system

Abstract

The system according to this embodiment aims to efficiently generate practice problems tailored to the question trends of the applicant's desired school, thereby enabling the student to effectively prepare for the exam. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit receives input of the desired school, subject, and subject area of ​​the questions. The analysis unit analyzes the question trends based on the information received by the reception unit. The generation unit generates similar questions based on the question trends analyzed by the analysis unit. The output unit outputs answers to the questions generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently generate practice problems in line with the question tendencies of the target schools, and it is difficult for examinees to take sufficient countermeasures.

[0005] The system according to the embodiment aims to efficiently generate practice problems in line with the question tendencies of the target schools and enable examinees to effectively take countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit receives input of the desired school, subject, and subject area of ​​the questions. The analysis unit analyzes the question trends based on the information received by the reception unit. The generation unit generates similar questions based on the question trends analyzed by the analysis unit. The output unit outputs answers to the questions generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment efficiently generates practice problems tailored to the question trends of the applicant's desired school, enabling the student to prepare effectively. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The practice problem generation and scoring system according to an embodiment of the present invention is a system that generates practice problems tailored to the question trends of a target school and scores the answers submitted by examinees. This system accepts input of the target school, subject, and subject area, and the generating AI creates similar problems based on this information. For example, it analyzes the question trends of past exams from the target school and generates new problems based on those trends. This allows examinees to solve many problems similar to past exams, making it easier to grasp the question trends. Next, the examinee creates an answer to the problem. When the answer is completed, or when the examinee declares that they do not know the answer, the generating AI outputs the answer to the problem. For example, if the examinee enters an answer and declares that they do not know the answer, the generating AI generates and outputs the answer for that part. This system expands the amount of practice problems that can be done, shortens the time required to create practice problems, and reduces the workload in educational settings. Specifically, because the generating AI automatically generates problems, the time spent by cram school instructors creating problems is significantly reduced. Also, because the generating AI generates the answers, examinees can proceed with their studies efficiently. Furthermore, this system is expected to have future applications, such as being used for qualification examinations. For example, by analyzing the trends in questions asked in qualification exams and generating questions based on those trends, it can also be used for qualification exam preparation. In this way, a system for generating and grading practice problems tailored to the question trends of a target school is a groundbreaking system that improves the learning efficiency of test-takers and reduces the workload of educational institutions. Thus, a system for generating and grading practice problems can improve the learning efficiency of test-takers and reduce the workload of educational institutions.

[0029] The practice problem generation and scoring system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit accepts input of the desired school, subject, and subject area of ​​the exam. The reception unit provides, for example, an interface for the user to input the desired school, subject, and subject area of ​​the exam. The analysis unit analyzes the exam trends based on the information received by the reception unit. The analysis unit uses, for example, an algorithm to collect past exam data from the desired school and analyze the exam trends. The generation unit generates similar problems based on the exam trends analyzed by the analysis unit. The generation unit generates new problems based on the exam trends of the desired school, for example, using a generation AI. The generation AI learns the exam trends of past exams and generates new problems based on those trends. The output unit outputs answers to the problems generated by the generation unit. The output unit generates and outputs answers to parts that a test-taker does not know, for example, when the test-taker inputs an answer and declares parts they do not know. As a result, the practice problem generation and scoring system according to this embodiment can generate and score practice problems that are tailored to the question trends of the applicant's desired school.

[0030] The reception department accepts input of desired schools, subjects, and subject areas. For example, the reception department provides an interface for users to input their desired schools, subjects, and subject areas. Specifically, users can input information about their desired schools, subjects, and subject areas through a web form or mobile application designed for ease of use. This interface allows users to review the information they enter in real time and make corrections or additions as needed. The interface also has a function to save information previously entered by users for reuse on subsequent visits. Furthermore, the reception department manages a database to automatically categorize the information entered by users and appropriately hand it over to the analysis and generation departments. This database is designed to efficiently store user input information and allow for quick access when needed. For example, if a user selects a specific university as their desired school, information regarding that university's past exam data and question trends is automatically associated and sent to the analysis department. This allows the reception department to quickly and accurately collect the information users need, improving the overall efficiency of the system.

[0031] The analysis department analyzes question trends based on information received by the reception department. For example, the analysis department collects past exam data from the target school and uses algorithms to analyze question trends. Specifically, it stores exam questions from the past few years in a database and analyzes in detail the frequency, difficulty, and format of each question. The analysis algorithm uses natural language processing technology to analyze the question text and extract the themes and keywords that will be asked. It also uses statistical methods to calculate how often specific themes and formats will be asked, and quantifies the question trends. Furthermore, the analysis department can use AI to learn from past exam data and predict future question trends. For example, it uses machine learning models to predict future question trends from past question patterns and provide users with more accurate information. Based on these analysis results, the analysis department can provide specific advice to users on what preparations they should make for their target school's exam. In this way, the analysis department can provide users with important information to help them study efficiently and maximize the effectiveness of the entire system.

[0032] The generation unit generates similar problems based on the question trends analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate new problems based on the question trends of the target school. For example, the generation AI learns the question trends of past exams and generates new problems based on those trends. Specifically, the generation AI uses natural language generation technology to create new problem statements while mimicking the writing style and format of past exams. The generation AI can generate related problems based on keywords and themes extracted from the past exam database. For example, for mathematics problems, it can generate problems based on specific formulas and theorems, and for English problems, it can generate problems based on specific grammar and vocabulary. The generation AI also has a function to adjust the difficulty level of the problems, so that it can provide problems of appropriate difficulty according to the user's learning progress and level of understanding. Furthermore, the generation unit simultaneously generates example answers and explanations for the generated problems, so that users can self-grade and review after solving the problems. In this way, the generation unit can support users in conducting effective learning tailored to the question trends of their target school and improve the overall learning effect of the system.

[0033] The output unit outputs the answers to the problems generated by the generation unit. For example, if a test-taker inputs an answer and declares parts they don't understand, the output unit will generate and output the answer to those parts. Specifically, it provides an interface for the user to input an answer, and once an answer is entered, the system automatically analyzes the answer and determines whether it is correct or incorrect. If the user declares parts they don't understand, the output unit uses the generation AI to generate the answer to those parts and outputs it along with a detailed explanation. The generation AI uses natural language processing technology to understand the content of the problem and generate an appropriate answer. For example, for a math problem, it will explain the steps to solve the problem and how to apply the formula in detail, and for an English problem, it will provide explanations of grammar and vocabulary. The output unit also has a function to display the answer and explanation in a visually easy-to-understand way so that the user can easily check the answer. For example, it can highlight important points and supplement the explanation with diagrams and tables. Furthermore, the output unit also provides a function to save the user's answer history so that it can be reviewed later. In this way, the output unit can deepen the user's understanding during the problem-solving process and support effective learning.

[0034] The reception desk can receive information from test-takers indicating parts they do not understand. For example, the reception desk can provide a function for test-takers to mark parts they do not understand when entering their answers. The reception desk can also accept text input of parts they do not understand. Furthermore, the reception desk can accept voice input of parts they do not understand. This allows test-takers to receive appropriate feedback by reporting parts they do not understand. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when a test-taker enters parts they do not understand, the reception desk can use AI to automatically detect those parts and provide feedback.

[0035] The generation unit can analyze the question trends of past exams from the target school and generate new questions based on those trends. For example, the generation unit collects past exam data from the target school and analyzes the question trends using a generation AI. The generation AI learns the question trends of past exams and generates new questions based on those trends. The generation unit, for example, has the generation AI analyze the question trends of past exams and generates new questions based on those trends. The generation unit, for example, has the generation AI analyze the question trends of past exams and generates new questions based on those trends. This improves the learning effectiveness of test-takers by generating questions based on the question trends of the target school. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs past exam data from the target school into the generation AI, and the generation AI generates new questions.

[0036] The output unit can generate and output answers to parts that the test-taker does not understand when they input their answers and declare the parts they do not understand. The output unit can, for example, provide a function to mark parts that the test-taker does not understand when they input their answers. The output unit can also accept input of parts that the test-taker does not understand as text. Furthermore, the output unit can accept input of parts that the test-taker does not understand as voice. For example, when the test-taker inputs parts they do not understand, the output unit generates and outputs the answer to that part. The output unit can, for example, use a generation AI to generate and output the answer to the part that the test-taker does not understand. This improves the efficiency of learning by allowing the test-taker to obtain answers to parts they do not understand. Some or all of the above processing in the output unit may be performed using AI or not. For example, when the test-taker inputs parts they do not understand, the output unit can automatically generate and output the answer using AI.

[0037] The analysis unit can analyze the trends in questions asked in qualification exams and generate questions based on those trends. For example, the analysis unit collects past exam data for qualification exams and uses an algorithm to analyze the trends in questions. The analysis unit collects past exam data for qualification exams and uses an algorithm to analyze the trends in questions. The analysis unit collects past exam data for qualification exams and uses an algorithm to analyze the trends in questions. By generating questions based on the trends in questions asked in qualification exams, it can also be used for qualification exam preparation. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs past exam data for qualification exams into an AI, the AI ​​analyzes the trends in questions, and generates new questions based on those trends.

[0038] The generation unit can generate questions based on the question trends of qualification exams. For example, the generation unit collects past exam question data and analyzes the question trends using a generation AI. The generation AI learns the question trends of past questions and generates new questions based on those trends. For example, the generation unit has the generation AI analyze the question trends of past qualification exams and generate new questions based on those trends. For example, the generation unit has the generation AI analyze the question trends of past qualification exams and generate new questions based on those trends. This allows the system to generate questions based on the question trends of qualification exams, making it useful for qualification exam preparation. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs past exam question data into the generation AI, and the generation AI generates new questions.

[0039] The reception desk can analyze past input history and suggest the optimal input method. For example, the reception desk can collect and analyze data on the desired schools and subjects that the user has entered in the past. For example, the reception desk can automatically display as suggestions the desired schools and subjects that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest desired schools and subjects to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input past input history data into AI, and the AI ​​can suggest the optimal input method.

[0040] The reception unit can filter input content based on the user's learning progress. For example, the reception unit collects and analyzes test results and study time data to evaluate the user's learning progress. For example, the reception unit can prioritize inputting unstudied areas based on the user's learning progress. The reception unit can also exclude areas that have already been studied based on the user's learning progress. Furthermore, the reception unit can highlight areas that the user finds particularly difficult based on the user's learning progress. This allows for efficient learning by filtering input content based on the user's learning progress. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs learning progress data into the AI, and the AI ​​filters the input content to the optimal level.

[0041] The reception system can prioritize inputting highly relevant information based on the user's geographical location during data entry. For example, the reception system can acquire the user's geographical location and prioritize displaying highly relevant information. For example, the reception system can prioritize displaying nearby schools of interest based on the user's geographical location. The reception system can also prioritize displaying region-specific subjects or fields of study based on the user's geographical location. Furthermore, the reception system can prioritize displaying information on local educational institutions based on the user's geographical location. This enables efficient data entry by prioritizing the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system inputs geographical location data into the AI, and the AI ​​prioritizes displaying highly relevant information.

[0042] The reception desk can analyze the user's social media activity and input relevant information during the input process. For example, the reception desk can collect and analyze the user's social media activity data. For example, the reception desk can prioritize displaying subjects or fields of interest based on the user's social media activity. It can also prioritize displaying information related to the user's desired school based on the user's social media activity. Furthermore, the reception desk can prioritize displaying resources that are useful for learning based on the user's social media activity. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input social media activity data into AI, and the AI ​​can prioritize displaying relevant information.

[0043] The analysis unit can predict current trends by referring to past question trend data during analysis. For example, the analysis unit collects and analyzes past question trend data. For example, the analysis unit predicts current question trends based on past question trend data. The analysis unit can also predict future question trends based on past question trend data. Furthermore, the analysis unit can predict question trends for specific subjects or fields based on past question trend data. This allows for the prediction of current trends by referring to past question trend data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs past question trend data into AI, and the AI ​​predicts current question trends.

[0044] The analysis unit can apply different analysis algorithms to each subject during the analysis. For example, the analysis unit may apply a specific algorithm to analyze the trends in mathematics questions. For example, the analysis unit may apply a different algorithm to analyze the trends in English questions. Furthermore, the analysis unit may apply yet another algorithm to analyze the trends in science questions. This allows for efficient analysis by applying different analysis algorithms to each subject. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs subject-specific question trend data into the AI, and the AI ​​applies a different analysis algorithm for each subject.

[0045] The analysis unit can perform analysis based on the geographical distribution of target schools. For example, the analysis unit can collect and analyze geographical distribution data of target schools. For example, the analysis unit can analyze question trends based on the geographical distribution of target schools. The analysis unit can also analyze question trends in specific regions based on the geographical distribution of target schools. Furthermore, the analysis unit can compare question trends for each region based on the geographical distribution of target schools. This makes efficient analysis possible by performing analysis based on the geographical distribution of target schools. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data into AI, and the AI ​​can analyze question trends.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can collect and analyze relevant literature. For example, the analysis unit can improve the accuracy of its analysis of question trends by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis of specific subjects or fields by referring to relevant literature. Furthermore, the analysis unit can improve the accuracy of its prediction of future question trends by referring to relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs relevant literature data into the AI, and the AI ​​improves the accuracy of the analysis.

[0047] The generation unit can generate new problems by referring to past problem data during the generation process. For example, the generation unit collects and analyzes past problem data. For example, the generation unit generates new problems based on past problem data. The generation unit can also generate similar problems based on past problem data. Furthermore, the generation unit can generate problems of different formats based on past problem data. This allows for the efficient generation of new problems by referring to past problem data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs past problem data into a generation AI, and the generation AI generates new problems.

[0048] The generation unit can apply different generation algorithms to each subject during generation. For example, the generation unit applies a specific algorithm to generate mathematics problems. For example, it applies a different algorithm to generate English problems. Furthermore, the generation unit can apply yet another algorithm to generate science problems. This allows for efficient problem generation by applying different generation algorithms to each subject. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs subject-specific problem data into the generation AI, and the generation AI applies a different generation algorithm for each subject.

[0049] The generation unit can generate questions based on the geographical distribution of the target schools during the generation process. For example, the generation unit collects and analyzes geographical distribution data of the target schools. The generation unit generates questions based on the geographical distribution of the target schools. The generation unit can also generate questions for specific regions based on the geographical distribution of the target schools. Furthermore, the generation unit can compare questions from different regions based on the geographical distribution of the target schools. This enables efficient question generation by generating questions based on the geographical distribution of the target schools. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs geographical distribution data into a generation AI, and the generation AI generates questions.

[0050] The generation unit can improve the accuracy of its generation by referring to relevant literature during the generation process. For example, the generation unit can collect and analyze relevant literature. The generation unit can improve the accuracy of problem generation by referring to relevant literature. It can also improve the accuracy of problem generation for specific subjects or fields by referring to relevant literature. Furthermore, the generation unit can improve the accuracy of predicting future problem generation by referring to relevant literature. As a result, the accuracy of generation is improved by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs relevant literature data into the generation AI, and the generation AI improves the accuracy of the generation.

[0051] The output unit can generate the optimal answer by referring to past answer data during output. For example, the output unit collects and analyzes past answer data. For example, the output unit generates the optimal answer based on past answer data. The output unit can also generate similar answers based on past answer data. Furthermore, the output unit can generate answers in different formats based on past answer data. This allows for the efficient generation of the optimal answer by referring to past answer data. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit inputs past answer data into AI, and the AI ​​generates the optimal answer.

[0052] The output unit can apply different output algorithms to each subject during output. For example, the output unit might apply a specific algorithm to output the answer to a mathematics question. For example, it might apply a different algorithm to output the answer to an English question. Furthermore, the output unit could apply yet another algorithm to output the answer to a science question. This allows for efficient answer generation by applying different output algorithms to each subject. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit inputs answer data for each subject into the AI, and the AI ​​applies a different output algorithm for each subject.

[0053] The output unit can generate answers based on the geographical distribution of the target schools at the time of output. For example, the output unit collects and analyzes geographical distribution data of the target schools. The output unit generates answers based on the geographical distribution of the target schools. The output unit can also generate answers for a specific region based on the geographical distribution of the target schools. Furthermore, the output unit can compare answers for each region based on the geographical distribution of the target schools. This enables efficient answer generation by generating answers based on the geographical distribution of the target schools. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit inputs geographical distribution data into AI, and the AI ​​generates answers.

[0054] The output unit can improve the accuracy of the answers by referring to relevant literature during output. For example, the output unit collects and analyzes relevant literature. The output unit improves the accuracy of the answers by referring to relevant literature. The output unit can also improve the accuracy of answers for specific subjects or fields by referring to relevant literature. Furthermore, the output unit can improve the accuracy of future answer predictions by referring to relevant literature. As a result, the accuracy of the answers is improved by referring to relevant literature. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit inputs relevant literature data into the AI, and the AI ​​improves the accuracy of the answers.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can estimate the user's learning style and customize the interface based on that estimate. For example, it can analyze what types of problems the user has preferred to solve in the past and prioritize displaying problems of that type. It can also analyze when the user has studied in the past and send notifications accordingly. Furthermore, it can analyze which devices the user has used for studying in the past and provide an interface optimized for those devices. By providing an interface tailored to the user's learning style, learning efficiency is improved.

[0057] The analysis unit can analyze the user's learning history and adjust the question format based on their learning progress. For example, it can analyze the accuracy rate of questions the user has answered in the past and prioritize questions from areas where the accuracy rate is low. It can also analyze the time it takes the user to answer questions in the past and focus on questions from areas where the user takes a long time to answer. Furthermore, it can analyze the difficulty level of questions the user has answered in the past and gradually increase the difficulty level of the questions. By adjusting the question format based on the user's learning history, efficient learning becomes possible.

[0058] The generation unit can generate questions based on the user's learning objectives. For example, it can generate questions that reinforce necessary skills based on the passing score of the user's target school. It can also set goals to be achieved within a learning period set by the user and generate questions tailored to those goals. Furthermore, it can generate questions that focus on specific subjects or fields set by the user. This allows for efficient learning by providing questions that align with the user's learning objectives.

[0059] The output unit can provide feedback based on the user's learning progress. For example, it can analyze the accuracy rate of the questions the user has answered and provide additional questions for areas where the accuracy rate is low. It can also analyze the time the user has spent answering questions and suggest more efficient answering methods for areas where the user has spent a long time. Furthermore, it can analyze the difficulty level of the questions the user has answered and provide explanations for difficult questions. By providing feedback based on the user's learning progress, the learning efficiency is improved.

[0060] The analysis unit can adjust the question format based on the user's learning environment. For example, the analysis unit uses sensors to detect the noise level in the user's learning location and avoids questions that require concentration if the noise level is high. It can also detect the lighting conditions during the time the user is learning and present questions that are less visually demanding if the lighting is dim. Furthermore, the analysis unit can detect the battery level of the device the user is using for learning and present questions that can be answered quickly if the battery is low. This allows for more efficient learning by providing question formats tailored to the user's learning environment.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk accepts input of the applicant's desired school, subject, and subject area. For example, it provides an interface for the user to input their desired school, subject, and subject area. Step 2: The analysis unit analyzes the question trends based on the information received by the reception unit. For example, it collects past exam data from the target school and uses an algorithm to analyze the question trends. Step 3: The generation unit generates similar problems based on the question trends analyzed by the analysis unit. For example, a generation AI is used to generate new problems based on the question trends of the target school. The generation AI learns the question trends of past exams and generates new problems based on those trends. Step 4: The output unit outputs the answers to the questions generated by the generation unit. For example, if a test-taker inputs an answer and declares a part they don't understand, the output unit generates and outputs the answer to that part.

[0063] (Example of form 2) The practice problem generation and scoring system according to an embodiment of the present invention is a system that generates practice problems tailored to the question trends of a target school and scores the answers submitted by examinees. This system accepts input of the target school, subject, and subject area, and the generating AI creates similar problems based on this information. For example, it analyzes the question trends of past exams from the target school and generates new problems based on those trends. This allows examinees to solve many problems similar to past exams, making it easier to grasp the question trends. Next, the examinee creates an answer to the problem. When the answer is completed, or when the examinee declares that they do not know the answer, the generating AI outputs the answer to the problem. For example, if the examinee enters an answer and declares that they do not know the answer, the generating AI generates and outputs the answer for that part. This system expands the amount of practice problems that can be done, shortens the time required to create practice problems, and reduces the workload in educational settings. Specifically, because the generating AI automatically generates problems, the time spent by cram school instructors creating problems is significantly reduced. Also, because the generating AI generates the answers, examinees can proceed with their studies efficiently. Furthermore, this system is expected to have future applications, such as being used for qualification examinations. For example, by analyzing the trends in questions asked in qualification exams and generating questions based on those trends, it can also be used for qualification exam preparation. In this way, a system for generating and grading practice problems tailored to the question trends of a target school is a groundbreaking system that improves the learning efficiency of test-takers and reduces the workload of educational institutions. Thus, a system for generating and grading practice problems can improve the learning efficiency of test-takers and reduce the workload of educational institutions.

[0064] The practice problem generation and scoring system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit accepts input of the desired school, subject, and subject area of ​​the exam. The reception unit provides, for example, an interface for the user to input the desired school, subject, and subject area of ​​the exam. The analysis unit analyzes the exam trends based on the information received by the reception unit. The analysis unit uses, for example, an algorithm to collect past exam data from the desired school and analyze the exam trends. The generation unit generates similar problems based on the exam trends analyzed by the analysis unit. The generation unit generates new problems based on the exam trends of the desired school, for example, using a generation AI. The generation AI learns the exam trends of past exams and generates new problems based on those trends. The output unit outputs answers to the problems generated by the generation unit. The output unit generates and outputs answers to parts that a test-taker does not know, for example, when the test-taker inputs an answer and declares parts they do not know. As a result, the practice problem generation and scoring system according to this embodiment can generate and score practice problems that are tailored to the question trends of the applicant's desired school.

[0065] The reception department accepts input of desired schools, subjects, and subject areas. For example, the reception department provides an interface for users to input their desired schools, subjects, and subject areas. Specifically, users can input information about their desired schools, subjects, and subject areas through a web form or mobile application designed for ease of use. This interface allows users to review the information they enter in real time and make corrections or additions as needed. The interface also has a function to save information previously entered by users for reuse on subsequent visits. Furthermore, the reception department manages a database to automatically categorize the information entered by users and appropriately hand it over to the analysis and generation departments. This database is designed to efficiently store user input information and allow for quick access when needed. For example, if a user selects a specific university as their desired school, information regarding that university's past exam data and question trends is automatically associated and sent to the analysis department. This allows the reception department to quickly and accurately collect the information users need, improving the overall efficiency of the system.

[0066] The analysis department analyzes question trends based on information received by the reception department. For example, the analysis department collects past exam data from the target school and uses algorithms to analyze question trends. Specifically, it stores exam questions from the past few years in a database and analyzes in detail the frequency, difficulty, and format of each question. The analysis algorithm uses natural language processing technology to analyze the question text and extract the themes and keywords that will be asked. It also uses statistical methods to calculate how often specific themes and formats will be asked, and quantifies the question trends. Furthermore, the analysis department can use AI to learn from past exam data and predict future question trends. For example, it uses machine learning models to predict future question trends from past question patterns and provide users with more accurate information. Based on these analysis results, the analysis department can provide specific advice to users on what preparations they should make for their target school's exam. In this way, the analysis department can provide users with important information to help them study efficiently and maximize the effectiveness of the entire system.

[0067] The generation unit generates similar problems based on the question trends analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate new problems based on the question trends of the target school. For example, the generation AI learns the question trends of past exams and generates new problems based on those trends. Specifically, the generation AI uses natural language generation technology to create new problem statements while mimicking the writing style and format of past exams. The generation AI can generate related problems based on keywords and themes extracted from the past exam database. For example, for mathematics problems, it can generate problems based on specific formulas and theorems, and for English problems, it can generate problems based on specific grammar and vocabulary. The generation AI also has a function to adjust the difficulty level of the problems, so that it can provide problems of appropriate difficulty according to the user's learning progress and level of understanding. Furthermore, the generation unit simultaneously generates example answers and explanations for the generated problems, so that users can self-grade and review after solving the problems. In this way, the generation unit can support users in conducting effective learning tailored to the question trends of their target school and improve the overall learning effect of the system.

[0068] The output unit outputs the answers to the problems generated by the generation unit. For example, if a test-taker inputs an answer and declares parts they don't understand, the output unit will generate and output the answer to those parts. Specifically, it provides an interface for the user to input an answer, and once an answer is entered, the system automatically analyzes the answer and determines whether it is correct or incorrect. If the user declares parts they don't understand, the output unit uses the generation AI to generate the answer to those parts and outputs it along with a detailed explanation. The generation AI uses natural language processing technology to understand the content of the problem and generate an appropriate answer. For example, for a math problem, it will explain the steps to solve the problem and how to apply the formula in detail, and for an English problem, it will provide explanations of grammar and vocabulary. The output unit also has a function to display the answer and explanation in a visually easy-to-understand way so that the user can easily check the answer. For example, it can highlight important points and supplement the explanation with diagrams and tables. Furthermore, the output unit also provides a function to save the user's answer history so that it can be reviewed later. In this way, the output unit can deepen the user's understanding during the problem-solving process and support effective learning.

[0069] The reception desk can receive information from test-takers indicating parts they do not understand. For example, the reception desk can provide a function for test-takers to mark parts they do not understand when entering their answers. The reception desk can also accept text input of parts they do not understand. Furthermore, the reception desk can accept voice input of parts they do not understand. This allows test-takers to receive appropriate feedback by reporting parts they do not understand. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when a test-taker enters parts they do not understand, the reception desk can use AI to automatically detect those parts and provide feedback.

[0070] The generation unit can analyze the question trends of past exams from the target school and generate new questions based on those trends. For example, the generation unit collects past exam data from the target school and analyzes the question trends using a generation AI. The generation AI learns the question trends of past exams and generates new questions based on those trends. The generation unit, for example, has the generation AI analyze the question trends of past exams and generates new questions based on those trends. The generation unit, for example, has the generation AI analyze the question trends of past exams and generates new questions based on those trends. This improves the learning effectiveness of test-takers by generating questions based on the question trends of the target school. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs past exam data from the target school into the generation AI, and the generation AI generates new questions.

[0071] The output unit can generate and output answers to parts that the test-taker does not understand when they input their answers and declare the parts they do not understand. The output unit can, for example, provide a function to mark parts that the test-taker does not understand when they input their answers. The output unit can also accept input of parts that the test-taker does not understand as text. Furthermore, the output unit can accept input of parts that the test-taker does not understand as voice. For example, when the test-taker inputs parts they do not understand, the output unit generates and outputs the answer to that part. The output unit can, for example, use a generation AI to generate and output the answer to the part that the test-taker does not understand. This improves the efficiency of learning by allowing the test-taker to obtain answers to parts they do not understand. Some or all of the above processing in the output unit may be performed using AI or not. For example, when the test-taker inputs parts they do not understand, the output unit can automatically generate and output the answer using AI.

[0072] The analysis unit can analyze the trends in questions asked in qualification exams and generate questions based on those trends. For example, the analysis unit collects past exam data for qualification exams and uses an algorithm to analyze the trends in questions. The analysis unit collects past exam data for qualification exams and uses an algorithm to analyze the trends in questions. The analysis unit collects past exam data for qualification exams and uses an algorithm to analyze the trends in questions. By generating questions based on the trends in questions asked in qualification exams, it can also be used for qualification exam preparation. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs past exam data for qualification exams into an AI, the AI ​​analyzes the trends in questions, and generates new questions based on those trends.

[0073] The generation unit can generate questions based on the question trends of qualification exams. For example, the generation unit collects past exam question data and analyzes the question trends using a generation AI. The generation AI learns the question trends of past questions and generates new questions based on those trends. For example, the generation unit has the generation AI analyze the question trends of past qualification exams and generate new questions based on those trends. For example, the generation unit has the generation AI analyze the question trends of past qualification exams and generate new questions based on those trends. This allows the system to generate questions based on the question trends of qualification exams, making it useful for qualification exam preparation. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs past exam question data into the generation AI, and the generation AI generates new questions.

[0074] The reception desk can estimate the user's emotions and adjust the timing of inputting desired schools and subjects based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on changes in facial expressions and adjust the input timing. The reception desk can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the voice, calculate an emotion score, and adjust the input timing. The reception desk can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on fluctuations in heart rate and adjust the input timing. By adjusting the input timing according to the user's emotions, stress can be reduced and input can be made more efficient. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI or not. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0075] The reception desk can analyze past input history and suggest the optimal input method. For example, the reception desk can collect and analyze data on the desired schools and subjects that the user has entered in the past. For example, the reception desk can automatically display as suggestions the desired schools and subjects that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest desired schools and subjects to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input past input history data into AI, and the AI ​​can suggest the optimal input method.

[0076] The reception unit can filter input content based on the user's learning progress. For example, the reception unit collects and analyzes test results and study time data to evaluate the user's learning progress. For example, the reception unit can prioritize inputting unstudied areas based on the user's learning progress. The reception unit can also exclude areas that have already been studied based on the user's learning progress. Furthermore, the reception unit can highlight areas that the user finds particularly difficult based on the user's learning progress. This allows for efficient learning by filtering input content based on the user's learning progress. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs learning progress data into the AI, and the AI ​​filters the input content to the optimal level.

[0077] The reception unit can estimate the user's emotions and determine the priority of input content based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of input content. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of input content. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of input content. This enables efficient input by determining the priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI or not. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0078] The reception system can prioritize inputting highly relevant information based on the user's geographical location during data entry. For example, the reception system can acquire the user's geographical location and prioritize displaying highly relevant information. For example, the reception system can prioritize displaying nearby schools of interest based on the user's geographical location. The reception system can also prioritize displaying region-specific subjects or fields of study based on the user's geographical location. Furthermore, the reception system can prioritize displaying information on local educational institutions based on the user's geographical location. This enables efficient data entry by prioritizing the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system inputs geographical location data into the AI, and the AI ​​prioritizes displaying highly relevant information.

[0079] The reception desk can analyze the user's social media activity and input relevant information during the input process. For example, the reception desk can collect and analyze the user's social media activity data. For example, the reception desk can prioritize displaying subjects or fields of interest based on the user's social media activity. It can also prioritize displaying information related to the user's desired school based on the user's social media activity. Furthermore, the reception desk can prioritize displaying resources that are useful for learning based on the user's social media activity. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input social media activity data into AI, and the AI ​​can prioritize displaying relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method for question trends based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the analysis method. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the analysis method. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the analysis method. This allows for efficient analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0081] The analysis unit can predict current trends by referring to past question trend data during analysis. For example, the analysis unit collects and analyzes past question trend data. For example, the analysis unit predicts current question trends based on past question trend data. The analysis unit can also predict future question trends based on past question trend data. Furthermore, the analysis unit can predict question trends for specific subjects or fields based on past question trend data. This allows for the prediction of current trends by referring to past question trend data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs past question trend data into AI, and the AI ​​predicts current question trends.

[0082] The analysis unit can apply different analysis algorithms to each subject during the analysis. For example, the analysis unit may apply a specific algorithm to analyze the trends in mathematics questions. For example, the analysis unit may apply a different algorithm to analyze the trends in English questions. Furthermore, the analysis unit may apply yet another algorithm to analyze the trends in science questions. This allows for efficient analysis by applying different analysis algorithms to each subject. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs subject-specific question trend data into the AI, and the AI ​​applies a different analysis algorithm for each subject.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the display method. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the display method. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the display method. This allows for efficient display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0084] The analysis unit can perform analysis based on the geographical distribution of target schools. For example, the analysis unit can collect and analyze geographical distribution data of target schools. For example, the analysis unit can analyze question trends based on the geographical distribution of target schools. The analysis unit can also analyze question trends in specific regions based on the geographical distribution of target schools. Furthermore, the analysis unit can compare question trends for each region based on the geographical distribution of target schools. This makes efficient analysis possible by performing analysis based on the geographical distribution of target schools. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data into AI, and the AI ​​can analyze question trends.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can collect and analyze relevant literature. For example, the analysis unit can improve the accuracy of its analysis of question trends by referring to relevant literature. The analysis unit can also improve the accuracy of its analysis of specific subjects or fields by referring to relevant literature. Furthermore, the analysis unit can improve the accuracy of its prediction of future question trends by referring to relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs relevant literature data into the AI, and the AI ​​improves the accuracy of the analysis.

[0086] The generation unit can estimate the user's emotions and adjust the problem generation method based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions and adjust the problem generation method. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the problem generation method. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the problem generation method. This allows for efficient problem generation by adjusting the problem generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0087] The generation unit can generate new problems by referring to past problem data during the generation process. For example, the generation unit collects and analyzes past problem data. For example, the generation unit generates new problems based on past problem data. The generation unit can also generate similar problems based on past problem data. Furthermore, the generation unit can generate problems of different formats based on past problem data. This allows for the efficient generation of new problems by referring to past problem data. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs past problem data into a generation AI, and the generation AI generates new problems.

[0088] The generation unit can apply different generation algorithms to each subject during generation. For example, the generation unit applies a specific algorithm to generate mathematics problems. For example, it applies a different algorithm to generate English problems. Furthermore, the generation unit can apply yet another algorithm to generate science problems. This allows for efficient problem generation by applying different generation algorithms to each subject. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs subject-specific problem data into the generation AI, and the generation AI applies a different generation algorithm for each subject.

[0089] The generation unit can estimate the user's emotions and determine the priority of the problems to be generated based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions and determine the priority of the problems. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of the problems. Furthermore, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of the problems. This enables efficient problem generation by determining the priority of problems according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0090] The generation unit can generate questions based on the geographical distribution of the target schools during the generation process. For example, the generation unit collects and analyzes geographical distribution data of the target schools. The generation unit generates questions based on the geographical distribution of the target schools. The generation unit can also generate questions for specific regions based on the geographical distribution of the target schools. Furthermore, the generation unit can compare questions from different regions based on the geographical distribution of the target schools. This enables efficient question generation by generating questions based on the geographical distribution of the target schools. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs geographical distribution data into a generation AI, and the generation AI generates questions.

[0091] The generation unit can improve the accuracy of its generation by referring to relevant literature during the generation process. For example, the generation unit can collect and analyze relevant literature. The generation unit can improve the accuracy of problem generation by referring to relevant literature. It can also improve the accuracy of problem generation for specific subjects or fields by referring to relevant literature. Furthermore, the generation unit can improve the accuracy of predicting future problem generation by referring to relevant literature. As a result, the accuracy of generation is improved by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs relevant literature data into the generation AI, and the generation AI improves the accuracy of the generation.

[0092] The output unit can estimate the user's emotions and adjust the display method of the answer based on the estimated user emotions. For example, the output unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the output unit can calculate an emotion score based on changes in facial expressions and adjust the display method. The output unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the output unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the display method. The output unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the output unit can calculate an emotion score based on fluctuations in heart rate and adjust the display method. This allows for efficient display by adjusting the display method of the answer according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the output unit may be performed using AI or not. For example, the output unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0093] The output unit can generate the optimal answer by referring to past answer data during output. For example, the output unit collects and analyzes past answer data. For example, the output unit generates the optimal answer based on past answer data. The output unit can also generate similar answers based on past answer data. Furthermore, the output unit can generate answers in different formats based on past answer data. This allows for the efficient generation of the optimal answer by referring to past answer data. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit inputs past answer data into AI, and the AI ​​generates the optimal answer.

[0094] The output unit can apply different output algorithms to each subject during output. For example, the output unit might apply a specific algorithm to output the answer to a mathematics question. For example, it might apply a different algorithm to output the answer to an English question. Furthermore, the output unit could apply yet another algorithm to output the answer to a science question. This allows for efficient answer generation by applying different output algorithms to each subject. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit inputs answer data for each subject into the AI, and the AI ​​applies a different output algorithm for each subject.

[0095] The output unit can estimate the user's emotions and determine the priority of answers based on the estimated emotions. For example, the output unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the output unit can calculate an emotion score based on changes in facial expressions and determine the priority of answers. The output unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the output unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of answers. The output unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the output unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of answers. This enables efficient answer generation by determining the priority of answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples. Some or all of the processing described above in the output unit may be performed using AI or not. For example, the output unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0096] The output unit can generate answers based on the geographical distribution of the target schools at the time of output. For example, the output unit collects and analyzes geographical distribution data of the target schools. The output unit generates answers based on the geographical distribution of the target schools. The output unit can also generate answers for a specific region based on the geographical distribution of the target schools. Furthermore, the output unit can compare answers for each region based on the geographical distribution of the target schools. This enables efficient answer generation by generating answers based on the geographical distribution of the target schools. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit inputs geographical distribution data into AI, and the AI ​​generates answers.

[0097] The output unit can improve the accuracy of the answers by referring to relevant literature during output. For example, the output unit collects and analyzes relevant literature. The output unit improves the accuracy of the answers by referring to relevant literature. The output unit can also improve the accuracy of answers for specific subjects or fields by referring to relevant literature. Furthermore, the output unit can improve the accuracy of future answer predictions by referring to relevant literature. As a result, the accuracy of the answers is improved by referring to relevant literature. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit inputs relevant literature data into the AI, and the AI ​​improves the accuracy of the answers.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's learning style and customize the interface based on that estimate. For example, it can analyze what types of problems the user has preferred to solve in the past and prioritize displaying problems of that type. It can also analyze when the user has studied in the past and send notifications accordingly. Furthermore, it can analyze which devices the user has used for studying in the past and provide an interface optimized for those devices. By providing an interface tailored to the user's learning style, learning efficiency is improved.

[0100] The analysis unit can analyze the user's learning history and adjust the question format based on their learning progress. For example, it can analyze the accuracy rate of questions the user has answered in the past and prioritize questions from areas where the accuracy rate is low. It can also analyze the time it takes the user to answer questions in the past and focus on questions from areas where the user takes a long time to answer. Furthermore, it can analyze the difficulty level of questions the user has answered in the past and gradually increase the difficulty level of the questions. By adjusting the question format based on the user's learning history, efficient learning becomes possible.

[0101] The generation unit can generate questions based on the user's learning objectives. For example, it can generate questions that reinforce necessary skills based on the passing score of the user's target school. It can also set goals to be achieved within a learning period set by the user and generate questions tailored to those goals. Furthermore, it can generate questions that focus on specific subjects or fields set by the user. This allows for efficient learning by providing questions that align with the user's learning objectives.

[0102] The output unit can provide feedback based on the user's learning progress. For example, it can analyze the accuracy rate of the questions the user has answered and provide additional questions for areas where the accuracy rate is low. It can also analyze the time the user has spent answering questions and suggest more efficient answering methods for areas where the user has spent a long time. Furthermore, it can analyze the difficulty level of the questions the user has answered and provide explanations for difficult questions. By providing feedback based on the user's learning progress, the learning efficiency is improved.

[0103] The analysis unit can adjust the question format based on the user's learning environment. For example, the analysis unit uses sensors to detect the noise level in the user's learning location and avoids questions that require concentration if the noise level is high. It can also detect the lighting conditions during the time the user is learning and present questions that are less visually demanding if the lighting is dim. Furthermore, the analysis unit can detect the battery level of the device the user is using for learning and present questions that can be answered quickly if the battery is low. This allows for more efficient learning by providing question formats tailored to the user's learning environment.

[0104] The reception desk can estimate the user's emotions and display messages to improve learning motivation based on those estimates. For example, if the reception desk estimates that the user is tired, it will display an encouraging message. It can also display advice to maintain concentration if it estimates that the user is focused. Furthermore, if it estimates that the user is feeling anxious, it can suggest ways to relax. This allows for improved learning motivation by providing messages tailored to the user's emotions.

[0105] The analysis unit can estimate the user's emotions and adjust the question format based on those estimates. For example, if the analysis unit estimates that the user is stressed, it will prioritize presenting easier questions. Conversely, if the analysis unit estimates that the user is relaxed, it can present more difficult questions. Furthermore, if the analysis unit estimates that the user is focused, it can present questions consecutively. This allows for more efficient learning by providing question formats tailored to the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the question format based on those estimates. For example, if the generation unit estimates that the user is tired, it can generate short questions or multiple-choice questions. It can also generate longer questions or essay questions if it estimates that the user is focused. Furthermore, if it estimates that the user is anxious, it can generate questions designed to promote relaxation. This allows for more efficient learning by providing question formats tailored to the user's emotions.

[0107] The output unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the output unit estimates that the user is stressed, it will prioritize displaying positive feedback. If the output unit estimates that the user is relaxed, it can also provide detailed explanations. Furthermore, if the output unit estimates that the user is focused, it can guide the user to the next question. This improves learning efficiency by providing feedback tailored to the user's emotions.

[0108] The output unit can estimate the user's emotions and adjust the display method of the answers based on the estimated emotions. For example, if the output unit estimates that the user is tired, it will display the answers concisely. Conversely, if the output unit estimates that the user is focused, it can display detailed answers. Furthermore, if the output unit estimates that the user is feeling anxious, it can adopt a display method that promotes relaxation. This allows for more efficient learning by providing answer displays tailored to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk accepts input of the applicant's desired school, subject, and subject area. For example, it provides an interface for the user to input their desired school, subject, and subject area. Step 2: The analysis unit analyzes the question trends based on the information received by the reception unit. For example, it collects past exam data from the target school and uses an algorithm to analyze the question trends. Step 3: The generation unit generates similar problems based on the question trends analyzed by the analysis unit. For example, a generation AI is used to generate new problems based on the question trends of the target school. The generation AI learns the question trends of past exams and generates new problems based on those trends. Step 4: The output unit outputs the answers to the questions generated by the generation unit. For example, if a test-taker inputs an answer and declares a part they don't understand, the output unit generates and outputs the answer to that part.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input their desired school, subject, and subject area. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past exam data from the desired school and analyzes the exam trends. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates new questions using a generation AI. The output unit is implemented by the output device 40 of the smart device 14 and outputs answers to the generated questions. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input their desired school, subject, and subject area by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects past exam data from the desired school and analyzes the exam trends. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates new questions using a generation AI. The output unit is implemented, for example, by the speaker 240 of the smart glasses 214 and outputs the answer to the generated question by voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to voice input their desired school, subject, and subject area. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects past exam data from the desired school and analyzes the exam trends. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates new questions using a generation AI. The output unit is implemented by, for example, the display 343 of the headset terminal 314 and displays the answers to the generated questions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input their desired school, subject, and subject area by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects past exam data from the desired school and analyzes the trends in exam questions. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates new questions using a generation AI. The output unit is implemented, for example, by the speaker 240 of the robot 414 and outputs the answer to the generated question by voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The reception desk accepts inputs regarding the desired school, subjects, and subject areas covered in the exam, An analysis unit analyzes the question trends based on the information received by the aforementioned reception unit, A generation unit that generates similar problems based on the question trends analyzed by the aforementioned analysis unit, The system comprises an output unit that outputs an answer to a problem generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept information from test-takers regarding the parts they do not understand. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is We analyze the question trends in past exams from your target school and generate new questions based on those trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, When a test-taker enters their answers and reports parts they don't understand, the system generates and outputs the answers for those parts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze the trends in questions asked in certification exams and generate questions based on those trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate questions based on the trends in questions asked in certification exams. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting desired schools and subjects based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When inputting data, the input content is filtered based on the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input data, the system prioritizes inputting information that is more relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During input, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method for question trends based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, past exam trend data is referenced to predict current trends. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each subject. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the analysis will be performed based on the geographical distribution of the students' preferred schools. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust the problem generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating new problems, past problem data is referenced to create new problems. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, a different generation algorithm is applied for each subject. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the problems to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, questions are generated based on the geographical distribution of the target schools. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, we refer to relevant literature to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, The system estimates the user's emotions and adjusts how the answers are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, When outputting, the system generates the optimal answer by referring to past answer data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, When outputting, a different output algorithm is applied for each subject. The system described in Appendix 1, characterized by the features described herein. (Note 28) The output unit is, The system estimates the user's emotions and prioritizes the answers based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The output unit is, When outputting, the system generates answers based on the geographical distribution of the target schools. The system described in Appendix 1, characterized by the features described herein. (Note 30) The output unit is, When outputting, refer to relevant literature to improve the accuracy of the answer. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts inputs regarding the desired school, subjects, and subject areas covered in the exam, An analysis unit analyzes the question trends based on the information received by the aforementioned reception unit, A generation unit that generates similar problems based on the question trends analyzed by the aforementioned analysis unit, The system comprises an output unit that outputs an answer to a problem generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept information from test-takers regarding the parts they do not understand. The system according to feature 1.

3. The generating unit is We analyze the question trends in past exams from your target school and generate new questions based on those trends. The system according to feature 1.

4. The output unit is, When a test-taker enters their answers and reports parts they don't understand, the system generates and outputs the answers for those parts. The system according to feature 1.

5. The aforementioned analysis unit, We analyze the trends in questions asked in certification exams and generate questions based on those trends. The system according to feature 1.

6. The generating unit is Generate questions based on the trends in questions asked in certification exams. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting desired schools and subjects based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze past input history and suggest the optimal input method. The system according to feature 1.

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