to the user's learning style and lifestyle. For example, it reduces the learning load during busy periods and suggests intensive study during periods of free time. Furthermore, to maintain user motivation, the Learning Planning Department sets small, achievable goals and devise ways to help users feel a sense of accomplishment. In addition, the Learning Planning Department continuously improves learning plans based on user feedback. For example, if a user does not feel that a particular learning method is effective, it suggests an alternative. The Learning Planning Department also analyzes the user's learning history, taking into account past successes and failures, to create more effective learning plans. This allows the learning planning unit to provide maximum support for improving the user's academic ability and enable efficient learning.

[0032] The Progress Management Department manages learning progress based on the learning plans created by the Learning Planning Department. For example, the Progress Management Department monitors users' learning progress in real time and adjusts the learning plan as needed. Specifically, it regularly checks whether users are progressing according to the learning plan and evaluates their progress. It uses AI to analyze users' learning progress and adjusts the learning plan according to the progress. For example, if a user is not progressing according to the plan, the AI ​​identifies the cause and suggests revising the learning plan. The Progress Management Department regularly evaluates users' learning progress and takes necessary measures. For example, if a user is struggling in a particular area, it suggests additional learning focused on that area. The Progress Management Department also accumulates user learning data and analyzes long-term learning trends. This allows it to understand users' learning patterns and fluctuations in progress, enabling it to provide more effective learning support. Furthermore, the Progress Management Department provides feedback according to the progress to maintain user motivation. For example, when a user achieves a goal, it provides praise and rewards to increase motivation. The Progress Management Department also shares user learning progress in cooperation with other systems and departments to realize comprehensive learning support. This allows the progress management unit to efficiently manage users' learning progress and support their academic improvement.

[0033] The curriculum proposal department proposes appropriate learning materials and problems based on the progress managed by the progress management department. Specifically, it automatically selects and provides learning materials and problems that match the user's academic ability. It uses AI to select and provide learning materials and problems that match the user's academic ability. For example, if a user has weaknesses in a particular area, it proposes learning materials and problems that focus on that area. The curriculum proposal department proposes learning materials that include many basic problems according to the user's academic ability. For example, if a user needs to strengthen their basic knowledge, it proposes learning materials that include many basic problems, allowing the user to solidify their foundation. The curriculum proposal department also proposes learning materials that match the user's learning style and preferences. For example, for users who prefer visual learning, it proposes learning materials that make extensive use of diagrams and graphs, and for users who prefer auditory learning, it proposes learning materials with audio explanations. Furthermore, the curriculum proposal department adjusts the difficulty level of the learning materials according to the user's learning progress. For example, if a user deepens their understanding by repeatedly solving a particular problem, it proposes problems of the same theme but at different difficulty levels. This allows the user to improve their academic ability step by step. The curriculum proposal department continuously improves the quality of the learning materials based on user feedback. For example, if a user does not feel that a particular learning material is effective, alternative materials will be suggested. Furthermore, the learning material suggestion department can analyze the user's learning history and re-suggest materials that were effective in the past. This allows the learning material suggestion department to maximize support for the user's academic improvement and achieve efficient learning.

[0034] The Advice Department provides advice and support on learning methods based on the learning materials and problems proposed by the Learning Materials Proposal Department. Specifically, it analyzes the user's learning progress and provides necessary advice and support. It uses AI to analyze the user's learning progress and propose effective learning methods. For example, if a user is not feeling the effects of a particular learning method, it will suggest an alternative. The Advice Department provides advice tailored to the user's learning style and preferences. For example, for users who prefer visual learning, it will suggest learning methods that make extensive use of diagrams and graphs, and for users who prefer auditory learning, it will suggest learning methods with audio explanations. The Advice Department also provides advice to increase the user's motivation. For example, if a user has lost motivation to learn, it will suggest goal setting and reward systems to boost motivation. Furthermore, the Advice Department adjusts the content of its advice according to the user's learning progress. For example, if a user is struggling in a particular area, it will provide specific advice focused on that area. The Advice Department continuously improves the quality of its advice based on user feedback. For example, if a user is not feeling the effects of a particular piece of advice, it will suggest alternative advice. The Advice Department can also analyze the user's learning history and re-suggest advice that was effective in the past. This allows the advice section to provide maximum support for improving the user's academic performance and enable efficient learning.

[0035] The academic ability measurement unit can compare the current academic ability with the passing criteria of the desired school and work backward to calculate the necessary steps for improving academic ability. For example, the academic ability measurement unit can obtain the passing criteria of the desired school from a database and compare it with the current academic ability. For example, the academic ability measurement unit can use AI to compare the current academic ability with the passing criteria of the desired school and work backward to calculate the necessary steps for improving academic ability. For example, the academic ability measurement unit can measure the user's academic ability and analyze the gap with the passing criteria. This allows it to work backward to calculate the necessary steps for improving academic ability based on the passing criteria of the desired school. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can work backward to calculate the necessary steps for improving academic ability using an AI model that takes the passing criteria of the desired school and the current academic ability as input and outputs the necessary steps for improving academic ability.

[0036] The learning planning unit can create step-by-step learning plans on a monthly and yearly basis. For example, the learning planning unit can create step-by-step learning plans on a monthly and yearly basis based on the user's academic ability measurement results. For example, the learning planning unit can use AI to analyze the user's academic ability measurement results and create step-by-step learning plans. For example, the learning planning unit can create step-by-step learning plans on a monthly and yearly basis based on the user's academic ability improvement goals. This makes it possible to create step-by-step learning plans on a monthly and yearly basis. Some or all of the above-described processes in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can create a learning plan using an AI model that takes the user's academic ability measurement results as input and outputs a step-by-step learning plan.

[0037] The progress management unit can monitor the user's learning progress in real time and adjust the learning plan as needed. For example, the progress management unit can monitor the user's learning progress in real time and adjust the learning plan according to the progress status. For example, the progress management unit can analyze the user's learning progress using AI and adjust the learning plan as needed. For example, the progress management unit can periodically evaluate the user's learning progress and take necessary measures. This allows the user's learning progress to be monitored in real time and the learning plan to be adjusted as needed. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can adjust the learning plan using an AI model that takes user learning progress data as input and outputs adjustments to the learning plan.

[0038] The curriculum proposal unit can automatically select and provide curriculum materials and problems that match the user's academic ability. For example, the curriculum proposal unit can select and provide appropriate curriculum materials and problems based on the user's academic ability measurement results. For example, the curriculum proposal unit can use AI to analyze the user's academic ability measurement results and select appropriate curriculum materials and problems. For example, the curriculum proposal unit can propose curriculum materials that include many basic problems according to the user's academic ability. This enables the automatic selection and provision of curriculum materials and problems that match the user's academic ability. Some or all of the above processing in the curriculum proposal unit may be performed using AI, for example, or without AI. For example, the curriculum proposal unit can select curriculum materials and problems using an AI model that takes the user's academic ability measurement results as input and outputs appropriate curriculum materials and problems.

[0039] The advice unit can analyze the user's learning progress and provide necessary advice and support. For example, the advice unit can analyze the user's learning progress and suggest effective learning methods. For example, the advice unit can use AI to analyze the user's learning progress and provide necessary advice and support. For example, if a learning method is not effective, the advice unit can suggest an alternative learning method or provide advice to increase learning motivation. This allows the advice unit to analyze the user's learning progress and provide necessary advice and support. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice and support using an AI model that takes user learning progress data as input and outputs advice and support.

[0040] The academic ability measurement unit can analyze the user's past academic ability measurement results and select the optimal measurement method. For example, the academic ability measurement unit may prioritize selecting measurement methods in which the user has previously achieved high scores. For example, the academic ability measurement unit may avoid measurement methods that the user struggles with and use methods in which the user excels. For example, the academic ability measurement unit may use AI to select the most effective measurement method based on the user's past measurement results. In this way, the optimal measurement method can be selected by analyzing the user's past academic ability measurement results. Some or all of the above processing in the academic ability measurement unit may be performed using AI, or not using AI. For example, the academic ability measurement unit can select the optimal measurement method using an AI model that takes the user's past academic ability measurement results as input and outputs the optimal measurement method.

[0041] The academic ability measurement unit can filter questions based on the user's current learning status and areas of interest during the academic ability measurement process. For example, the academic ability measurement unit can prioritize questions related to the subject the user is currently studying. For example, the academic ability measurement unit can present questions that are of interest to the user based on their areas of interest. For example, the academic ability measurement unit can present questions of appropriate difficulty according to the user's learning progress. This allows for more accurate academic ability measurement by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can perform filtering using an AI model that takes the user's current learning status and areas of interest as input and outputs filtered questions.

[0042] The academic ability measurement unit can prioritize highly relevant measurement items when measuring academic ability, taking into account the user's geographical location information. For example, the academic ability measurement unit can select measurement items based on the educational curriculum of the area where the user lives. For example, the academic ability measurement unit can prioritize questions that are frequently asked in the user's area. For example, the academic ability measurement unit can ask region-specific questions based on the user's geographical location information. By prioritizing highly relevant measurement items while considering the user's geographical location information, a more appropriate measurement of academic ability becomes possible. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can select measurement items using an AI model that takes the user's geographical location information as input and outputs highly relevant measurement items.

[0043] The academic ability measurement unit can analyze the user's social media activity during academic ability measurement and add relevant academic ability measurement items. For example, the academic ability measurement unit can ask questions related to topics the user has shown interest in on social media. For example, the academic ability measurement unit can identify areas of interest from the user's social media activity and add questions in those areas. For example, the academic ability measurement unit can select relevant academic ability measurement items based on the user's social media activity history. In this way, relevant academic ability measurement items can be added by analyzing the user's social media activity. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can add academic ability measurement items using an AI model that takes the user's social media activity data as input and outputs relevant academic ability measurement items.

[0044] The learning planning unit can adjust the level of detail in a learning plan based on the importance of the subject matter. For example, the learning planning unit can create a detailed learning plan for important subjects, and a simplified learning plan for less important subjects. The learning planning unit adjusts the level of detail in the learning plan according to the importance of the subject matter. This allows for the provision of more effective learning plans by adjusting the level of detail based on the importance of the subject matter. Some or all of the above processes in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can use an AI model that takes the importance of subject matter data as input to adjust the level of detail in the learning plan.

[0045] The learning planning unit can apply different planning algorithms depending on the academic ability category when formulating a learning plan. For example, the learning planning unit might apply an algorithm that includes step-by-step development to a mathematics learning plan. For example, it might apply an algorithm that emphasizes repetitive learning to an English learning plan. For example, it might apply an algorithm that emphasizes experiments and observations to a science learning plan. By applying different planning algorithms depending on the academic ability category, it is possible to provide a more effective learning plan. Some or all of the above processing in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can create a learning plan using an AI model that takes academic ability category data as input and applies different planning algorithms.

[0046] The learning planning unit can determine the priority of learning plans based on the timing of academic ability measurements when formulating learning plans. For example, if an important exam is approaching, the learning planning unit will prioritize the learning plan for that subject. For example, the learning planning unit will determine which subjects should be prioritized for learning based on the results of academic ability measurements. For example, the learning planning unit will adjust the priority of learning plans according to the timing of academic ability measurements. By determining the priority of plans based on the timing of academic ability measurements, a more effective learning plan can be provided. Some or all of the above processes in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can determine the priority of learning plans using an AI model that takes academic ability measurement timing data as input and determines the priority of plans.

[0047] The learning planning unit can adjust the order of learning plans based on the relationships between academic abilities when formulating a learning plan. For example, the learning planning unit can adjust the order of learning plans by considering the relationship between mathematics and physics. For example, the learning planning unit can adjust the order of learning plans by considering the relationship between English and social studies. For example, the learning planning unit can optimize the order of learning plans based on the relationships between academic abilities. This makes it possible to provide a more effective learning plan by adjusting the order of plans based on the relationships between academic abilities. Some or all of the above processing in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can adjust the order of learning plans using an AI model that takes academic ability relationship data as input and adjusts the order of plans.

[0048] The progress management unit can improve the accuracy of its management by considering the interrelationships of academic abilities during progress management. For example, the progress management unit can manage the progress of mathematics and physics in a linked manner. For example, the progress management unit can manage the progress of English and social studies in a linked manner. For example, the progress management unit can improve the accuracy of its progress management by considering the interrelationships of academic abilities. This makes more effective progress management possible by improving the accuracy of management by considering the interrelationships of academic abilities. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can perform progress management using an AI model that improves the accuracy of management, with data on the interrelationships of academic abilities as input.

[0049] The progress management unit can manage progress while taking user attribute information into consideration. For example, the progress management unit can adjust the progress management criteria according to the user's age. For example, the progress management unit can adjust the progress management method according to the user's learning style. For example, the progress management unit can perform optimal progress management based on the user's attribute information. This makes more effective progress management possible by taking user attribute information into consideration. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can perform progress management using an AI model that takes user attribute information as input and performs progress management.

[0050] The progress management unit can manage progress while considering the geographical distribution of academic ability. For example, the progress management unit can manage progress based on the educational curriculum of the user's region. For example, the progress management unit can manage progress while considering the questions that frequently appear in the user's region. For example, the progress management unit can perform optimal progress management based on the geographical distribution of academic ability. This makes more effective progress management possible by considering the geographical distribution of academic ability. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can perform progress management using an AI model that takes geographical distribution data of academic ability as input.

[0051] The progress management unit can improve the accuracy of its progress management by referring to relevant literature on academic ability. For example, the progress management unit sets progress management standards based on relevant literature on academic ability. For example, the progress management unit improves its progress management methods by referring to relevant literature on academic ability. For example, the progress management unit improves the accuracy of its progress management based on relevant literature on academic ability. As a result, by improving the accuracy of management by referring to relevant literature on academic ability, more effective progress management becomes possible. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can perform progress management using an AI model that improves the accuracy of management, with relevant literature on academic ability as input.

[0052] The curriculum proposal department can improve the accuracy of its proposals by considering the interrelationships of academic abilities. For example, the curriculum proposal department can propose materials that are useful for both subjects by considering the relationship between mathematics and physics. For example, the curriculum proposal department can propose materials that are useful for both subjects by considering the relationship between English and social studies. For example, the curriculum proposal department can propose the most suitable materials based on the interrelationships of academic abilities. By improving the accuracy of proposals by considering the interrelationships of academic abilities, more effective curriculum proposals become possible. Some or all of the above processing in the curriculum proposal department may be performed using AI, for example, or without AI. For example, the curriculum proposal department can take data on the interrelationships of academic abilities as input and make curriculum proposals using an AI model that improves the accuracy of proposals.

[0053] The curriculum proposal unit can make recommendations while considering the user's attribute information. For example, the curriculum proposal unit can propose appropriate curriculum materials according to the user's age. For example, the curriculum proposal unit can propose optimal curriculum materials according to the user's learning style. For example, the curriculum proposal unit can propose optimal curriculum materials based on the user's attribute information. By considering the user's attribute information when making recommendations, more effective curriculum proposals become possible. Some or all of the above processing in the curriculum proposal unit may be performed using AI, for example, or without AI. For example, the curriculum proposal unit can make curriculum proposals using an AI model that takes user attribute information as input and makes curriculum proposals.

[0054] The curriculum proposal unit can make curriculum proposals while considering the geographical distribution of academic ability. For example, the curriculum proposal unit can propose curriculum based on the educational curriculum of the area where the user lives. For example, the curriculum proposal unit can propose curriculum considering the types of questions that frequently appear in the user's area. For example, the curriculum proposal unit can propose the most suitable curriculum based on the geographical distribution of academic ability. By considering the geographical distribution of academic ability when making proposals, it becomes possible to propose more effective curriculum. Some or all of the above processing in the curriculum proposal unit may be performed using AI, for example, or without AI. For example, the curriculum proposal unit can make curriculum proposals using an AI model that takes geographical distribution data of academic ability as input.

[0055] The curriculum proposal department can improve the accuracy of its proposals by referring to relevant literature on academic ability when proposing curriculum materials. For example, the curriculum proposal department proposes the most suitable curriculum materials based on relevant literature on academic ability. For example, the curriculum proposal department sets selection criteria for curriculum materials by referring to relevant literature on academic ability. For example, the curriculum proposal department improves the accuracy of its proposals based on relevant literature on academic ability. By improving the accuracy of proposals by referring to relevant literature on academic ability, more effective curriculum proposals become possible. Some or all of the above processes in the curriculum proposal department may be performed using AI, for example, or without AI. For example, the curriculum proposal department can take data on relevant literature on academic ability as input and use an AI model to improve the accuracy of proposals to make curriculum proposals.

[0056] The advice unit can analyze the user's past learning behavior and select the optimal advice method when providing advice. For example, the advice unit provides advice based on learning methods that have been effective for the user in the past. For example, the advice unit selects the optimal advice method from the user's past learning behavior. For example, the advice unit analyzes the user's past learning behavior and provides effective advice. In this way, the optimal advice method can be selected by analyzing the user's past learning behavior. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the user's past learning behavior data as input and selects the optimal advice method.

[0057] The advice unit can customize the means of advice based on the user's current learning status when providing advice. For example, the advice unit can provide advice related to the subject the user is currently studying. For example, the advice unit can provide appropriate advice according to the user's learning progress. For example, the advice unit can provide optimal advice based on the user's current learning status. This makes it possible to provide more effective advice by customizing the means of advice based on the user's current learning status. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the user's current learning status data as input and customizes the means of advice.

[0058] The advice unit can select the optimal advice method by considering the user's geographical location information when providing advice. For example, the advice unit can provide advice based on the educational curriculum of the area where the user lives. For example, the advice unit can provide advice by considering the types of questions that frequently appear in the user's area. For example, the advice unit can provide optimal advice based on the user's geographical location information. By selecting the optimal advice method by considering the user's geographical location information, more effective advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the user's geographical location information as input and selects the optimal advice method.

[0059] The advice unit can analyze the user's social media activity and propose methods for providing advice. For example, the advice unit can provide advice related to topics the user has shown interest in on social media. For example, the advice unit can provide advice related to areas of interest based on the user's social media activity. For example, the advice unit can provide optimal advice based on the user's social media activity history. In this way, by analyzing the user's social media activity, it is possible to propose the optimal method for providing advice. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can take the user's social media activity data as input and provide advice using an AI model that proposes methods for providing advice.

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

[0061] The academic improvement support system can customize learning plans according to the user's learning style. For example, users who prefer visual learning will be offered learning materials that make extensive use of diagrams and graphs. Users who prefer auditory learning will be offered audio materials and podcasts. Furthermore, users who prefer practical learning will be provided with experiment- and project-based learning plans. This allows the system to provide the optimal learning plan tailored to each user's learning style.

[0062] The academic improvement support system can analyze a user's learning history and optimize their learning plan based on past learning patterns. For example, it prioritizes learning methods that were effective in the past and eliminates those that were less effective. It also provides special supplementary materials for areas where the user has struggled in the past. This allows the system to provide a more effective learning plan based on the user's past learning history.

[0063] The academic improvement support system can dynamically adjust the difficulty level of the learning plan according to the user's learning progress. For example, if the user is progressing according to the plan, the difficulty level of the next step will be increased. Conversely, if progress is slow, the difficulty level will be lowered and the user will be prompted to relearn from the basics. Also, if progress is fast in a particular area, learning in that area will be temporarily suspended and adjusted to focus on other areas. In this way, the system can provide an optimal learning plan tailored to the user's learning progress.

[0064] The academic improvement support system can adjust learning plans based on the user's learning environment. For example, it recommends studying in a library or study room for users who prefer a quiet environment. Conversely, it suggests appropriate music playlists for users who find studying while listening to music effective. It also provides online learning materials and webinars for users who find online learning effective. This allows the system to provide an optimal learning plan tailored to the user's learning environment.

[0065] The academic improvement support system can customize learning plans based on the user's learning goals. For example, users with short-term goals will be provided with a short-term learning plan for focused study. Conversely, users with long-term goals will be provided with a long-term learning plan for gradual progress. Furthermore, users aiming for specific exams or qualifications will be provided with a learning plan tailored to those exams. This allows the system to provide the optimal learning plan according to the user's learning goals.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The academic ability measurement unit measures the user's academic ability. For example, it compares the current academic ability with the passing criteria for the desired school entered by the user and works backward to calculate the necessary steps for academic improvement. It uses AI to measure the user's academic ability and analyzes the gap with the passing criteria. It also measures the user's academic ability regularly to track their progress. Step 2: The Learning Planning Department creates a learning plan based on the academic ability measured by the Academic Ability Measurement Department. For example, it creates a step-by-step learning plan on a monthly and yearly basis. Using AI, it monitors the user's learning progress in real time and adjusts the learning plan as needed. If the user is not progressing according to plan, it suggests additional study time or advises changing the learning method. Step 3: The progress management department manages learning progress based on the learning plan created by the learning planning department. For example, it monitors users' learning progress in real time and adjusts the learning plan as needed. It uses AI to analyze users' learning progress and adjusts the learning plan according to the progress. It regularly evaluates users' learning progress and takes necessary measures. Step 4: The curriculum proposal department proposes appropriate curriculum materials and problems based on the progress managed by the progress management department. For example, it automatically selects and provides curriculum materials and problems according to the user's academic level. It uses AI to select and provide curriculum materials and problems according to the user's academic level. It proposes curriculum materials that include many basic problems according to the user's academic level. Step 5: The Advice Department provides advice and support on learning methods based on the learning materials and problems proposed by the Learning Materials Proposal Department. For example, it analyzes the user's learning progress and provides necessary advice and support. It uses AI to analyze the user's learning progress and proposes effective learning methods. If the learning method is not effective, it proposes alternative learning methods or provides advice to increase learning motivation.

[0068] (Example of form 2) The academic ability improvement support system according to an embodiment of the present invention is a system that provides a platform and services for junior high school students to measure their academic ability and efficiently improve the necessary academic ability in order to pass the entrance exam for their desired high school. This academic ability improvement support system accurately measures the current academic ability based on the passing criteria of the desired high school and provides the necessary learning steps by working backward from there. The aim is to create a step-by-step learning plan on a monthly and yearly basis and to manage and support the progress of academic ability improvement. Academic ability is measured regularly, appropriate teaching materials and problems are suggested, and advice and assistance on learning methods are provided according to the progress. For example, the user inputs their desired school and measures their current academic ability. The AI ​​agent compares the passing criteria of the desired school with the current academic ability and works backward from there to calculate the necessary steps for improving academic ability. For example, if the student lacks basic knowledge in mathematics, the AI ​​agent suggests learning steps from basic to advanced levels. Next, a step-by-step learning plan is created on a monthly and yearly basis. The AI ​​agent monitors the user's learning progress in real time and adjusts the learning plan as needed. For example, if the student is not progressing according to plan, the AI ​​agent suggests additional study time or advises changing learning methods. Furthermore, academic ability is measured regularly. The AI ​​agent measures the user's academic ability regularly and understands their progress. This allows users to track their academic progress and take necessary steps. It also suggests appropriate learning materials and problems. The AI ​​agent automatically selects and provides learning materials and problems tailored to the user's academic level. For example, if a user lacks foundational math skills, it will suggest materials containing many basic problems. Finally, it provides advice and support on learning methods based on progress. The AI ​​agent analyzes the user's learning progress and provides necessary advice and support. For example, if a learning method is ineffective, it will suggest an alternative or offer advice to boost motivation. This system allows junior high school students to efficiently improve their academic abilities in order to pass their desired schools' entrance exams. Furthermore, it reduces the financial burden on parents, enabling learning support for the entire family. In short, this academic improvement support system allows junior high school students to efficiently improve their academic abilities in order to pass their desired schools' entrance exams.

[0069] The academic ability improvement support system according to this embodiment comprises an academic ability measurement unit, a learning plan unit, a progress management unit, a teaching material suggestion unit, and an advice unit. The academic ability measurement unit measures the user's academic ability. The academic ability measurement unit compares the current academic ability with the passing criteria of the desired school entered by the user, and works backward to calculate the necessary steps for improving academic ability. The academic ability measurement unit measures the user's academic ability using AI, for example, and analyzes the gap with the passing criteria. The academic ability measurement unit periodically measures the user's academic ability and grasps the progress. The learning plan unit creates a learning plan based on the academic ability measured by the academic ability measurement unit. The learning plan unit creates a step-by-step learning plan, for example, on a monthly and yearly basis. The learning plan unit monitors the user's learning progress in real time using AI, for example, and adjusts the learning plan as needed. The learning plan unit suggests additional study time or advises changing the learning method if the plan is not progressing as intended. The progress management unit manages learning progress based on the learning plan created by the learning plan unit. The Progress Management Department, for example, monitors users' learning progress in real time and adjusts the learning plan as needed. The Progress Management Department, for example, analyzes users' learning progress using AI and adjusts the learning plan according to the progress. The Progress Management Department, for example, periodically evaluates users' learning progress and takes necessary measures. The Material Proposal Department proposes appropriate materials and problems based on the progress managed by the Progress Management Department. The Material Proposal Department, for example, automatically selects and provides materials and problems according to the user's academic ability. The Material Proposal Department, for example, uses AI to select and provide materials and problems according to the user's academic ability. The Material Proposal Department, for example, proposes materials that include many basic problems according to the user's academic ability. The Advice Department provides advice and support on learning methods based on the materials and problems proposed by the Material Proposal Department. The Advice Department, for example, analyzes users' learning progress and provides necessary advice and support. The Advice Department, for example, uses AI to analyze users' learning progress and proposes effective learning methods. The Advice Department, for example, if a learning method is not effective, proposes an alternative learning method or provides advice to increase learning motivation.As a result, the academic performance improvement support system according to this embodiment can efficiently measure the user's academic ability, create a learning plan, manage progress, suggest appropriate learning materials and problems, and provide advice and support on learning methods.

[0070] The Academic Ability Measurement Department measures the user's academic ability. For example, it compares the current academic ability with the passing criteria of the user's desired school and calculates the necessary steps for improvement. Specifically, when the user enters the passing criteria for their desired school, the Academic Ability Measurement Department retrieves those criteria from the database and compares them to the user's current academic ability. This comparison uses advanced AI-powered analysis techniques to analyze the user's strengths and weaknesses in detail. For example, if the user has weaknesses in a specific area of ​​mathematics, it suggests learning steps that focus on that area. The Academic Ability Measurement Department regularly measures the user's academic ability and tracks their progress. Regular measurements use online tests and quiz-style questions, and these results are analyzed in real time. The AI ​​analyzes the user's answer patterns and response times to track fluctuations in academic ability. This allows for early detection of improvements or stagnation in the user's academic ability and enables appropriate countermeasures to be taken. Furthermore, the Academic Ability Measurement Department creates an individualized learning profile based on the user's learning history and past test results. This profile meticulously records the user's learning tendencies, strengths, and weaknesses, which can be used to develop future learning plans. This allows the academic ability measurement unit to efficiently and accurately measure the user's academic ability and provide support tailored to their individual learning needs.

[0071] The Learning Planning Department creates learning plans based on academic ability measured by the Academic Ability Measurement Department. For example, the Learning Planning Department creates step-by-step learning plans on a monthly and yearly basis. Specifically, it sets short-term and long-term goals based on the user's academic ability measurement results and creates a corresponding learning schedule. AI is used to monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if a user is not progressing according to plan, the AI ​​analyzes the cause and suggests additional study time or advice on changing learning methods. The Learning Planning Department provides flexible learning plans tailored to the user's learning style and lifestyle. For example, it reduces the learning load during busy periods and suggests intensive study during periods of free time. Furthermore, to maintain user motivation, the Learning Planning Department sets small, achievable goals and devise ways to help users feel a sense of accomplishment. In addition, the Learning Planning Department continuously improves learning plans based on user feedback. For example, if a user does not feel that a particular learning method is effective, it suggests an alternative. The Learning Planning Department also analyzes the user's learning history, taking into account past successes and failures, to create more effective learning plans. This allows the learning planning unit to provide maximum support for improving the user's academic ability and enable efficient learning.

[0072] The Progress Management Department manages learning progress based on the learning plans created by the Learning Planning Department. For example, the Progress Management Department monitors users' learning progress in real time and adjusts the learning plan as needed. Specifically, it regularly checks whether users are progressing according to the learning plan and evaluates their progress. It uses AI to analyze users' learning progress and adjusts the learning plan according to the progress. For example, if a user is not progressing according to the plan, the AI ​​identifies the cause and suggests revising the learning plan. The Progress Management Department regularly evaluates users' learning progress and takes necessary measures. For example, if a user is struggling in a particular area, it suggests additional learning focused on that area. The Progress Management Department also accumulates user learning data and analyzes long-term learning trends. This allows it to understand users' learning patterns and fluctuations in progress, enabling it to provide more effective learning support. Furthermore, the Progress Management Department provides feedback according to the progress to maintain user motivation. For example, when a user achieves a goal, it provides praise and rewards to increase motivation. The Progress Management Department also shares user learning progress in cooperation with other systems and departments to realize comprehensive learning support. This allows the progress management unit to efficiently manage users' learning progress and support their academic improvement.

[0073] The curriculum proposal department proposes appropriate learning materials and problems based on the progress managed by the progress management department. Specifically, it automatically selects and provides learning materials and problems that match the user's academic ability. It uses AI to select and provide learning materials and problems that match the user's academic ability. For example, if a user has weaknesses in a particular area, it proposes learning materials and problems that focus on that area. The curriculum proposal department proposes learning materials that include many basic problems according to the user's academic ability. For example, if a user needs to strengthen their basic knowledge, it proposes learning materials that include many basic problems, allowing the user to solidify their foundation. The curriculum proposal department also proposes learning materials that match the user's learning style and preferences. For example, for users who prefer visual learning, it proposes learning materials that make extensive use of diagrams and graphs, and for users who prefer auditory learning, it proposes learning materials with audio explanations. Furthermore, the curriculum proposal department adjusts the difficulty level of the learning materials according to the user's learning progress. For example, if a user deepens their understanding by repeatedly solving a particular problem, it proposes problems of the same theme but at different difficulty levels. This allows the user to improve their academic ability step by step. The curriculum proposal department continuously improves the quality of the learning materials based on user feedback. For example, if a user does not feel that a particular learning material is effective, alternative materials will be suggested. Furthermore, the learning material suggestion department can analyze the user's learning history and re-suggest materials that were effective in the past. This allows the learning material suggestion department to maximize support for the user's academic improvement and achieve efficient learning.

[0074] The Advice Department provides advice and support on learning methods based on the learning materials and problems proposed by the Learning Materials Proposal Department. Specifically, it analyzes the user's learning progress and provides necessary advice and support. It uses AI to analyze the user's learning progress and propose effective learning methods. For example, if a user is not feeling the effects of a particular learning method, it will suggest an alternative. The Advice Department provides advice tailored to the user's learning style and preferences. For example, for users who prefer visual learning, it will suggest learning methods that make extensive use of diagrams and graphs, and for users who prefer auditory learning, it will suggest learning methods with audio explanations. The Advice Department also provides advice to increase the user's motivation. For example, if a user has lost motivation to learn, it will suggest goal setting and reward systems to boost motivation. Furthermore, the Advice Department adjusts the content of its advice according to the user's learning progress. For example, if a user is struggling in a particular area, it will provide specific advice focused on that area. The Advice Department continuously improves the quality of its advice based on user feedback. For example, if a user is not feeling the effects of a particular piece of advice, it will suggest alternative advice. The Advice Department can also analyze the user's learning history and re-suggest advice that was effective in the past. This allows the advice section to provide maximum support for improving the user's academic performance and enable efficient learning.

[0075] The academic ability measurement unit can compare the current academic ability with the passing criteria of the desired school and work backward to calculate the necessary steps for improving academic ability. For example, the academic ability measurement unit can obtain the passing criteria of the desired school from a database and compare it with the current academic ability. For example, the academic ability measurement unit can use AI to compare the current academic ability with the passing criteria of the desired school and work backward to calculate the necessary steps for improving academic ability. For example, the academic ability measurement unit can measure the user's academic ability and analyze the gap with the passing criteria. This allows it to work backward to calculate the necessary steps for improving academic ability based on the passing criteria of the desired school. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can work backward to calculate the necessary steps for improving academic ability using an AI model that takes the passing criteria of the desired school and the current academic ability as input and outputs the necessary steps for improving academic ability.

[0076] The learning planning unit can create step-by-step learning plans on a monthly and yearly basis. For example, the learning planning unit can create step-by-step learning plans on a monthly and yearly basis based on the user's academic ability measurement results. For example, the learning planning unit can use AI to analyze the user's academic ability measurement results and create step-by-step learning plans. For example, the learning planning unit can create step-by-step learning plans on a monthly and yearly basis based on the user's academic ability improvement goals. This makes it possible to create step-by-step learning plans on a monthly and yearly basis. Some or all of the above-described processes in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can create a learning plan using an AI model that takes the user's academic ability measurement results as input and outputs a step-by-step learning plan.

[0077] The progress management unit can monitor the user's learning progress in real time and adjust the learning plan as needed. For example, the progress management unit can monitor the user's learning progress in real time and adjust the learning plan according to the progress status. For example, the progress management unit can analyze the user's learning progress using AI and adjust the learning plan as needed. For example, the progress management unit can periodically evaluate the user's learning progress and take necessary measures. This allows the user's learning progress to be monitored in real time and the learning plan to be adjusted as needed. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can adjust the learning plan using an AI model that takes user learning progress data as input and outputs adjustments to the learning plan.

[0078] The curriculum proposal unit can automatically select and provide curriculum materials and problems that match the user's academic ability. For example, the curriculum proposal unit can select and provide appropriate curriculum materials and problems based on the user's academic ability measurement results. For example, the curriculum proposal unit can use AI to analyze the user's academic ability measurement results and select appropriate curriculum materials and problems. For example, the curriculum proposal unit can propose curriculum materials that include many basic problems according to the user's academic ability. This enables the automatic selection and provision of curriculum materials and problems that match the user's academic ability. Some or all of the above processing in the curriculum proposal unit may be performed using AI, for example, or without AI. For example, the curriculum proposal unit can select curriculum materials and problems using an AI model that takes the user's academic ability measurement results as input and outputs appropriate curriculum materials and problems.

[0079] The advice unit can analyze the user's learning progress and provide necessary advice and support. For example, the advice unit can analyze the user's learning progress and suggest effective learning methods. For example, the advice unit can use AI to analyze the user's learning progress and provide necessary advice and support. For example, if a learning method is not effective, the advice unit can suggest an alternative learning method or provide advice to increase learning motivation. This allows the advice unit to analyze the user's learning progress and provide necessary advice and support. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice and support using an AI model that takes user learning progress data as input and outputs advice and support.

[0080] The academic ability measurement unit can estimate the user's emotions and adjust the timing of the academic ability measurement based on the estimated emotions. For example, if the user is feeling stressed, the academic ability measurement unit will adjust the timing of the academic ability measurement to coincide with a time when the user can relax. For example, if the user is concentrating, the academic ability measurement unit will conduct the academic ability measurement at that time to obtain accurate results. For example, if the user is tired, the academic ability measurement unit will adjust the schedule to conduct the academic ability measurement after the user has rested. By adjusting the timing of the academic ability measurement based on the user's emotions, more accurate academic ability measurement becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can take user emotion data as input and adjust the timing of the academic ability measurement using an AI model that adjusts the timing of the academic ability measurement.

[0081] The academic ability measurement unit can analyze the user's past academic ability measurement results and select the optimal measurement method. For example, the academic ability measurement unit may prioritize selecting measurement methods in which the user has previously achieved high scores. For example, the academic ability measurement unit may avoid measurement methods that the user struggles with and use methods in which the user excels. For example, the academic ability measurement unit may use AI to select the most effective measurement method based on the user's past measurement results. In this way, the optimal measurement method can be selected by analyzing the user's past academic ability measurement results. Some or all of the above processing in the academic ability measurement unit may be performed using AI, or not using AI. For example, the academic ability measurement unit can select the optimal measurement method using an AI model that takes the user's past academic ability measurement results as input and outputs the optimal measurement method.

[0082] The academic ability measurement unit can filter questions based on the user's current learning status and areas of interest during the academic ability measurement process. For example, the academic ability measurement unit can prioritize questions related to the subject the user is currently studying. For example, the academic ability measurement unit can present questions that are of interest to the user based on their areas of interest. For example, the academic ability measurement unit can present questions of appropriate difficulty according to the user's learning progress. This allows for more accurate academic ability measurement by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can perform filtering using an AI model that takes the user's current learning status and areas of interest as input and outputs filtered questions.

[0083] The academic ability measurement unit can estimate the user's emotions and determine the priority of the academic abilities to measure based on the estimated emotions. For example, if the user is feeling anxious, the academic ability measurement unit may start measuring subjects the user excels at to provide a sense of security. For example, if the user is confident, the academic ability measurement unit may start measuring subjects the user struggles with to increase their motivation to take on challenges. The academic ability measurement unit adjusts the order of subjects to be measured according to the user's emotions. This makes it possible to measure academic ability more effectively by determining the priority of the academic abilities to be measured based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can take user emotion data as input and determine the priority of academic abilities to be measured using an AI model that determines the priority of the academic abilities to be measured.

[0084] The academic ability measurement unit can prioritize highly relevant measurement items when measuring academic ability, taking into account the user's geographical location information. For example, the academic ability measurement unit can select measurement items based on the educational curriculum of the area where the user lives. For example, the academic ability measurement unit can prioritize questions that are frequently asked in the user's area. For example, the academic ability measurement unit can ask region-specific questions based on the user's geographical location information. By prioritizing highly relevant measurement items while considering the user's geographical location information, a more appropriate measurement of academic ability becomes possible. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can select measurement items using an AI model that takes the user's geographical location information as input and outputs highly relevant measurement items.

[0085] The academic ability measurement unit can analyze the user's social media activity during academic ability measurement and add relevant academic ability measurement items. For example, the academic ability measurement unit can ask questions related to topics the user has shown interest in on social media. For example, the academic ability measurement unit can identify areas of interest from the user's social media activity and add questions in those areas. For example, the academic ability measurement unit can select relevant academic ability measurement items based on the user's social media activity history. In this way, relevant academic ability measurement items can be added by analyzing the user's social media activity. Some or all of the above processing in the academic ability measurement unit may be performed using AI, for example, or without AI. For example, the academic ability measurement unit can add academic ability measurement items using an AI model that takes the user's social media activity data as input and outputs relevant academic ability measurement items.

[0086] The learning plan unit can estimate the user's emotions and adjust the presentation of the learning plan based on the estimated emotions. For example, if the user is stressed, the learning plan unit provides a simple and easy-to-understand learning plan. For example, if the user is relaxed, the learning plan unit provides a detailed learning plan and closely manages the learning progress. For example, if the user is motivated, the learning plan unit provides a challenging learning plan. In this way, by adjusting the presentation of the learning plan based on the user's emotions, a more effective learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning plan unit may be performed using AI, for example, or without AI. For example, the learning plan unit can take user emotion data as input and adjust the presentation of the learning plan using an AI model that adjusts the presentation of the learning plan.

[0087] The learning planning unit can adjust the level of detail in a learning plan based on the importance of the subject matter. For example, the learning planning unit can create a detailed learning plan for important subjects, and a simplified learning plan for less important subjects. The learning planning unit adjusts the level of detail in the learning plan according to the importance of the subject matter. This allows for the provision of more effective learning plans by adjusting the level of detail based on the importance of the subject matter. Some or all of the above processes in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can use an AI model that takes the importance of subject matter data as input to adjust the level of detail in the learning plan.

[0088] The learning planning unit can apply different planning algorithms depending on the academic ability category when formulating a learning plan. For example, the learning planning unit might apply an algorithm that includes step-by-step development to a mathematics learning plan. For example, it might apply an algorithm that emphasizes repetitive learning to an English learning plan. For example, it might apply an algorithm that emphasizes experiments and observations to a science learning plan. By applying different planning algorithms depending on the academic ability category, it is possible to provide a more effective learning plan. Some or all of the above processing in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can create a learning plan using an AI model that takes academic ability category data as input and applies different planning algorithms.

[0089] The learning plan unit can estimate the user's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if the user is tired, the learning plan unit provides a short-term learning plan. For example, if the user is relaxed, the learning plan unit provides a long-term learning plan. For example, the learning plan unit adjusts the duration of the learning plan according to the user's emotions. This allows for the provision of a more effective learning plan by adjusting the length of the learning plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning plan unit may be performed using AI, for example, or without AI. For example, the learning plan unit can take user emotion data as input and adjust the length of the learning plan using an AI model that adjusts the length of the learning plan.

[0090] The learning planning unit can determine the priority of learning plans based on the timing of academic ability measurements when formulating learning plans. For example, if an important exam is approaching, the learning planning unit will prioritize the learning plan for that subject. For example, the learning planning unit will determine which subjects should be prioritized for learning based on the results of academic ability measurements. For example, the learning planning unit will adjust the priority of learning plans according to the timing of academic ability measurements. By determining the priority of plans based on the timing of academic ability measurements, a more effective learning plan can be provided. Some or all of the above processes in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can determine the priority of learning plans using an AI model that takes academic ability measurement timing data as input and determines the priority of plans.

[0091] The learning planning unit can adjust the order of learning plans based on the relationships between academic abilities when formulating a learning plan. For example, the learning planning unit can adjust the order of learning plans by considering the relationship between mathematics and physics. For example, the learning planning unit can adjust the order of learning plans by considering the relationship between English and social studies. For example, the learning planning unit can optimize the order of learning plans based on the relationships between academic abilities. This makes it possible to provide a more effective learning plan by adjusting the order of plans based on the relationships between academic abilities. Some or all of the above processing in the learning planning unit may be performed using AI, for example, or without AI. For example, the learning planning unit can adjust the order of learning plans using an AI model that takes academic ability relationship data as input and adjusts the order of plans.

[0092] The progress management unit can estimate the user's emotions and adjust the progress management criteria based on the estimated user emotions. For example, if the user is stressed, the progress management unit will relax the progress management criteria. For example, if the user is relaxed, the progress management unit will tighten the progress management criteria. The progress management unit adjusts the progress management criteria according to the user's emotions. This allows for more effective progress management by adjusting the progress management criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can adjust the progress management criteria using an AI model that takes user emotion data as input.

[0093] The progress management unit can improve the accuracy of its management by considering the interrelationships of academic abilities during progress management. For example, the progress management unit can manage the progress of mathematics and physics in a linked manner. For example, the progress management unit can manage the progress of English and social studies in a linked manner. For example, the progress management unit can improve the accuracy of its progress management by considering the interrelationships of academic abilities. This makes more effective progress management possible by improving the accuracy of management by considering the interrelationships of academic abilities. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can perform progress management using an AI model that improves the accuracy of management, with data on the interrelationships of academic abilities as input.

[0094] The progress management unit can manage progress while taking user attribute information into consideration. For example, the progress management unit can adjust the progress management criteria according to the user's age. For example, the progress management unit can adjust the progress management method according to the user's learning style. For example, the progress management unit can perform optimal progress management based on the user's attribute information. This makes more effective progress management possible by taking user attribute information into consideration. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without using AI. For example, the progress management unit can perform progress management using an AI model that takes user attribute information as input and performs progress management.

[0095] The progress management unit can estimate the user's emotions and adjust the order in which progress management results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the progress management unit will display the results in order from best to worst. For example, if the user is feeling confident, the progress management unit will display the results in order from worst to best. The progress management unit adjusts the order in which progress management results are displayed according to the user's emotions. This allows for more effective progress management by adjusting the order in which progress management results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can adjust the order in which progress management results are displayed using an AI model that takes user emotion data as input and adjusts the order in which progress management results are displayed.

[0096] The progress management unit can manage progress while considering the geographical distribution of academic ability. For example, the progress management unit can manage progress based on the educational curriculum of the user's region. For example, the progress management unit can manage progress while considering the questions that frequently appear in the user's region. For example, the progress management unit can perform optimal progress management based on the geographical distribution of academic ability. This makes more effective progress management possible by considering the geographical distribution of academic ability. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can perform progress management using an AI model that takes geographical distribution data of academic ability as input.

[0097] The progress management unit can improve the accuracy of its progress management by referring to relevant literature on academic ability. For example, the progress management unit sets progress management standards based on relevant literature on academic ability. For example, the progress management unit improves its progress management methods by referring to relevant literature on academic ability. For example, the progress management unit improves the accuracy of its progress management based on relevant literature on academic ability. As a result, by improving the accuracy of management by referring to relevant literature on academic ability, more effective progress management becomes possible. Some or all of the above processes in the progress management unit may be performed using AI, for example, or without AI. For example, the progress management unit can perform progress management using an AI model that improves the accuracy of management, with relevant literature on academic ability as input.

[0098] The material suggestion unit can estimate the user's emotions and determine the priority of suggested materials based on the estimated emotions. For example, if the user is feeling stressed, the material suggestion unit will suggest easier materials first. For example, if the user is relaxed, the material suggestion unit will suggest more difficult materials. The material suggestion unit adjusts the priority of suggested materials according to the user's emotions. This makes it possible to suggest more effective materials by determining the priority of suggested materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the material suggestion unit may be performed using AI or not using AI. For example, the material suggestion unit can take user emotion data as input and determine the priority of suggested materials using an AI model that determines the priority of suggested materials.

[0099] The curriculum proposal department can improve the accuracy of its proposals by considering the interrelationships of academic abilities. For example, the curriculum proposal department can propose materials that are useful for both subjects by considering the relationship between mathematics and physics. For example, the curriculum proposal department can propose materials that are useful for both subjects by considering the relationship between English and social studies. For example, the curriculum proposal department can propose the most suitable materials based on the interrelationships of academic abilities. By improving the accuracy of proposals by considering the interrelationships of academic abilities, more effective curriculum proposals become possible. Some or all of the above processing in the curriculum proposal department may be performed using AI, for example, or without AI. For example, the curriculum proposal department can take data on the interrelationships of academic abilities as input and make curriculum proposals using an AI model that improves the accuracy of proposals.

[0100] The curriculum proposal unit can make recommendations while considering the user's attribute information. For example, the curriculum proposal unit can propose appropriate curriculum materials according to the user's age. For example, the curriculum proposal unit can propose optimal curriculum materials according to the user's learning style. For example, the curriculum proposal unit can propose optimal curriculum materials based on the user's attribute information. By considering the user's attribute information when making recommendations, more effective curriculum proposals become possible. Some or all of the above processing in the curriculum proposal unit may be performed using AI, for example, or without AI. For example, the curriculum proposal unit can make curriculum proposals using an AI model that takes user attribute information as input and makes curriculum proposals.

[0101] The material suggestion unit can estimate the user's emotions and adjust the display method of suggested materials based on the estimated user emotions. For example, if the user is nervous, the material suggestion unit provides a simple and highly visible display method. For example, if the user is relaxed, the material suggestion unit provides a display method that includes detailed information. For example, if the user is in a hurry, the material suggestion unit provides a display method that gets straight to the point. By adjusting the display method of suggested materials based on the user's emotions, more effective material suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the material suggestion unit may be performed using AI, for example, or without AI. For example, the material suggestion unit can take user emotion data as input and adjust the display method of suggested materials using an AI model.

[0102] The curriculum proposal unit can make curriculum proposals while considering the geographical distribution of academic ability. For example, the curriculum proposal unit can propose curriculum based on the educational curriculum of the area where the user lives. For example, the curriculum proposal unit can propose curriculum considering the types of questions that frequently appear in the user's area. For example, the curriculum proposal unit can propose the most suitable curriculum based on the geographical distribution of academic ability. By considering the geographical distribution of academic ability when making proposals, it becomes possible to propose more effective curriculum. Some or all of the above processing in the curriculum proposal unit may be performed using AI, for example, or without AI. For example, the curriculum proposal unit can make curriculum proposals using an AI model that takes geographical distribution data of academic ability as input.

[0103] The curriculum proposal department can improve the accuracy of its proposals by referring to relevant literature on academic ability when proposing curriculum materials. For example, the curriculum proposal department proposes the most suitable curriculum materials based on relevant literature on academic ability. For example, the curriculum proposal department sets selection criteria for curriculum materials by referring to relevant literature on academic ability. For example, the curriculum proposal department improves the accuracy of its proposals based on relevant literature on academic ability. By improving the accuracy of proposals by referring to relevant literature on academic ability, more effective curriculum proposals become possible. Some or all of the above processes in the curriculum proposal department may be performed using AI, for example, or without AI. For example, the curriculum proposal department can take data on relevant literature on academic ability as input and use an AI model to improve the accuracy of proposals to make curriculum proposals.

[0104] The advice unit can estimate the user's emotions and adjust its advice based on those emotions. For example, if the user is stressed, the advice unit can provide relaxing advice. If the user is relaxed, the advice unit can provide detailed advice. If the user is motivated, the advice unit can provide challenging advice. By adjusting the advice based on the user's emotions, more effective advice becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can take user emotion data as input and adjust its advice using an AI model that adjusts the advice method.

[0105] The advice unit can analyze the user's past learning behavior and select the optimal advice method when providing advice. For example, the advice unit provides advice based on learning methods that have been effective for the user in the past. For example, the advice unit selects the optimal advice method from the user's past learning behavior. For example, the advice unit analyzes the user's past learning behavior and provides effective advice. In this way, the optimal advice method can be selected by analyzing the user's past learning behavior. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can provide advice using an AI model that takes the user's past learning behavior data as input and selects the optimal advice method.

[0106] The advice unit can customize the means of advice based on the user's current learning status when providing advice. For example, the advice unit can provide advice related to the subject the user is currently studying. For example, the advice unit can provide appropriate advice according to the user's learning progress. For example, the advice unit can provide optimal advice based on the user's current learning status. This makes it possible to provide more effective advice by customizing the means of advice based on the user's current learning status. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the user's current learning status data as input and customizes the means of advice.

[0107] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling anxious, the advice unit will prioritize reassuring advice. For example, if the user is confident, the advice unit will prioritize challenging advice. The advice unit adjusts the priority of advice according to the user's emotions. This allows for more effective advice by determining the priority of advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can take user emotion data as input and determine the priority of advice using an AI model that determines the priority of advice.

[0108] The advice unit can select the optimal advice method by considering the user's geographical location information when providing advice. For example, the advice unit can provide advice based on the educational curriculum of the area where the user lives. For example, the advice unit can provide advice by considering the types of questions that frequently appear in the user's area. For example, the advice unit can provide optimal advice based on the user's geographical location information. By selecting the optimal advice method by considering the user's geographical location information, more effective advice becomes possible. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can provide advice using an AI model that takes the user's geographical location information as input and selects the optimal advice method.

[0109] The advice unit can analyze the user's social media activity and propose methods for providing advice. For example, the advice unit can provide advice related to topics the user has shown interest in on social media. For example, the advice unit can provide advice related to areas of interest based on the user's social media activity. For example, the advice unit can provide optimal advice based on the user's social media activity history. In this way, by analyzing the user's social media activity, it is possible to propose the optimal method for providing advice. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can take the user's social media activity data as input and provide advice using an AI model that proposes methods for providing advice.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The academic improvement support system can customize learning plans according to the user's learning style. For example, users who prefer visual learning will be offered learning materials that make extensive use of diagrams and graphs. Users who prefer auditory learning will be offered audio materials and podcasts. Furthermore, users who prefer practical learning will be provided with experiment- and project-based learning plans. This allows the system to provide the optimal learning plan tailored to each user's learning style.

[0112] The academic improvement support system can analyze a user's learning history and optimize their learning plan based on past learning patterns. For example, it prioritizes learning methods that were effective in the past and eliminates those that were less effective. It also provides special supplementary materials for areas where the user has struggled in the past. This allows the system to provide a more effective learning plan based on the user's past learning history.

[0113] The academic improvement support system can dynamically adjust the difficulty level of the learning plan according to the user's learning progress. For example, if the user is progressing according to the plan, the difficulty level of the next step will be increased. Conversely, if progress is slow, the difficulty level will be lowered and the user will be prompted to relearn from the basics. Also, if progress is fast in a particular area, learning in that area will be temporarily suspended and adjusted to focus on other areas. In this way, the system can provide an optimal learning plan tailored to the user's learning progress.

[0114] The academic improvement support system can adjust learning plans based on the user's learning environment. For example, it recommends studying in a library or study room for users who prefer a quiet environment. Conversely, it suggests appropriate music playlists for users who find studying while listening to music effective. It also provides online learning materials and webinars for users who find online learning effective. This allows the system to provide an optimal learning plan tailored to the user's learning environment.

[0115] The academic improvement support system can customize learning plans based on the user's learning goals. For example, users with short-term goals will be provided with a short-term learning plan for focused study. Conversely, users with long-term goals will be provided with a long-term learning plan for gradual progress. Furthermore, users aiming for specific exams or qualifications will be provided with a learning plan tailored to those exams. This allows the system to provide the optimal learning plan according to the user's learning goals.

[0116] The academic improvement support system can estimate the user's emotions and adjust the progress of the learning plan based on those emotions. For example, if the user is feeling stressed, the system will slow down the pace of the learning plan and increase the time for relaxation. Conversely, if the user is motivated, the system will accelerate the pace of the learning plan and add more challenging tasks. Also, if the user is tired, the system will incorporate rest time into the plan. In this way, by adjusting the progress of the learning plan based on the user's emotions, the system can support more effective learning.

[0117] The academic improvement support system can estimate the user's emotions and suggest learning methods based on those emotions. For example, if the user is feeling anxious, it will suggest a relaxing learning method. Conversely, if the user is confident, it will suggest a challenging learning method. Also, if the user is focused, it will suggest a more difficult task at that time. In this way, by suggesting the optimal learning method based on the user's emotions, the learning effect can be maximized.

[0118] The academic performance improvement support system can estimate the user's emotions and provide feedback to enhance learning motivation based on those estimated emotions. For example, if the user is feeling down, it can send an encouraging message. Conversely, if the user is feeling successful, it can send a message encouraging further challenges. Also, if the user is tired, it can send a message recommending that they take a rest. In this way, by providing appropriate feedback based on the user's emotions, it can maintain their motivation to learn.

[0119] The academic improvement support system can estimate the user's emotions and adjust how it reports learning progress based on those emotions. For example, if the user is feeling anxious, it prioritizes positive progress reports. Conversely, if the user is confident, it provides detailed progress reports including areas for improvement. If the user is relaxed, it provides a comprehensive report on overall progress. This allows for improved learning effectiveness by providing optimal progress reports based on the user's emotions.

[0120] The academic improvement support system can estimate the user's emotions and adjust the timing of learning based on those emotions. For example, if the user is feeling stressed, it can adjust the learning schedule to coincide with a time when the user can relax. Conversely, if the user is focused, it can adjust the learning schedule to coincide with that time. Also, if the user is tired, it can adjust the schedule to allow them to rest before learning. In this way, by adjusting the timing of learning based on the user's emotions, it can support more effective learning.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The academic ability measurement unit measures the user's academic ability. For example, it compares the current academic ability with the passing criteria for the desired school entered by the user and works backward to calculate the necessary steps for academic improvement. It uses AI to measure the user's academic ability and analyzes the gap with the passing criteria. It also measures the user's academic ability regularly to track their progress. Step 2: The Learning Planning Department creates a learning plan based on the academic ability measured by the Academic Ability Measurement Department. For example, it creates a step-by-step learning plan on a monthly and yearly basis. Using AI, it monitors the user's learning progress in real time and adjusts the learning plan as needed. If the user is not progressing according to plan, it suggests additional study time or advises changing the learning method. Step 3: The progress management department manages learning progress based on the learning plan created by the learning planning department. For example, it monitors users' learning progress in real time and adjusts the learning plan as needed. It uses AI to analyze users' learning progress and adjusts the learning plan according to the progress. It regularly evaluates users' learning progress and takes necessary measures. Step 4: The curriculum proposal department proposes appropriate curriculum materials and problems based on the progress managed by the progress management department. For example, it automatically selects and provides curriculum materials and problems according to the user's academic level. It uses AI to select and provide curriculum materials and problems according to the user's academic level. It proposes curriculum materials that include many basic problems according to the user's academic level. Step 5: The Advice Department provides advice and support on learning methods based on the learning materials and problems proposed by the Learning Materials Proposal Department. For example, it analyzes the user's learning progress and provides necessary advice and support. It uses AI to analyze the user's learning progress and proposes effective learning methods. If the learning method is not effective, it proposes alternative learning methods or provides advice to increase learning motivation.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the academic ability measurement unit, learning plan unit, progress management unit, teaching material suggestion unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the academic ability measurement unit measures the user's academic ability using the camera 42 and microphone 38B of the smart device 14 and analyzes the gap with the passing criteria using the control unit 46A. The learning plan unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates a learning plan based on the user's academic ability. The progress management unit is implemented in the specific processing unit 46A of the smart device 14 and monitors the user's learning progress in real time and adjusts the learning plan as needed. The teaching material suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and selects and provides teaching materials and problems according to the user's academic ability. The advice unit is implemented in the specific processing unit 46A of the smart device 14 and analyzes the user's learning progress and provides necessary advice and assistance. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the academic ability measurement unit, learning plan unit, progress management unit, teaching material suggestion unit, and advice unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the academic ability measurement unit measures the user's academic ability using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the gap with the passing criteria using the control unit 46A. The learning plan unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a learning plan based on the user's academic ability. The progress management unit is implemented by, for example, the control unit 46A of the smart glasses 214 and monitors the user's learning progress in real time and adjusts the learning plan as needed. The teaching material suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects and provides teaching materials and problems according to the user's academic ability. The advice unit is implemented by, for example, the control unit 46A of the smart glasses 214 and analyzes the user's learning progress and provides necessary advice and assistance. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the academic ability measurement unit, learning plan unit, progress management unit, teaching material suggestion unit, and advice unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the academic ability measurement unit measures the user's academic ability using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the gap with the passing criteria using the control unit 46A. The learning plan unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a learning plan based on the user's academic ability. The progress management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and monitors the user's learning progress in real time and adjusts the learning plan as needed. The teaching material suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects and provides teaching materials and problems according to the user's academic ability. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314 and analyzes the user's learning progress and provides necessary advice and assistance. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the academic ability measurement unit, learning plan unit, progress management unit, teaching material suggestion unit, and advice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the academic ability measurement unit measures the user's academic ability using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the gap with the passing criteria. The learning plan unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and creates a learning plan based on the user's academic ability. The progress management unit is implemented by, for example, the control unit 46A of the robot 414, and monitors the user's learning progress in real time and adjusts the learning plan as needed. The teaching material suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and selects and provides teaching materials and problems according to the user's academic ability. The advice unit is implemented by, for example, the control unit 46A of the robot 414, and analyzes the user's learning progress and provides necessary advice and assistance. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The academic ability measurement unit measures the user's academic ability, A learning planning department that creates a learning plan based on the academic ability measured by the aforementioned academic ability measurement department, A progress management unit manages learning progress based on the learning plan established by the aforementioned learning planning unit, A teaching materials proposal department proposes appropriate teaching materials and problems based on the progress managed by the aforementioned progress management department, The system includes an advice unit that provides advice and support on learning methods based on the teaching materials and problems proposed by the aforementioned teaching material proposal unit. A system characterized by the following features. (Note 2) The aforementioned academic ability measurement unit, Compare your current academic ability with the admission criteria of your desired school, and work backward to determine the necessary steps for improving your academic ability. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning planning unit, Create a step-by-step learning plan, month by month and year by year. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned progress management unit, Monitor the user's learning progress in real time and adjust the learning plan as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned teaching material proposal department, The system automatically selects and provides learning materials and problems tailored to the user's academic level. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Analyze the user's learning progress and provide necessary advice and support. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned academic ability measurement unit, The system estimates the user's emotions and adjusts the timing of academic performance measurements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned academic ability measurement unit, Analyze the user's past academic performance test results and select the optimal measurement method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned academic ability measurement unit, When measuring academic ability, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned academic ability measurement unit, It estimates the user's emotions and determines the priority of the academic ability to measure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned academic ability measurement unit, When measuring academic ability, the system prioritizes highly relevant measurement items, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned academic ability measurement unit, When measuring academic ability, analyze users' social media activity and add relevant academic ability measurement items. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning planning unit, It estimates the user's emotions and adjusts how the learning plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning planning unit, When creating a study plan, adjust the level of detail in the plan based on the importance of each academic skill. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning planning unit, When creating a study plan, apply different planning algorithms depending on the academic ability category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning planning unit, It estimates the user's emotions and adjusts the length of the learning plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning planning unit, When creating a study plan, prioritize the plan based on when academic ability will be measured. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning planning unit, When creating a study plan, adjust the order of the plan based on the relationships between academic abilities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress management unit, Estimate user sentiment and adjust progress management criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress management unit, When managing progress, improve the accuracy of management by considering the interrelationships between academic abilities. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress management unit, When managing progress, take user attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress management unit, It estimates the user's emotions and adjusts the order in which progress management results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress management unit, When managing progress, the geographical distribution of academic ability should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress management unit, When managing student progress, refer to relevant literature on academic ability to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned teaching material proposal department, It estimates the user's emotions and determines the priority of suggested learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned teaching material proposal department, When proposing teaching materials, we will improve the accuracy of the proposals by considering the interrelationships between academic abilities. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned teaching material proposal department, When proposing teaching materials, we take user attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned teaching material proposal department, It estimates the user's emotions and adjusts how suggested learning materials are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned teaching material proposal department, When proposing teaching materials, consider the geographical distribution of academic ability. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned teaching material proposal department, When proposing teaching materials, we will improve the accuracy of the proposals by referring to relevant literature on academic ability. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, It estimates the user's emotions and adjusts the advice given based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advice section, When providing advice, the system analyzes the user's past learning behavior to select the most appropriate advice method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, When providing advice, customize the advice method based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, When providing advice, the optimal advice method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advice section, When providing advice, we analyze the user's social media activity and suggest methods for providing advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The academic ability measurement unit measures the user's academic ability, A learning planning department that creates a learning plan based on the academic ability measured by the aforementioned academic ability measurement department, A progress management unit manages learning progress based on the learning plan established by the aforementioned learning planning unit, A teaching materials proposal department proposes appropriate teaching materials and problems based on the progress managed by the aforementioned progress management department, The system includes an advice unit that provides advice and support on learning methods based on the teaching materials and problems proposed by the aforementioned teaching material proposal unit. A system characterized by the following features.

2. The aforementioned academic ability measurement unit, Compare your current academic ability with the admission criteria of your desired school, and work backward to determine the necessary steps for improving your academic ability. The system according to feature 1.

3. The aforementioned learning planning unit, Create a step-by-step learning plan, month by month and year by year. The system according to feature 1.

4. The aforementioned progress management unit, Monitor the user's learning progress in real time and adjust the learning plan as needed. The system according to feature 1.

5. The aforementioned teaching material proposal department, The system automatically selects and provides learning materials and problems tailored to the user's academic level. The system according to feature 1.

6. The aforementioned advice section, Analyze the user's learning progress and provide necessary advice and support. The system according to feature 1.

7. The aforementioned academic ability measurement unit, The system estimates the user's emotions and adjusts the timing of academic performance measurements based on those estimated emotions. The system according to feature 1.

8. The aforementioned academic ability measurement unit, Analyze the user's past academic performance test results and select the optimal measurement method. The system according to feature 1.

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