System

Abstract

An object of a system according to an embodiment is to diagnose whether an examinee is good or bad and to provide an appropriate question based on the diagnosis.SOLUTION: A system includes a problem solving part, a diagnosis part, a problem generation part, and a presentation part. The question answering part records questions answered by examinees. The diagnosis unit analyzes the answers recorded by the question answering unit, and diagnoses the tendency of strong points and weak points. The problem generation unit generates a new problem based on a result of the diagnosis by the diagnosis unit. The presentation part presents the question generated by the question generation part to the examinee.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not been able to efficiently diagnose the strengths and weaknesses of test-takers and provide appropriate questions based on that information, so there is room for improvement.

[0005] The system according to the embodiment aims to diagnose the strengths and weaknesses of test-takers and provide appropriate questions based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes a question answering unit, a diagnosis unit, a question generation unit, and a presentation unit. The question answering unit records the questions answered by the examinee. The diagnosis unit analyzes the answers recorded by the question answering unit and diagnoses the examinee's strengths and weaknesses. The question generation unit generates new questions based on the results of the diagnosis by the diagnosis unit. The presentation unit presents the questions generated by the question generation unit to the examinee. [Effects of the Invention]

[0007] The system according to the embodiment can diagnose the strengths and weaknesses of test-takers and provide appropriate questions based on the diagnosis. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The learning support system according to an embodiment of the present invention analyzes the answers and answering methods given by test takers, and uses a generation AI to diagnose their strengths and weaknesses and provide optimal questions for each individual test taker. This allows the learning support system to efficiently support test takers' learning and improve their grades.

[0029] A learning support system according to an embodiment includes a question answering unit, a diagnosis unit, a question generation unit, and a presentation unit. The question answering unit records questions answered by test takers. For example, it records the content of the questions answered by the test takers and the answering methods as digital data. The question answering unit can also record the accuracy of the answers and the answering time. For example, the question answering unit stores the correct answer rate and answering time of the questions answered by the test takers in a database. The diagnosis unit analyzes the answers recorded by the question answering unit and diagnoses strengths and weaknesses. For example, the generation AI analyzes the test takers' answer data and identifies their strengths and weaknesses. The diagnosis unit can also evaluate the consistency and confidence of the answers. For example, the generation AI quantifies the consistency and confidence of the test takers' answers and reflects them in the diagnosis results. The question generation unit generates new questions based on the results of the diagnosis by the diagnosis unit. For example, the generation AI generates difficult questions for areas in which the test-taker is strong and basic questions for areas in which the test-taker is weak. The question generation unit can also provide question formats that match the test-taker's learning style and preferences. For example, the generation AI generates questions that make extensive use of diagrams and graphs for test-taker who prefer visual learning. The presentation unit presents the questions generated by the question generation unit to the test-taker. For example, the presentation unit displays the generated questions on a screen so that the test-taker can answer them. The presentation unit can also print the generated questions and provide them on paper. For example, the presentation unit prints the generated questions on a printer and distributes them to the test-taker. In this way, the learning support system according to the embodiment can support efficient learning by diagnosing the test-taker's strengths and weaknesses and providing individually optimal questions.

[0030] When analyzing answer data, the diagnostic unit can make more accurate diagnoses based on the test-taker's learning history and past performance data. For example, when the generation AI analyzes answer data, the diagnostic unit takes into account the test-taker's past learning history. For example, it diagnoses strengths and weaknesses based on the accuracy rate and answer time of questions previously answered. The diagnostic unit can also take into account past performance data. For example, the generation AI diagnoses strengths and weaknesses based on past test results and evaluation scores. In this way, by taking into account learning history and past performance data, more accurate diagnoses are possible.

[0031] The diagnostic unit evaluates the test-taker's confidence in their answers and can diagnose their strengths and weaknesses in more detail. For example, when the generation AI analyzes answer data, the diagnostic unit evaluates the test-taker's confidence in their answers. For example, it quantifies the confidence of the answers and diagnoses their strengths and weaknesses. The diagnostic unit can also evaluate the consistency and confidence of the answers. For example, the generation AI quantifies the consistency and confidence of the test-taker's answers and reflects this in the diagnosis results. In this way, by evaluating the confidence, it is possible to diagnose their strengths and weaknesses in more detail.

[0032] The question generation unit can provide a question format that matches the test-taker's learning style and preferences based on the diagnostic results. For example, the generation AI can create questions that make extensive use of diagrams and graphs for test-taker who prefer visual learning. The question generation unit can also provide a question format that matches the test-taker's preferences. For example, the generation AI can create questions that match the test-taker's preferences based on the test-taker's past learning history and questionnaire survey results. This maximizes the learning effect by providing a question format that matches the test-taker's learning style and preferences.

[0033] The question generation unit can analyze trends in past exam questions and create predicted questions based on the diagnostic results. For example, the generation AI in the question generation unit analyzes trends in past exam questions and creates predicted questions based on the diagnostic results. For example, it creates questions that are likely to appear in the next exam based on the frequency and format of questions in the past. The question generation unit can also analyze trends in past exam questions and develop algorithms for creating predicted questions. For example, the generation AI can learn data from past exam questions and generate predicted questions. This allows the system to analyze trends in past exam questions and create predicted questions, effectively supporting test takers in their exam preparation.

[0034] The question generation unit can randomly combine questions of different difficulty levels based on the diagnostic results, thereby improving the adaptability of test takers. For example, the question generation unit provides questions of different difficulty levels by randomly combining them based on the diagnostic results using a generation AI. For example, easy questions and difficult questions may be presented alternately to improve the adaptability of test takers. The question generation unit can also develop an algorithm for randomly combining questions of different difficulty levels. For example, the generation AI evaluates the difficulty of the questions and generates questions by randomly combining them. In this way, the adaptability of test takers can be improved by randomly combining questions of different difficulty levels.

[0035] The question generation unit can introduce a collaborative learning mode with other test takers based on the diagnostic results, providing a format for solving problems together. The question generation unit, for example, the generation AI introduces a collaborative learning mode with other test takers based on the diagnostic results. For example, it provides a format for solving problems in groups, allowing students to work together to advance their studies. The question generation unit can also develop an algorithm for introducing the collaborative learning mode. For example, the generation AI analyzes the learning data of test takers, forms optimal groups, and promotes collaborative learning. This allows test takers to work together to advance their studies by introducing a collaborative learning mode with other test takers.

[0036] The presentation unit can analyze the answering process in real time when a test-taker solves a problem and provide immediate feedback. For example, the presentation unit can analyze the answering process in real time when a test-taker solves a problem and provide immediate feedback. For example, it can detect errors in the middle of the answer and present the correct solution. The presentation unit can also develop algorithms for analyzing the answering process in real time. For example, the generation AI can monitor the progress of the answer and provide feedback at the appropriate time. This makes it possible to analyze the answering process in real time and provide immediate feedback, thereby improving learning effectiveness.

[0037] The presentation unit can measure the time it takes for test takers to solve problems and provide time management advice. For example, the presentation unit can measure the time it takes for test takers to solve problems and provide time management advice. For example, it can present the amount of time that should be spent on each problem and suggest efficient time allocation. The presentation unit can also develop an algorithm for measuring the answer time. For example, the generation AI can monitor the answer time in real time and provide time management advice. This makes it possible to support efficient learning by measuring the answer time and providing time management advice.

[0038] The presentation unit can provide a realistic learning environment using virtual reality (VR) when test takers solve problems. For example, the presentation unit can provide a realistic learning environment using a VR headset when test takers solve problems. For example, the atmosphere of an exam hall can be reproduced, allowing test takers to solve problems in an environment similar to that of an actual exam. The presentation unit can also develop algorithms for providing a learning environment using virtual reality. For example, a generative AI can provide a virtual classroom or interactive simulation, allowing test takers to learn in a realistic environment. This can improve learning effectiveness by providing a realistic learning environment using virtual reality.

[0039] The presentation unit can improve motivation by introducing a real-time competition mode with other test takers. For example, the presentation unit can introduce a mode in which test takers compete with other test takers in real time when solving a problem. For example, test takers can solve the same problem at the same time and compete for answer speed and accuracy. The presentation unit can also develop algorithms for introducing the real-time competition mode. For example, the generation AI can analyze test taker answer data in real time and provide the competition mode. This can improve motivation by introducing a real-time competition mode with other test takers.

[0040] The diagnostic unit can visualize the learning progress in real time and provide feedback to the test-taker. For example, the diagnostic unit visualizes the learning progress in real time when the generative AI repeats diagnosis and question presentation. For example, it visually displays the test-taker's learning progress using graphs and charts. The diagnostic unit can also develop algorithms for visualizing the learning progress. For example, the generative AI analyzes learning data and displays the progress in real time. This makes it possible to visualize the learning progress in real time and provide feedback, thereby improving the learning effect.

[0041] The diagnostic unit can analyze the test-taker's learning patterns and propose an optimal learning schedule. For example, the diagnostic unit analyzes the test-taker's learning patterns when the generation AI repeats diagnosis and question presentation. For example, it analyzes the time and frequency of study and proposes an optimal learning schedule. The diagnostic unit can also develop algorithms for analyzing learning patterns. For example, the generation AI analyzes learning data and proposes an optimal learning schedule. This makes it possible to support efficient learning by analyzing learning patterns and proposing an optimal learning schedule.

[0042] The diagnostic unit can combine learning across different subjects to improve overall academic ability. For example, when the generative AI repeats diagnosis and problem presentation, the diagnostic unit combines learning across different subjects. For example, it can alternate between mathematics and physics questions to improve overall academic ability. The diagnostic unit can also develop algorithms for combining learning across different subjects. For example, the generative AI can analyze learning data and suggest the optimal combination of subjects. This makes it possible to combine learning across different subjects to improve overall academic ability.

[0043] The diagnostic unit can introduce a collaborative learning mode with other test-takers, providing a format for cooperative learning. For example, when the generative AI repeats diagnosis and problem presentation, the diagnostic unit introduces a collaborative learning mode with other test-takers. For example, it provides a format for solving problems in groups, allowing for cooperative learning. The diagnostic unit can also develop algorithms for introducing the collaborative learning mode. For example, the generative AI analyzes the test-takers' learning data, forms optimal groups, and promotes collaborative learning. This allows for cooperative learning by introducing a collaborative learning mode with other test-takers.

[0044] The problem generator can provide specialized or advanced problems to further develop areas of strength. For example, the problem generator provides specialized problems to further develop areas in which the generation AI is strong. For example, a test-taker who is good at mathematics can be given advanced questions on calculus or linear algebra. The problem generator can also develop algorithms to provide advanced questions to further develop areas of strength. For example, the generation AI can identify an area in which a test-taker is strong and generate advanced questions specialized for that area. This can improve learning effectiveness by providing specialized or advanced questions to further develop areas of strength.

[0045] The problem generation unit can provide problems that allow students to learn step by step, from basic to applied, in order to overcome their weaknesses. For example, the problem generation unit provides problems that allow students to learn step by step, from basic to applied, in order to overcome their weaknesses. For example, for a test-taker who is not good at mathematical probability and statistics, questions can be presented step by step, from basic probability problems to applied statistics problems. The problem generation unit can also develop an algorithm for providing problems that allow students to learn step by step. For example, the generation AI can identify a test-taker's weak areas and generate step-by-step problems specialized for those areas. This can improve learning effectiveness by providing problems that allow students to learn step by step, from basic to applied, in order to overcome their weaknesses.

[0046] The question generation unit can provide a combination of questions from different fields to further develop areas of strength. For example, the question generation unit provides a combination of questions from different fields to further develop areas in which the generation AI is strong. For example, a test-taker who is good at mathematics can be given questions related to mathematics and physics. The question generation unit can also develop an algorithm for combining questions from different fields. For example, the generation AI can identify an area in which a test-taker is strong and generate questions from different fields related to that area. This can improve learning effectiveness by providing a combination of questions from different fields to further develop areas of strength.

[0047] The question generation unit can introduce a collaborative learning mode with other test-takers to help them overcome their weaknesses, providing a format for them to study together. The question generation unit, for example, introduces a collaborative learning mode with other test-takers to help the generation AI overcome their weaknesses. For example, it provides a format for solving problems in groups, and they can study together. The question generation unit can also develop an algorithm for introducing the collaborative learning mode. For example, the generation AI analyzes the learning data of test-takers, forms optimal groups, and promotes collaborative learning. By introducing a collaborative learning mode with other test-takers, the learning effect for overcoming weaknesses can be improved.

[0048] The diagnostic unit can analyze the test taker's learning data, predict the possibility of passing, and propose an optimal study strategy. In the diagnostic unit, for example, the generative AI analyzes the test taker's learning data and predicts the possibility of passing. For example, it calculates the probability of passing based on past learning data and mock test scores. The diagnostic unit can also develop algorithms to predict the possibility of passing and propose an optimal study strategy. For example, the generative AI analyzes the test taker's learning data and suggests areas that should be focused on and how to proceed with learning. In this way, the test taker's learning effectiveness can be improved by analyzing the learning data, predicting the possibility of passing, and proposing an optimal study strategy.

[0049] The diagnostic unit can analyze the test-taker's learning data and provide a specific action plan for passing the exam. For example, the diagnostic unit uses a generative AI to analyze the test-taker's learning data and provide a specific action plan for passing the exam. For example, it can specifically suggest a daily study schedule and study content. The diagnostic unit can also develop an algorithm for providing an action plan for passing the exam. For example, the generative AI can analyze the test-taker's learning data and suggest specific study activities and goal setting. This can improve the test-taker's learning effectiveness by analyzing the learning data and providing a specific action plan for passing the exam.

[0050] The diagnostic unit can analyze the test taker's learning data and provide a study plan that can be adapted to different exams and qualification exams. For example, the diagnostic unit uses a generative AI to analyze the test taker's learning data and provide a study plan that can be adapted to different exams and qualification exams. For example, the diagnostic unit can propose a study plan that can be adapted to not only the exams of the test taker's desired school, but also other universities and qualification exams. The diagnostic unit can also develop an algorithm for providing a study plan that can be adapted to different exams and qualification exams. For example, the generative AI can analyze the test taker's learning data and generate a study plan that can be adapted to multiple exams. This can improve the test taker's learning effectiveness by analyzing the study data and providing a study plan that can be adapted to different exams and qualification exams.

[0051] The diagnostic unit can analyze the test taker's learning data and introduce a collaborative learning mode with other test takers, providing a format in which test takers work together to pass the exam. For example, the diagnostic unit has the generative AI analyze the test taker's learning data and introduce a collaborative learning mode with other test takers. For example, it can provide a format in which test takers solve problems in groups, working together to pass the exam. The diagnostic unit can also develop an algorithm for introducing the collaborative learning mode. For example, the generative AI can analyze the test taker's learning data and form optimal groups to promote collaborative learning. This allows test takers to analyze the learning data and introduce a collaborative learning mode with other test takers, working together to pass the exam.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The learning support system can further include a reward system. The reward system can award badges or points to test takers when they achieve certain goals. For example, it can reward test takers for getting consecutive correct answers or solving problems within a certain time. The reward system can also allow test takers to use the points they collect to obtain additional learning resources or benefits. This can increase test takers' motivation and encourage them to continue studying.

[0054] The learning support system may further include a social feedback function. The social feedback function allows test takers to receive feedback from other test takers and teachers on questions they have answered. For example, they may receive comments on the accuracy of their answers or on their answering methods. The social feedback function may also allow test takers to exchange advice with each other. This allows test takers to receive feedback from other people's perspectives and improve the quality of their learning.

[0055] The learning support system can also be equipped with a learning history visualization function. The learning history visualization function displays the questions that the test-taker has answered in the past and their scores in graphs and charts. For example, it visually shows the progress of the correct answer rate and the change in answer time. The learning history visualization function can also make it possible to see at a glance in which areas the test-taker has progressed and in which areas improvement is needed. This allows the test-taker to objectively evaluate their learning situation and create an effective study plan.

[0056] The learning support system can also be equipped with a study rhythm adjustment function. This function analyzes the study patterns of test-takers and suggests optimal study times and break times. For example, it adjusts the schedule so that test-takers study at times when they are most able to concentrate. The study rhythm adjustment function can also issue an alert to encourage test-takers to take appropriate breaks when they feel tired. This allows test-takers to study efficiently and maximize their learning effectiveness.

[0057] The learning support system can also be equipped with a function to promote intercultural understanding. The function provides test-takers with opportunities to interact with other test-takers from different cultures and backgrounds. For example, they can learn different perspectives and ways of thinking through online discussions and collaborative projects. The function can also provide quizzes and learning content related to different cultures. This allows test-takers to deepen their understanding of different cultures and develop a global perspective.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The problem-solving section records the questions that the examinee answers. For example, it records the content of the questions that the examinee answered and the answering method as digital data. It also records the accuracy of the answers and the answering time, and saves the accuracy rate and answering time in a database. Step 2: The diagnostic unit analyzes the answers recorded by the problem-solving unit and diagnoses strengths and weaknesses. For example, the generative AI analyzes the test-taker's answer data and identifies areas of strength and weakness. It also evaluates the consistency and confidence of the answers, converting them into numbers and reflecting them in the diagnostic results. Step 3: The question generator generates new questions based on the results of the diagnosis by the diagnostic unit. For example, the generator AI generates more difficult questions for areas in which the test-taker excels, and more basic questions for areas in which the test-taker is weak. It can also provide question formats that match the test-taker's learning style and preferences. Step 4: The presentation unit presents the questions generated by the question generation unit to the examinee. For example, the generated questions may be displayed on a screen so that the examinee can answer them. The generated questions may also be printed and provided on paper.

[0060] (Example 2) The learning support system according to an embodiment of the present invention analyzes the answers and answering methods given by test takers, and uses a generation AI to diagnose their strengths and weaknesses and provide optimal questions for each individual test taker. This allows the learning support system to efficiently support test takers' learning and improve their grades.

[0061] A learning support system according to an embodiment includes a question answering unit, a diagnosis unit, a question generation unit, and a presentation unit. The question answering unit records questions answered by test takers. For example, it records the content of the questions answered by the test takers and the answering methods as digital data. The question answering unit can also record the accuracy of the answers and the answering time. For example, the question answering unit stores the correct answer rate and answering time of the questions answered by the test takers in a database. The diagnosis unit analyzes the answers recorded by the question answering unit and diagnoses strengths and weaknesses. For example, the generation AI analyzes the test takers' answer data and identifies their strengths and weaknesses. The diagnosis unit can also evaluate the consistency and confidence of the answers. For example, the generation AI quantifies the consistency and confidence of the test takers' answers and reflects them in the diagnosis results. The question generation unit generates new questions based on the results of the diagnosis by the diagnosis unit. For example, the generation AI generates difficult questions for areas in which the test-taker is strong and basic questions for areas in which the test-taker is weak. The question generation unit can also provide question formats that match the test-taker's learning style and preferences. For example, the generation AI generates questions that make extensive use of diagrams and graphs for test-taker who prefer visual learning. The presentation unit presents the questions generated by the question generation unit to the test-taker. For example, the presentation unit displays the generated questions on a screen so that the test-taker can answer them. The presentation unit can also print the generated questions and provide them on paper. For example, the presentation unit prints the generated questions on a printer and distributes them to the test-taker. In this way, the learning support system according to the embodiment can support efficient learning by diagnosing the test-taker's strengths and weaknesses and providing individually optimal questions.

[0062] When analyzing answer data, the diagnostic unit can make more accurate diagnoses based on the test-taker's learning history and past performance data. For example, when the generation AI analyzes answer data, the diagnostic unit takes into account the test-taker's past learning history. For example, it diagnoses strengths and weaknesses based on the accuracy rate and answer time of questions previously answered. The diagnostic unit can also take into account past performance data. For example, the generation AI diagnoses strengths and weaknesses based on past test results and evaluation scores. In this way, by taking into account learning history and past performance data, more accurate diagnoses are possible.

[0063] The diagnostic unit evaluates the test-taker's confidence in their answers and can diagnose their strengths and weaknesses in more detail. For example, when the generation AI analyzes answer data, the diagnostic unit evaluates the test-taker's confidence in their answers. For example, it quantifies the confidence of the answers and diagnoses their strengths and weaknesses. The diagnostic unit can also evaluate the consistency and confidence of the answers. For example, the generation AI quantifies the consistency and confidence of the test-taker's answers and reflects this in the diagnosis results. In this way, by evaluating the confidence, it is possible to diagnose their strengths and weaknesses in more detail.

[0064] The diagnostic unit can diagnose the impact of emotional fluctuations on strengths and weaknesses based on the test-taker's emotions. For example, when the generation AI analyzes answer data, the diagnostic unit estimates the test-taker's emotions in real time. For example, it analyzes facial expressions and vocal tone to diagnose the impact of emotional fluctuations on strengths and weaknesses. The diagnostic unit can also quantify emotional fluctuations and reflect this in the diagnosis results. For example, the generation AI can quantify emotional fluctuations and diagnose tendencies in strengths and weaknesses. This enables more accurate learning support by diagnosing the impact of emotional fluctuations on strengths and weaknesses.

[0065] The question generation unit can provide a question format that matches the test-taker's learning style and preferences based on the diagnostic results. For example, the generation AI can create questions that make extensive use of diagrams and graphs for test-taker who prefer visual learning. The question generation unit can also provide a question format that matches the test-taker's preferences. For example, the generation AI can create questions that match the test-taker's preferences based on the test-taker's past learning history and questionnaire survey results. This maximizes the learning effect by providing a question format that matches the test-taker's learning style and preferences.

[0066] The question generation unit can analyze trends in past exam questions and create predicted questions based on the diagnostic results. For example, the generation AI in the question generation unit analyzes trends in past exam questions and creates predicted questions based on the diagnostic results. For example, it creates questions that are likely to appear in the next exam based on the frequency and format of questions in the past. The question generation unit can also analyze trends in past exam questions and develop algorithms for creating predicted questions. For example, the generation AI can learn data from past exam questions and generate predicted questions. This allows the system to analyze trends in past exam questions and create predicted questions, effectively supporting test takers in their exam preparation.

[0067] The question generation unit can create questions to reduce stress based on the test-taker's emotions and the test-takers' emotions, based on the diagnosis results. For example, the question generation unit uses a generation AI to estimate the test-taker's emotions in real time based on the diagnosis results and create questions to reduce stress. For example, the question generation unit can provide a question format and content that promotes relaxation. The question generation unit can also develop an algorithm to create questions to reduce stress, taking into account the test-taker's emotions. For example, the generation AI can quantify emotional fluctuations and generate questions to reduce stress. This can optimize the learning environment by taking into account the test-taker's emotions and creating questions to reduce stress.

[0068] The question generation unit can randomly combine questions of different difficulty levels based on the diagnostic results, thereby improving the adaptability of test takers. For example, the question generation unit provides questions of different difficulty levels by randomly combining them based on the diagnostic results using a generation AI. For example, easy questions and difficult questions may be presented alternately to improve the adaptability of test takers. The question generation unit can also develop an algorithm for randomly combining questions of different difficulty levels. For example, the generation AI evaluates the difficulty of the questions and generates questions by randomly combining them. In this way, the adaptability of test takers can be improved by randomly combining questions of different difficulty levels.

[0069] The question generation unit can introduce a collaborative learning mode with other test takers based on the diagnostic results, providing a format for solving problems together. The question generation unit, for example, the generation AI introduces a collaborative learning mode with other test takers based on the diagnostic results. For example, it provides a format for solving problems in groups, allowing students to work together to advance their studies. The question generation unit can also develop an algorithm for introducing the collaborative learning mode. For example, the generation AI analyzes the learning data of test takers, forms optimal groups, and promotes collaborative learning. This allows test takers to work together to advance their studies by introducing a collaborative learning mode with other test takers.

[0070] The question generation unit can create questions that elicit positive emotions based on the test-taker's emotions and the test-taker's emotions, based on the diagnostic results. For example, the question generation unit uses the generation AI to estimate the test-taker's emotions in real time based on the diagnostic results and create questions that elicit positive emotions. For example, it provides question formats and content that allow test-taker to feel a sense of success. The question generation unit can also develop algorithms to create questions that elicit positive emotions, taking into account the test-taker's emotions. For example, the generation AI can quantify emotional fluctuations and generate questions that elicit positive emotions. By creating questions that elicit positive emotions, emotional factors can also be reflected in the evaluation.

[0071] The presentation unit can analyze the answering process in real time when a test-taker solves a problem and provide immediate feedback. For example, the presentation unit can analyze the answering process in real time when a test-taker solves a problem and provide immediate feedback. For example, it can detect errors in the middle of the answer and present the correct solution. The presentation unit can also develop algorithms for analyzing the answering process in real time. For example, the generation AI can monitor the progress of the answer and provide feedback at the appropriate time. This makes it possible to analyze the answering process in real time and provide immediate feedback, thereby improving learning effectiveness.

[0072] The presentation unit can measure the time it takes for test takers to solve problems and provide time management advice. For example, the presentation unit can measure the time it takes for test takers to solve problems and provide time management advice. For example, it can present the amount of time that should be spent on each problem and suggest efficient time allocation. The presentation unit can also develop an algorithm for measuring the answer time. For example, the generation AI can monitor the answer time in real time and provide time management advice. This makes it possible to support efficient learning by measuring the answer time and providing time management advice.

[0073] The presentation unit can use the emotion estimation function to provide relaxation techniques to reduce stress based on the emotions felt by test-takers when solving problems. For example, the presentation unit can estimate the emotions of test-takers in real time when solving problems and provide relaxation techniques to reduce stress. For example, the presentation unit can play relaxing music. The presentation unit can also develop algorithms to monitor emotions and provide relaxation techniques to reduce stress. For example, the generative AI can quantify emotional fluctuations and provide relaxation techniques. This can optimize the learning environment by monitoring emotions and providing relaxation techniques to reduce stress.

[0074] The presentation unit can provide a realistic learning environment using virtual reality (VR) when test takers solve problems. For example, the presentation unit can provide a realistic learning environment using a VR headset when test takers solve problems. For example, the atmosphere of an exam hall can be reproduced, allowing test takers to solve problems in an environment similar to that of an actual exam. The presentation unit can also develop algorithms for providing a learning environment using virtual reality. For example, a generative AI can provide a virtual classroom or interactive simulation, allowing test takers to learn in a realistic environment. This can improve learning effectiveness by providing a realistic learning environment using virtual reality.

[0075] The presentation unit can improve motivation by introducing a real-time competition mode with other test takers. For example, the presentation unit can introduce a mode in which test takers compete with other test takers in real time when solving a problem. For example, test takers can solve the same problem at the same time and compete for answer speed and accuracy. The presentation unit can also develop algorithms for introducing the real-time competition mode. For example, the generation AI can analyze test taker answer data in real time and provide the competition mode. This can improve motivation by introducing a real-time competition mode with other test takers.

[0076] The presentation unit can use the emotion estimation function to provide music and videos that elicit positive emotions based on the emotions felt by test-takers when solving problems. For example, the presentation unit uses a camera to analyze facial expressions while test-takers are solving problems and estimates their emotions in real time. The generation AI uses this data to provide music that elicits positive emotions. The presentation unit can also develop algorithms to monitor emotions and provide music and videos that elicit positive emotions. For example, the generation AI can quantify emotional fluctuations and provide music and videos that elicit positive emotions. This can improve learning effectiveness by analyzing emotions and providing music and videos that elicit positive emotions.

[0077] The diagnostic unit can visualize the learning progress in real time and provide feedback to the test-taker. For example, the diagnostic unit visualizes the learning progress in real time when the generative AI repeats diagnosis and question presentation. For example, it visually displays the test-taker's learning progress using graphs and charts. The diagnostic unit can also develop algorithms for visualizing the learning progress. For example, the generative AI analyzes learning data and displays the progress in real time. This makes it possible to visualize the learning progress in real time and provide feedback, thereby improving the learning effect.

[0078] The diagnostic unit can analyze the test-taker's learning patterns and propose an optimal learning schedule. For example, the diagnostic unit analyzes the test-taker's learning patterns when the generation AI repeats diagnosis and question presentation. For example, it analyzes the time and frequency of study and proposes an optimal learning schedule. The diagnostic unit can also develop algorithms for analyzing learning patterns. For example, the generation AI analyzes learning data and proposes an optimal learning schedule. This makes it possible to support efficient learning by analyzing learning patterns and proposing an optimal learning schedule.

[0079] The diagnostic unit can use the emotion estimation function to provide advice to maintain the test-taker's motivation to study based on the test-taker's emotional fluctuations. For example, as the generation AI repeats diagnosis and question presentation, the diagnostic unit estimates the test-taker's emotions in real time and provides advice to maintain the test-taker's motivation to study. For example, it can send encouraging messages depending on the emotional fluctuations. The diagnostic unit can also develop an algorithm to monitor emotional fluctuations and provide advice to maintain motivation. For example, the generation AI can quantify emotional fluctuations and provide advice to maintain motivation. This makes it possible to improve learning effectiveness by monitoring emotional fluctuations and providing advice to maintain motivation to study.

[0080] The diagnostic unit can combine learning across different subjects to improve overall academic ability. For example, when the generative AI repeats diagnosis and problem presentation, the diagnostic unit combines learning across different subjects. For example, it can alternate between mathematics and physics questions to improve overall academic ability. The diagnostic unit can also develop algorithms for combining learning across different subjects. For example, the generative AI can analyze learning data and suggest the optimal combination of subjects. This makes it possible to combine learning across different subjects to improve overall academic ability.

[0081] The diagnostic unit can introduce a collaborative learning mode with other test-takers, providing a format for cooperative learning. For example, when the generative AI repeats diagnosis and problem presentation, the diagnostic unit introduces a collaborative learning mode with other test-takers. For example, it provides a format for solving problems in groups, allowing for cooperative learning. The diagnostic unit can also develop algorithms for introducing the collaborative learning mode. For example, the generative AI analyzes the test-takers' learning data, forms optimal groups, and promotes collaborative learning. This allows for cooperative learning by introducing a collaborative learning mode with other test-takers.

[0082] The diagnostic unit can use the emotion estimation function to provide a learning environment that elicits positive emotions based on the test-taker's emotional fluctuations. For example, the diagnostic unit estimates the test-taker's emotions in real time as the generative AI repeats diagnosis and question presentation, and provides a learning environment that elicits positive emotions. For example, it plays relaxing music. The diagnostic unit can also monitor emotional fluctuations and develop algorithms to provide a learning environment that elicits positive emotions. For example, the generative AI can quantify emotional fluctuations and provide a learning environment that elicits positive emotions. This makes it possible to improve learning effectiveness by monitoring emotional fluctuations and providing a learning environment that elicits positive emotions.

[0083] The problem generator can provide specialized or advanced problems to further develop areas of strength. For example, the problem generator provides specialized problems to further develop areas in which the generation AI is strong. For example, a test-taker who is good at mathematics can be given advanced questions on calculus or linear algebra. The problem generator can also develop algorithms to provide advanced questions to further develop areas of strength. For example, the generation AI can identify an area in which a test-taker is strong and generate advanced questions specialized for that area. This can improve learning effectiveness by providing specialized or advanced questions to further develop areas of strength.

[0084] The problem generation unit can provide problems that allow students to learn step by step, from basic to applied, in order to overcome their weaknesses. For example, the problem generation unit provides problems that allow students to learn step by step, from basic to applied, in order to overcome their weaknesses. For example, for a test-taker who is not good at mathematical probability and statistics, questions can be presented step by step, from basic probability problems to applied statistics problems. The problem generation unit can also develop an algorithm for providing problems that allow students to learn step by step. For example, the generation AI can identify a test-taker's weak areas and generate step-by-step problems specialized for those areas. This can improve learning effectiveness by providing problems that allow students to learn step by step, from basic to applied, in order to overcome their weaknesses.

[0085] The question generation unit can use the emotion estimation function to provide a learning approach to elicit positive emotions based on the emotions of the test-taker. For example, the question generation unit uses the emotion estimation function to monitor the emotions of the test-taker in real time and provide a learning approach to elicit positive emotions. For example, if the test-taker is feeling stressed, the question generation unit can provide advice on how to relax. The question generation unit can also develop an algorithm for monitoring emotions and providing a learning approach to elicit positive emotions. For example, the generation AI can quantify emotional fluctuations and provide a learning approach to elicit positive emotions. This can improve learning effectiveness by monitoring emotions and providing a learning approach to elicit positive emotions.

[0086] The question generation unit can provide a combination of questions from different fields to further develop areas of strength. For example, the question generation unit provides a combination of questions from different fields to further develop areas in which the generation AI is strong. For example, a test-taker who is good at mathematics can be given questions related to mathematics and physics. The question generation unit can also develop an algorithm for combining questions from different fields. For example, the generation AI can identify an area in which a test-taker is strong and generate questions from different fields related to that area. This can improve learning effectiveness by providing a combination of questions from different fields to further develop areas of strength.

[0087] The question generation unit can introduce a collaborative learning mode with other test-takers to help them overcome their weaknesses, providing a format for them to study together. The question generation unit, for example, introduces a collaborative learning mode with other test-takers to help the generation AI overcome their weaknesses. For example, it provides a format for solving problems in groups, and they can study together. The question generation unit can also develop an algorithm for introducing the collaborative learning mode. For example, the generation AI analyzes the learning data of test-takers, forms optimal groups, and promotes collaborative learning. By introducing a collaborative learning mode with other test-takers, the learning effect for overcoming weaknesses can be improved.

[0088] The question generation unit can use the emotion estimation function to provide a learning environment that elicits positive emotions based on the emotions of the test-taker. For example, the question generation unit uses the emotion estimation function to monitor the emotions of the test-taker in real time and provide a learning environment that elicits positive emotions. For example, relaxing music is played. The question generation unit can also develop an algorithm for monitoring emotions and providing a learning environment that elicits positive emotions. For example, the generation AI quantifies emotional fluctuations and provides a learning environment that elicits positive emotions. This makes it possible to improve learning effectiveness by monitoring emotions and providing a learning environment that elicits positive emotions.

[0089] The diagnostic unit can analyze the test taker's learning data, predict the possibility of passing, and propose an optimal study strategy. In the diagnostic unit, for example, the generative AI analyzes the test taker's learning data and predicts the possibility of passing. For example, it calculates the probability of passing based on past learning data and mock test scores. The diagnostic unit can also develop algorithms to predict the possibility of passing and propose an optimal study strategy. For example, the generative AI analyzes the test taker's learning data and suggests areas that should be focused on and how to proceed with learning. In this way, the test taker's learning effectiveness can be improved by analyzing the learning data, predicting the possibility of passing, and proposing an optimal study strategy.

[0090] The diagnostic unit can analyze the test-taker's learning data and provide a specific action plan for passing the exam. For example, the diagnostic unit uses a generative AI to analyze the test-taker's learning data and provide a specific action plan for passing the exam. For example, it can specifically suggest a daily study schedule and study content. The diagnostic unit can also develop an algorithm for providing an action plan for passing the exam. For example, the generative AI can analyze the test-taker's learning data and suggest specific study activities and goal setting. This can improve the test-taker's learning effectiveness by analyzing the learning data and providing a specific action plan for passing the exam.

[0091] The diagnostic unit can use the emotion estimation function to provide advice to maintain motivation to pass the exam based on the examinee's emotions. For example, the generation AI in the diagnostic unit uses the emotion estimation function to monitor the examinee's emotions in real time and provide advice to maintain motivation to pass the exam. For example, it can send encouraging messages in response to emotional fluctuations. The diagnostic unit can also develop an algorithm to monitor emotional fluctuations and provide advice to maintain motivation. For example, the generation AI can quantify emotional fluctuations and provide advice to maintain motivation. This can improve the learning effectiveness of examinees by monitoring emotions and providing advice to maintain motivation to pass the exam.

[0092] The diagnostic unit can analyze the test taker's learning data and provide a study plan that can be adapted to different exams and qualification exams. For example, the diagnostic unit uses a generative AI to analyze the test taker's learning data and provide a study plan that can be adapted to different exams and qualification exams. For example, the diagnostic unit can propose a study plan that can be adapted to not only the exams of the test taker's desired school, but also other universities and qualification exams. The diagnostic unit can also develop an algorithm for providing a study plan that can be adapted to different exams and qualification exams. For example, the generative AI can analyze the test taker's learning data and generate a study plan that can be adapted to multiple exams. This can improve the test taker's learning effectiveness by analyzing the study data and providing a study plan that can be adapted to different exams and qualification exams.

[0093] The diagnostic unit can analyze the test taker's learning data and introduce a collaborative learning mode with other test takers, providing a format in which test takers work together to pass the exam. For example, the diagnostic unit has the generative AI analyze the test taker's learning data and introduce a collaborative learning mode with other test takers. For example, it can provide a format in which test takers solve problems in groups, working together to pass the exam. The diagnostic unit can also develop an algorithm for introducing the collaborative learning mode. For example, the generative AI can analyze the test taker's learning data and form optimal groups to promote collaborative learning. This allows test takers to analyze the learning data and introduce a collaborative learning mode with other test takers, working together to pass the exam.

[0094] The diagnostic unit can use the emotion estimation function to provide a learning environment that draws out positive emotions based on the test-taker's emotions. For example, the diagnostic unit uses the emotion estimation function to monitor the test-taker's emotions in real time and provide a learning environment that draws out positive emotions. For example, relaxing music is played. The diagnostic unit can also develop an algorithm to monitor emotional fluctuations and provide a learning environment that draws out positive emotions. For example, the generative AI quantifies emotional fluctuations and provides a learning environment that draws out positive emotions. This makes it possible to improve the learning effectiveness of test-taker by monitoring emotions and providing a learning environment that draws out positive emotions.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The learning support system can further include a reward system. The reward system can award badges or points to test takers when they achieve certain goals. For example, it can reward test takers for getting consecutive correct answers or solving problems within a certain time. The reward system can also allow test takers to use the points they collect to obtain additional learning resources or benefits. This can increase test takers' motivation and encourage them to continue studying.

[0097] The learning support system may further include a social feedback function. The social feedback function allows test takers to receive feedback from other test takers and teachers on questions they have answered. For example, they may receive comments on the accuracy of their answers or on their answering methods. The social feedback function may also allow test takers to exchange advice with each other. This allows test takers to receive feedback from other people's perspectives and improve the quality of their learning.

[0098] The learning support system can also be equipped with a learning history visualization function. The learning history visualization function displays the questions that the test-taker has answered in the past and their scores in graphs and charts. For example, it visually shows the progress of the correct answer rate and the change in answer time. The learning history visualization function can also make it possible to see at a glance in which areas the test-taker has progressed and in which areas improvement is needed. This allows the test-taker to objectively evaluate their learning situation and create an effective study plan.

[0099] The learning support system can also be equipped with a study rhythm adjustment function. This function analyzes the study patterns of test-takers and suggests optimal study times and break times. For example, it adjusts the schedule so that test-takers study at times when they are most able to concentrate. The study rhythm adjustment function can also issue an alert to encourage test-takers to take appropriate breaks when they feel tired. This allows test-takers to study efficiently and maximize their learning effectiveness.

[0100] The learning support system can also be equipped with a function to promote intercultural understanding. The function provides test-takers with opportunities to interact with other test-takers from different cultures and backgrounds. For example, they can learn different perspectives and ways of thinking through online discussions and collaborative projects. The function can also provide quizzes and learning content related to different cultures. This allows test-takers to deepen their understanding of different cultures and develop a global perspective.

[0101] The diagnosis unit can estimate the emotions of the test-taker and evaluate the learning progress based on the estimated emotions. For example, if the test-taker is feeling stressed, it can determine that the test-taker's learning progress may be delayed. The diagnosis unit can also monitor emotional fluctuations and identify factors that affect the learning progress. For example, if the test-taker has positive emotions, it can determine that the test-taker's learning progress is good. This makes it possible to evaluate the learning progress taking into account emotional fluctuations.

[0102] The diagnostic unit can estimate the test-taker's emotions and provide advice to maintain motivation to study based on the estimated emotions. For example, if the test-taker feels fatigued, the diagnostic unit can advise the test-taker to take a break. The diagnostic unit can also monitor emotional fluctuations and suggest specific methods to maintain motivation. For example, if the test-taker has positive emotions, the diagnostic unit can advise the test-taker to take on more challenging problems. This makes it possible to provide advice to maintain motivation that takes emotional fluctuations into account.

[0103] The diagnostic unit can estimate the examinee's emotions and provide advice to reduce study stress based on the estimated emotions. For example, if the examinee is nervous, it can suggest ways to relax. The diagnostic unit can also monitor emotional fluctuations and suggest specific ways to reduce stress. For example, if the examinee is feeling stressed, it can advise the examinee to take deep breaths or do some light exercise. This makes it possible to provide advice on stress reduction that takes into account emotional fluctuations.

[0104] The diagnostic unit can estimate the examinee's emotions and provide advice to improve study efficiency based on the estimated emotions. For example, if the examinee is lacking concentration, the diagnostic unit can advise the examinee to take a short break. The diagnostic unit can also monitor emotional fluctuations and suggest efficient study methods. For example, if the examinee is feeling positive, the diagnostic unit can advise the examinee to take on more difficult questions. This makes it possible to suggest efficient study methods that take emotional fluctuations into consideration.

[0105] The diagnostic unit can estimate the emotions of test takers and evaluate their learning outcomes based on the estimated emotions. For example, if a test taker has positive emotions, it determines that their learning outcomes are good. The diagnostic unit can also monitor emotional fluctuations and identify factors that affect learning outcomes. For example, if a test taker has negative emotions, it determines that their learning outcomes may be declining. This makes it possible to evaluate learning outcomes taking emotional fluctuations into account.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The problem-solving section records the questions that the examinee answers. For example, it records the content of the questions that the examinee answered and the answering method as digital data. It also records the accuracy of the answers and the answering time, and saves the accuracy rate and answering time in a database. Step 2: The diagnostic unit analyzes the answers recorded by the problem-solving unit and diagnoses strengths and weaknesses. For example, the generative AI analyzes the test-taker's answer data and identifies areas of strength and weakness. It also evaluates the consistency and confidence of the answers, converting them into numbers and reflecting them in the diagnostic results. Step 3: The question generator generates new questions based on the results of the diagnosis by the diagnostic unit. For example, the generator AI generates more difficult questions for areas in which the test-taker excels, and more basic questions for areas in which the test-taker is weak. It can also provide question formats that match the test-taker's learning style and preferences. Step 4: The presentation unit presents the questions generated by the question generation unit to the examinee. For example, the generated questions may be displayed on a screen so that the examinee can answer them. The generated questions may also be printed and provided on paper.

[0108] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

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

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question answering section for recording questions answered by examinees; a diagnosis unit that analyzes the answers recorded by the problem answering unit and diagnoses tendencies of strengths and weaknesses; a question generator that generates new questions based on the results of the diagnosis by the diagnosis unit; a presentation unit that presents the questions generated by the question generation unit to the examinee. A system characterized by:

2. The diagnostic unit When analyzing answer data, a more accurate diagnosis is made based on the test-taker's learning history and past performance data.

2. The system of claim 1.

3. The question generator Based on the diagnostic results, we provide test formats that match the test-taker's learning style and preferences.

2. The system of claim 1.

4. The presentation unit Analyzes the test-taker's solution process in real time and provides immediate feedback 2. The system of claim 1.

5. The diagnostic unit Providing advice to maintain motivation to study based on students' emotional fluctuations 2. The system of claim 1.

6. The question generator Providing a learning approach that draws out positive emotions based on test-taker's emotions 2. The system of claim 1.

7. The diagnostic unit Based on the test-taker's emotions, we provide advice to help them maintain their motivation to pass the test.

2. The system of claim 1.

Images