System

Abstract

An object of a system according to an embodiment is to provide an optimal mock examination question based on a learning progress and an understanding level of a user.SOLUTION: A system according to an embodiment includes a learning-progress analysis unit, a problem generation unit, and a test execution unit. The learning-progress analysis unit analyzes the learning progress and the degree of understanding of the user. A question generation part generates an optimum simulation test question on the basis of the learning progress and the comprehension degree of the user analyzed by the learning progress analysis part. The test execution unit provides the user with the mock test questions generated by the question generation unit and executes the mock test.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide optimal mock test questions based on a user's learning progress and level of understanding, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal mock test questions based on the user's learning progress and level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning progress analysis unit, a question generation unit, and a test implementation unit. The learning progress analysis unit analyzes a user's learning progress and level of understanding. The question generation unit generates optimal mock test questions based on the user's learning progress and level of understanding analyzed by the learning progress analysis unit. The test implementation unit provides the user with the mock test questions generated by the question generation unit and implements the mock test. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal mock test questions based on the user's learning progress and level of understanding. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The automatic mock test creation system according to the embodiment of the present invention is a system in which a generation AI automatically creates optimal mock test questions according to the user's learning progress and level of understanding. As a result, the automatic mock test creation system functions like a personal tutor for the user, and can efficiently and effectively support qualification study.

[0029] An automatic mock test creation system according to an embodiment includes a learning progress analysis unit, a question generation unit, and a test implementation unit. The learning progress analysis unit analyzes a user's learning progress and level of understanding. For example, data such as the user's correct answer rate, answer time, and learning history is collected, and the generation AI analyzes the data. The learning progress analysis unit also identifies areas and topics in which the user has strengths and weaknesses. For example, the generation AI performs analysis based on prompts including the user's learning history and answer data. The question generation unit generates optimal mock test questions based on the user's learning progress and level of understanding analyzed by the learning progress analysis unit. For example, the question generation unit generates questions that focus on areas in which the user is weak or questions similar to questions that the user got wrong in the past. The question generation unit also generates questions based on prompts that reflect the user's learning progress and level of understanding. The test implementation unit provides the user with the mock test questions generated by the question generation unit and conducts the mock test. For example, the generation AI provides the generated mock test questions to the user, and after the user answers the mock test, analyzes the answers and provides feedback. This allows the automatic mock test creation system according to the embodiment to provide optimal mock test questions based on the user's learning progress and level of understanding, supporting efficient learning. For example, by focusing on areas in which the user is weak, the user's ability to score well on the actual test can be improved. Furthermore, the feedback provided by the generation AI and the optimization of the learning plan allow the user to understand their own learning progress and develop an appropriate learning strategy.

[0030] The learning progress analysis unit can also analyze the content of related materials or reference books viewed by the user to evaluate the depth of the user's learning. The learning progress analysis unit, for example, collects data on reference books and materials viewed by the user, and the generation AI analyzes the content. For example, it analyzes which chapters and pages the user frequently views to evaluate the depth of learning. The learning progress analysis unit also uses text analysis technology to analyze the content of related materials and reference books. For example, the generation AI extracts keywords and phrases from the materials viewed by the user to evaluate the depth of learning. This allows for a more accurate evaluation of the depth of learning by analyzing the content of the materials viewed by the user.

[0031] The learning progress analysis unit can compare the user's learning data with that of other users to evaluate the user's relative progress. For example, the learning progress analysis unit collects learning data from other users and the generation AI evaluates the user's relative progress based on that data. For example, it evaluates the degree of progress compared to other users taking the same exam. The learning progress analysis unit also uses statistical analysis techniques to evaluate the user's relative progress. For example, the generation AI statistically analyzes the learning data of other users to evaluate the user's progress. This makes it possible to evaluate the user's relative progress by comparing it with the learning data of other users.

[0032] The learning progress analysis unit can provide the data in a visually easy-to-understand dashboard format, allowing the user to intuitively grasp their own progress. The learning progress analysis unit, for example, develops a dashboard that visually displays the user's learning progress. For example, the progress status is displayed using graphs and charts. The learning progress analysis unit also uses data visualization technology to display the progress in dashboard format. For example, the generation AI converts the user's learning data into graphs and charts and displays them visually. This allows the user to grasp their progress in a visually easy-to-understand dashboard format.

[0033] The question generation unit can analyze trends in past exam questions and predict and generate questions that are likely to appear in future exams. The question generation unit, for example, collects past exam question data, and the generation AI analyzes that data. For example, it identifies frequently appearing topics and question formats. The question generation unit also uses a predictive algorithm to predict questions that are likely to appear in future exams. For example, the generation AI generates questions that are likely to appear in future exams based on trends in past exam questions. This allows the system to effectively support users in preparing for exams by analyzing trends in past exam questions and predicting and generating questions that are likely to appear in future exams.

[0034] The question generation unit can generate questions in the optimal format according to the user's learning style. For example, the question generation unit analyzes the user's learning style (visual, auditory, experiential) and generates a question format according to that. For example, for a visual user, questions that make extensive use of diagrams and charts are generated. The question generation unit also uses learning style analysis technology to generate questions according to the user's learning style. For example, the generation AI analyzes the user's learning style and generates questions in the optimal format based on that. This can improve learning effectiveness by generating questions in the optimal format according to the user's learning style.

[0035] The question generation unit can combine questions from different qualification exams to generate mock test questions for multiple qualifications. For example, the question generation unit collects question data from different qualification exams, and the generation AI generates mock test questions for multiple qualifications based on that data. For example, questions that include common topics are generated. The question generation unit also uses question data integration technology to combine questions from different qualification exams. For example, the generation AI integrates question data from different qualification exams to generate mock test questions for multiple qualifications. This makes it possible to generate mock test questions for multiple qualifications by combining questions from different qualification exams.

[0036] The problem generator generates problem sets with gradually adjusted difficulty levels, thereby encouraging the user's growth. The problem generator generates problem sets with gradually adjusted difficulty levels, for example, according to the user's learning progress. For example, beginner, intermediate, and advanced problems are provided in sequence. The problem generator also uses a difficulty adjustment algorithm to gradually adjust the difficulty levels. For example, the generation AI analyzes the user's learning progress and generates problem sets with gradually adjusted difficulty levels based on that analysis. This allows the user's growth to be encouraged by generating problem sets with gradually adjusted difficulty levels.

[0037] The test implementation unit can analyze the user's answering speed and answer sequence in real time and provide feedback on improvements to the answering strategy. For example, the test implementation unit records the speed and sequence in which the user answers a mock test in real time, and the generation AI analyzes the data. For example, it identifies questions that take a long time to answer. The test implementation unit also uses answer data analysis technology to provide feedback on improvements to the answering strategy. For example, the generation AI analyzes the user's answering speed and answer sequence and proposes an efficient answering strategy. In this way, the user's answering efficiency can be improved by analyzing the answering speed and answer sequence in real time and providing feedback on improvements to the answering strategy.

[0038] The test implementation unit can evaluate the user's confidence in their answers and provide focused feedback for questions with low confidence. For example, the test implementation unit may have the user input their confidence level after answering a mock test to evaluate their confidence level. For example, the test implementation unit may have the user rate their confidence level for each question on a scale of 1 to 5. The test implementation unit may also use self-reports and behavioral data to provide focused feedback for questions with low confidence. For example, the generation AI may analyze the user's confidence data and suggest specific improvements for questions with low confidence. This allows the user's confidence level to be evaluated and focused feedback to be provided for questions with low confidence, thereby improving the user's understanding.

[0039] The test implementation unit can provide a relative evaluation of the user by comparing the results with those of other users. For example, the test implementation unit collects the mock test results of other users, and the generation AI provides a relative evaluation based on that data. For example, it evaluates the user's performance compared to other users taking the same test. The test implementation unit also uses statistical analysis technology to provide the relative evaluation. For example, the generation AI statistically analyzes the mock test results of other users and evaluates the user's performance. This allows a comparison with the results of other users to provide a relative evaluation and identify the user's learning strengths and weaknesses.

[0040] The test implementation department can provide feedback in video format, conveying it to the user in a visually easy-to-understand format. For example, the test implementation department develops a system that provides feedback on mock tests in video format. For example, it uses video to explain answers and areas for improvement. The test implementation department also uses video production technology to provide feedback in a visually easy-to-understand format. For example, the generative AI creates explanatory videos and animations based on the user's answer data. In this way, by providing feedback in video format, it can convey it to the user in a visually easy-to-understand format.

[0041] The study plan optimization unit can suggest optimal study times and environments based on the user's study history and mock test results. For example, the study plan optimization unit analyzes the user's study history and mock test results to suggest optimal study times. For example, it identifies the time of day when the user can concentrate best. The study plan optimization unit also uses environmental analysis technology to suggest optimal study environments. For example, the generation AI suggests study environments such as quiet places and appropriate lighting based on the user's study history and mock test results. This makes it possible to improve study efficiency by suggesting optimal study times and environments based on the user's study history and mock test results.

[0042] The study plan optimization unit can propose a reasonable study plan by taking into account the user's lifestyle and schedule. For example, the study plan optimization unit collects the user's lifestyle and schedule, and the generation AI proposes a reasonable study plan based on that data. For example, it sets study times that fit with work and home schedules. The study plan optimization unit also uses lifestyle analysis technology to consider the user's lifestyle. For example, the generation AI analyzes the user's sleep time and daily activity patterns to propose an optimal study plan. This makes it possible to increase the continuity of learning by proposing a reasonable study plan that takes into account the user's lifestyle and schedule.

[0043] The study plan optimization unit can compare the success stories of other users and derive the optimal plan. For example, the study plan optimization unit collects the study plans and success stories of other users, and the generation AI derives the optimal plan based on that data. For example, it refers to the success stories of other users taking the same exam. The study plan optimization unit also uses data analysis technology to compare success stories. For example, the generation AI analyzes the success stories of other users and identifies similarities and differences. This allows it to derive the optimal study plan by comparing it with the success stories of other users.

[0044] The study plan optimization unit can provide the data in a visually easy-to-understand Gantt chart format, allowing the user to intuitively grasp their progress. The study plan optimization unit, for example, develops a Gantt chart that visually displays the user's study plan. For example, it displays the study progress in a bar format. The study plan optimization unit also uses data visualization technology to display the progress in Gantt chart format. For example, the generation AI converts the user's study data into a Gantt chart and displays it visually. This allows the user to grasp their progress in a visually easy-to-understand Gantt chart format.

[0045] The study plan optimization unit can identify the time periods when the user can concentrate best and propose a plan for focused study during those time periods. The study plan optimization unit, for example, analyzes the user's study history and mock test results to identify the time periods when the user can concentrate best. For example, it identifies the time periods when the user has the highest rate of correct answers. The study plan optimization unit also uses behavioral data and psychological indicators to identify the time periods when the user can concentrate best. For example, the generation AI identifies the time periods when the user is most able to concentrate best based on the user's study history and mock test results. This allows the system to identify the time periods when the user can concentrate best and propose a plan for focused study during those time periods, thereby improving study efficiency.

[0046] The continuous learning support unit can periodically evaluate the user's learning progress and adjust the user's learning plan as necessary. For example, the continuous learning support unit periodically evaluates the user's learning history and mock test results, and the generation AI adjusts the learning plan based on that data. For example, the unit evaluates progress every week and updates the learning plan. The continuous learning support unit also uses schedule management technology for periodic evaluation. For example, the generation AI manages the user's learning schedule and periodically evaluates progress. This allows the user's learning effectiveness to be maximized by periodically evaluating learning progress and adjusting the learning plan as necessary.

[0047] The continuous learning support unit can provide a function that promotes learning competition and cooperation with other users. The continuous learning support unit, for example, provides a ranking function to promote learning competition with other users. For example, a ranking may be created based on mock test scores, and users may compete against each other. The continuous learning support unit also provides a group learning function to promote cooperation with other users. For example, the generative AI provides a platform where users can study together. This can increase users' motivation to learn by providing a function that promotes learning competition and cooperation with other users.

[0048] The continuous learning support unit can customize and provide a personalized learning experience according to the user's interests and concerns. The continuous learning support unit, for example, analyzes the user's interests and concerns and provides a personalized learning experience accordingly. For example, it provides questions related to topics that interest the user. The continuous learning support unit also uses personalization technology to provide a personalized learning experience. For example, the generative AI analyzes the user's interests and concerns and customizes learning content based on them. This allows for a personalized learning experience that is customized according to the user's interests and concerns, thereby improving the effectiveness of learning.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The learning progress analysis unit can analyze the user's posture and movements while studying and evaluate fluctuations in concentration. For example, if the user maintains the same posture for a long period of time, it can estimate that the user's concentration is declining. The learning progress analysis unit also uses posture recognition technology and motion analysis technology to analyze the user's posture and movements. For example, the generation AI can analyze changes in the user's posture and movements to evaluate fluctuations in concentration. This allows for a more accurate evaluation of fluctuations in concentration by analyzing the user's posture and movements.

[0051] The learning progress analysis unit can also analyze the content of related materials or reference books viewed by the user to evaluate the depth of the user's learning. For example, it can analyze which chapters or pages the user frequently views to evaluate the depth of learning. The learning progress analysis unit also uses text analysis technology to analyze the content of related materials and reference books. For example, the generation AI can extract keywords and phrases from the materials viewed by the user to evaluate the depth of learning. This allows for a more accurate evaluation of the depth of learning by analyzing the content of the materials viewed by the user.

[0052] The learning progress analysis unit can compare the user's learning data with that of other users to evaluate the user's relative progress. For example, the generation AI collects learning data from other users and evaluates the user's relative progress based on that data. For example, it evaluates the degree of progress compared to other users taking the same exam. The learning progress analysis unit also uses statistical analysis techniques to evaluate the user's relative progress. For example, the generation AI statistically analyzes the learning data of other users to evaluate the user's progress. This makes it possible to evaluate the user's relative progress by comparing it with the learning data of other users.

[0053] The learning progress analysis unit can provide the data in a visually easy-to-understand dashboard format, allowing the user to intuitively grasp their progress. For example, a dashboard that visually displays the user's learning progress can be developed. For example, progress can be displayed using graphs and charts. The learning progress analysis unit also uses data visualization technology to display progress in dashboard format. For example, the generation AI converts the user's learning data into graphs and charts and displays them visually. This allows the user to grasp progress in a visually easy-to-understand dashboard format.

[0054] The question generation unit can analyze trends in past exam questions and predict and generate questions that are likely to appear in future exams. For example, past exam question data is collected and the generation AI analyzes that data. For example, frequently appearing topics and question formats are identified. The question generation unit also uses a predictive algorithm to predict questions that are likely to appear in future exams. For example, the generation AI generates questions that are likely to appear in future exams based on trends in past exam questions. This allows the system to analyze trends in past exam questions and predict and generate questions that are likely to appear in future exams, thereby effectively supporting users' exam preparation.

[0055] The question generation unit can generate questions in the optimal format according to the user's learning style. For example, it can analyze the user's learning style (visual, auditory, experiential) and generate a question format that suits it. For example, it can generate questions that make extensive use of diagrams for visual users. The question generation unit also uses learning style analysis technology to generate questions that suit the user's learning style. For example, the generation AI can analyze the user's learning style and generate questions in the optimal format based on that. This can improve learning effectiveness by generating questions in the optimal format according to the user's learning style.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The learning progress analysis unit analyzes the user's learning progress and level of understanding. For example, data such as the user's accuracy rate for past problems, answering time, and learning history is collected, and the generation AI analyzes this data. The learning progress analysis unit also identifies areas and topics in which the user has strengths and weaknesses. For example, the generation AI performs analysis based on prompts that include the user's learning history and answer data. Step 2: The question generator generates optimal mock test questions based on the user's learning progress and level of understanding analyzed by the learning progress analyzer. For example, it generates questions that focus on areas the user is weak in, or questions similar to questions that the user got wrong in the past. The question generator also generates questions based on prompts provided by the generation AI that reflect the user's learning progress and level of understanding. Step 3: The test implementation unit provides the mock test questions generated by the question generation unit to the user and conducts the mock test. For example, the generation AI provides the generated mock test questions to the user, and after the user answers the mock test, analyzes the answers and provides feedback.

[0058] (Example 2) The automatic mock test creation system according to the embodiment of the present invention is a system in which a generation AI automatically creates optimal mock test questions according to the user's learning progress and level of understanding. As a result, the automatic mock test creation system functions like a personal tutor for the user, and can efficiently and effectively support qualification study.

[0059] An automatic mock test creation system according to an embodiment includes a learning progress analysis unit, a question generation unit, and a test implementation unit. The learning progress analysis unit analyzes a user's learning progress and level of understanding. For example, data such as the user's correct answer rate, answer time, and learning history is collected, and the generation AI analyzes the data. The learning progress analysis unit also identifies areas and topics in which the user has strengths and weaknesses. For example, the generation AI performs analysis based on prompts including the user's learning history and answer data. The question generation unit generates optimal mock test questions based on the user's learning progress and level of understanding analyzed by the learning progress analysis unit. For example, the question generation unit generates questions that focus on areas in which the user is weak or questions similar to questions that the user got wrong in the past. The question generation unit also generates questions based on prompts that reflect the user's learning progress and level of understanding. The test implementation unit provides the user with the mock test questions generated by the question generation unit and conducts the mock test. For example, the generation AI provides the generated mock test questions to the user, and after the user answers the mock test, analyzes the answers and provides feedback. This allows the automatic mock test creation system according to the embodiment to provide optimal mock test questions based on the user's learning progress and level of understanding, supporting efficient learning. For example, by focusing on areas in which the user is weak, the user's ability to score well on the actual test can be improved. Furthermore, the feedback provided by the generation AI and the optimization of the learning plan allow the user to understand their own learning progress and develop an appropriate learning strategy.

[0060] The learning progress analysis unit can analyze the user's facial expressions or voice while studying in real time, capturing changes in emotion and estimating the user's level of understanding. For example, the learning progress analysis unit uses a camera and microphone to record the user's facial expressions and voice in real time while studying, and the generation AI analyzes the data. For example, it analyzes changes in facial expressions and tone of voice when the user solves problems to estimate the user's level of understanding. The learning progress analysis unit also uses facial expression recognition technology and voice analysis technology to capture changes in the user's emotions. For example, the generation AI analyzes patterns of changes in the user's facial expressions and voice and calculates an emotion score. This enables more accurate learning support by capturing changes in the user's emotions and estimating the user's level of understanding.

[0061] The learning progress analysis unit can also analyze the content of related materials or reference books viewed by the user to evaluate the depth of the user's learning. The learning progress analysis unit, for example, collects data on reference books and materials viewed by the user, and the generation AI analyzes the content. For example, it analyzes which chapters and pages the user frequently views to evaluate the depth of learning. The learning progress analysis unit also uses text analysis technology to analyze the content of related materials and reference books. For example, the generation AI extracts keywords and phrases from the materials viewed by the user to evaluate the depth of learning. This allows for a more accurate evaluation of the depth of learning by analyzing the content of the materials viewed by the user.

[0062] The learning progress analysis unit can analyze the stress or anxiety a user feels about a particular problem and evaluate the user's learning progress based on those emotions. The learning progress analysis unit, for example, collects biometric data (heart rate and electrodermal activity) when the user solves a problem, and the generation AI analyzes that data. For example, if the heart rate increases, it is assumed that the user is feeling stressed. The learning progress analysis unit also uses physiological indicators and behavioral data to analyze the user's stress and anxiety. For example, the generation AI analyzes changes in the user's heart rate and electrodermal activity to evaluate the level of stress and anxiety. This allows for a more accurate evaluation of the user's learning progress by analyzing the user's stress and anxiety.

[0063] The learning progress analysis unit can compare the user's learning data with that of other users to evaluate the user's relative progress. For example, the learning progress analysis unit collects learning data from other users and the generation AI evaluates the user's relative progress based on that data. For example, it evaluates the degree of progress compared to other users taking the same exam. The learning progress analysis unit also uses statistical analysis techniques to evaluate the user's relative progress. For example, the generation AI statistically analyzes the learning data of other users to evaluate the user's progress. This makes it possible to evaluate the user's relative progress by comparing it with the learning data of other users.

[0064] The learning progress analysis unit can provide the data in a visually easy-to-understand dashboard format, allowing the user to intuitively grasp their own progress. The learning progress analysis unit, for example, develops a dashboard that visually displays the user's learning progress. For example, the progress status is displayed using graphs and charts. The learning progress analysis unit also uses data visualization technology to display the progress in dashboard format. For example, the generation AI converts the user's learning data into graphs and charts and displays them visually. This allows the user to grasp their progress in a visually easy-to-understand dashboard format.

[0065] The learning progress analysis unit analyzes fluctuations in the user's motivation while studying and can adjust the user's study plan based on the results. The learning progress analysis unit, for example, collects behavioral data of the user while studying (e.g., study time and break time), and the generation AI analyzes that data. For example, if study time becomes shorter, it is estimated that motivation is declining. The learning progress analysis unit also uses behavioral data and psychological indicators to analyze fluctuations in the user's motivation. For example, the generation AI analyzes changes in the user's study time and break time and evaluates fluctuations in motivation. This makes it possible to analyze fluctuations in the user's motivation and adjust the study plan, thereby providing effective study support.

[0066] The question generation unit can analyze trends in past exam questions and predict and generate questions that are likely to appear in future exams. The question generation unit, for example, collects past exam question data, and the generation AI analyzes that data. For example, it identifies frequently appearing topics and question formats. The question generation unit also uses a predictive algorithm to predict questions that are likely to appear in future exams. For example, the generation AI generates questions that are likely to appear in future exams based on trends in past exam questions. This allows the system to effectively support users in preparing for exams by analyzing trends in past exam questions and predicting and generating questions that are likely to appear in future exams.

[0067] The question generation unit can generate questions in the optimal format according to the user's learning style. For example, the question generation unit analyzes the user's learning style (visual, auditory, experiential) and generates a question format according to that. For example, for a visual user, questions that make extensive use of diagrams and charts are generated. The question generation unit also uses learning style analysis technology to generate questions according to the user's learning style. For example, the generation AI analyzes the user's learning style and generates questions in the optimal format based on that. This can improve learning effectiveness by generating questions in the optimal format according to the user's learning style.

[0068] The question generation unit can generate a question format that reduces stress based on emotional data about questions that the user found difficult in the past. The question generation unit, for example, collects emotional data about questions that the user found difficult in the past, and the generation AI analyzes the data. For example, it identifies the question format that caused stress. The question generation unit also uses emotion analysis technology to generate a question format that reduces stress based on the user's emotional data. For example, the generation AI analyzes the user's emotional data and generates a question format that reduces stress. This reduces the burden of learning by generating a question format that reduces stress based on emotional data about questions that the user found difficult in the past.

[0069] The question generation unit can combine questions from different qualification exams to generate mock test questions for multiple qualifications. For example, the question generation unit collects question data from different qualification exams, and the generation AI generates mock test questions for multiple qualifications based on that data. For example, questions that include common topics are generated. The question generation unit also uses question data integration technology to combine questions from different qualification exams. For example, the generation AI integrates question data from different qualification exams to generate mock test questions for multiple qualifications. This makes it possible to generate mock test questions for multiple qualifications by combining questions from different qualification exams.

[0070] The problem generator generates problem sets with gradually adjusted difficulty levels, thereby encouraging the user's growth. The problem generator generates problem sets with gradually adjusted difficulty levels, for example, according to the user's learning progress. For example, beginner, intermediate, and advanced problems are provided in sequence. The problem generator also uses a difficulty adjustment algorithm to gradually adjust the difficulty levels. For example, the generation AI analyzes the user's learning progress and generates problem sets with gradually adjusted difficulty levels based on that analysis. This allows the user's growth to be encouraged by generating problem sets with gradually adjusted difficulty levels.

[0071] The question generation unit generates questions related to the topics that interest the user most, thereby increasing the user's motivation to learn. For example, the question generation unit analyzes the topics that interest the user most and generates questions related to those topics. For example, it provides questions in areas that interest the user. The question generation unit also uses surveys and behavioral data to identify the user's interests. For example, the generation AI analyzes the user's past learning history and survey results to identify topics of interest. This allows the generation of questions related to the topics that interest the user most, thereby increasing the user's motivation to learn.

[0072] The test implementation unit can analyze the user's answering speed and answer sequence in real time and provide feedback on improvements to the answering strategy. For example, the test implementation unit records the speed and sequence in which the user answers a mock test in real time, and the generation AI analyzes the data. For example, it identifies questions that take a long time to answer. The test implementation unit also uses answer data analysis technology to provide feedback on improvements to the answering strategy. For example, the generation AI analyzes the user's answering speed and answer sequence and proposes an efficient answering strategy. In this way, the user's answering efficiency can be improved by analyzing the answering speed and answer sequence in real time and providing feedback on improvements to the answering strategy.

[0073] The test implementation unit can evaluate the user's confidence in their answers and provide focused feedback for questions with low confidence. For example, the test implementation unit may have the user input their confidence level after answering a mock test to evaluate their confidence level. For example, the test implementation unit may have the user rate their confidence level for each question on a scale of 1 to 5. The test implementation unit may also use self-reports and behavioral data to provide focused feedback for questions with low confidence. For example, the generation AI may analyze the user's confidence data and suggest specific improvements for questions with low confidence. This allows the user's confidence level to be evaluated and focused feedback to be provided for questions with low confidence, thereby improving the user's understanding.

[0074] The test implementation unit can analyze the stress and anxiety felt by the user during the mock test and provide advice on relaxation methods and improving concentration based on those emotions. For example, the test implementation unit collects biometric data (heart rate and electrodermal activity) to analyze the stress and anxiety felt by the user during the mock test. For example, if the heart rate increases, it is assumed that the user is feeling stressed. The test implementation unit also uses emotion analysis technology to analyze the user's stress and anxiety. For example, the generative AI analyzes the user's biometric data and evaluates the level of stress and anxiety. This allows the stress and anxiety felt during the mock test to be analyzed and advice on relaxation methods and improving concentration based on those emotions, thereby improving the user's test performance.

[0075] The test implementation unit can provide a relative evaluation of the user by comparing the results with those of other users. For example, the test implementation unit collects the mock test results of other users, and the generation AI provides a relative evaluation based on that data. For example, it evaluates the user's performance compared to other users taking the same test. The test implementation unit also uses statistical analysis technology to provide the relative evaluation. For example, the generation AI statistically analyzes the mock test results of other users and evaluates the user's performance. This allows a comparison with the results of other users to provide a relative evaluation and identify the user's learning strengths and weaknesses.

[0076] The test implementation department can provide feedback in video format, conveying it to the user in a visually easy-to-understand format. For example, the test implementation department develops a system that provides feedback on mock tests in video format. For example, it uses video to explain answers and areas for improvement. The test implementation department also uses video production technology to provide feedback in a visually easy-to-understand format. For example, the generative AI creates explanatory videos and animations based on the user's answer data. In this way, by providing feedback in video format, it can convey it to the user in a visually easy-to-understand format.

[0077] The test implementation unit can analyze the sense of accomplishment and satisfaction a user feels after a mock test and suggest the next learning step based on those emotions. For example, the test implementation unit collects emotional data (facial expressions and voice) to analyze the sense of accomplishment and satisfaction a user feels after a mock test. For example, it analyzes smiles and tone of voice. The test implementation unit also uses emotion analysis technology to analyze the user's sense of accomplishment and satisfaction. For example, the generation AI analyzes the user's facial expressions and voice data to evaluate the level of the sense of accomplishment and satisfaction. This allows the system to analyze the sense of accomplishment and satisfaction felt after a mock test and suggest the next learning step based on those emotions, thereby maintaining the user's motivation to learn.

[0078] The study plan optimization unit can suggest optimal study times and environments based on the user's study history and mock test results. For example, the study plan optimization unit analyzes the user's study history and mock test results to suggest optimal study times. For example, it identifies the time of day when the user can concentrate best. The study plan optimization unit also uses environmental analysis technology to suggest optimal study environments. For example, the generation AI suggests study environments such as quiet places and appropriate lighting based on the user's study history and mock test results. This makes it possible to improve study efficiency by suggesting optimal study times and environments based on the user's study history and mock test results.

[0079] The study plan optimization unit can propose a reasonable study plan by taking into account the user's lifestyle and schedule. For example, the study plan optimization unit collects the user's lifestyle and schedule, and the generation AI proposes a reasonable study plan based on that data. For example, it sets study times that fit with work and home schedules. The study plan optimization unit also uses lifestyle analysis technology to consider the user's lifestyle. For example, the generation AI analyzes the user's sleep time and daily activity patterns to propose an optimal study plan. This makes it possible to increase the continuity of learning by proposing a reasonable study plan that takes into account the user's lifestyle and schedule.

[0080] The study plan optimization unit analyzes fluctuations in the user's motivation for studying and can adjust the user's study plan based on the results. The study plan optimization unit, for example, collects behavioral data on the user while studying (e.g., study time and break time), and the generation AI analyzes that data. For example, if study time becomes shorter, it is estimated that motivation is declining. The study plan optimization unit also uses behavioral data and psychological indicators to analyze fluctuations in the user's motivation. For example, the generation AI analyzes changes in the user's study time and break time and evaluates fluctuations in motivation. This enables effective study support by analyzing fluctuations in the user's motivation and adjusting the study plan based on the results.

[0081] The study plan optimization unit can compare the success stories of other users and derive the optimal plan. For example, the study plan optimization unit collects the study plans and success stories of other users, and the generation AI derives the optimal plan based on that data. For example, it refers to the success stories of other users taking the same exam. The study plan optimization unit also uses data analysis technology to compare success stories. For example, the generation AI analyzes the success stories of other users and identifies similarities and differences. This allows it to derive the optimal study plan by comparing it with the success stories of other users.

[0082] The study plan optimization unit can provide the data in a visually easy-to-understand Gantt chart format, allowing the user to intuitively grasp their progress. The study plan optimization unit, for example, develops a Gantt chart that visually displays the user's study plan. For example, it displays the study progress in a bar format. The study plan optimization unit also uses data visualization technology to display the progress in Gantt chart format. For example, the generation AI converts the user's study data into a Gantt chart and displays it visually. This allows the user to grasp their progress in a visually easy-to-understand Gantt chart format.

[0083] The study plan optimization unit can identify the time periods when the user can concentrate best and propose a plan for focused study during those time periods. The study plan optimization unit, for example, analyzes the user's study history and mock test results to identify the time periods when the user can concentrate best. For example, it identifies the time periods when the user has the highest rate of correct answers. The study plan optimization unit also uses behavioral data and psychological indicators to identify the time periods when the user can concentrate best. For example, the generation AI identifies the time periods when the user is most able to concentrate best based on the user's study history and mock test results. This allows the system to identify the time periods when the user can concentrate best and propose a plan for focused study during those time periods, thereby improving study efficiency.

[0084] The continuous learning support unit can periodically evaluate the user's learning progress and adjust the user's learning plan as necessary. For example, the continuous learning support unit periodically evaluates the user's learning history and mock test results, and the generation AI adjusts the learning plan based on that data. For example, the unit evaluates progress every week and updates the learning plan. The continuous learning support unit also uses schedule management technology for periodic evaluation. For example, the generation AI manages the user's learning schedule and periodically evaluates progress. This allows the user's learning effectiveness to be maximized by periodically evaluating learning progress and adjusting the learning plan as necessary.

[0085] The continuous learning support unit can provide encouraging messages and rewards when the user's motivation drops. For example, the continuous learning support unit analyzes the user's learning history and emotional data and provides encouraging messages when motivation drops. For example, an encouraging message is sent when the user stops learning. The continuous learning support unit also uses a point system to provide rewards when motivation drops. For example, the generation AI awards points according to the user's learning progress and provides rewards when a certain number of points are accumulated. This makes it possible to maintain the user's motivation to learn by providing encouraging messages and rewards when motivation drops.

[0086] The continuous learning support unit can analyze the stress and anxiety a user feels about learning and provide advice on relaxation methods and improving concentration based on those emotions. For example, the continuous learning support unit collects biometric data (heart rate and electrodermal activity) to analyze the stress and anxiety a user feels while learning, and the generation AI analyzes that data. For example, if the heart rate increases, it is assumed that the user is feeling stressed. The continuous learning support unit also uses emotion analysis technology to analyze the user's stress and anxiety. For example, the generation AI analyzes the user's biometric data and evaluates the level of stress and anxiety. This allows the stress and anxiety felt about learning to be analyzed, and advice on relaxation methods and improving concentration based on those emotions, thereby improving the user's learning efficiency.

[0087] The continuous learning support unit can provide a function that promotes learning competition and cooperation with other users. The continuous learning support unit, for example, provides a ranking function to promote learning competition with other users. For example, a ranking may be created based on mock test scores, and users may compete against each other. The continuous learning support unit also provides a group learning function to promote cooperation with other users. For example, the generative AI provides a platform where users can study together. This can increase users' motivation to learn by providing a function that promotes learning competition and cooperation with other users.

[0088] The continuous learning support unit can customize and provide a personalized learning experience according to the user's interests and concerns. The continuous learning support unit, for example, analyzes the user's interests and concerns and provides a personalized learning experience accordingly. For example, it provides questions related to topics that interest the user. The continuous learning support unit also uses personalization technology to provide a personalized learning experience. For example, the generative AI analyzes the user's interests and concerns and customizes learning content based on them. This allows for a personalized learning experience that is customized according to the user's interests and concerns, thereby improving the effectiveness of learning.

[0089] The continuous learning support unit can analyze the sense of accomplishment and satisfaction a user feels from learning and suggest the next learning step based on that emotion. For example, the continuous learning support unit collects emotional data (facial expressions and voice) to analyze the sense of accomplishment and satisfaction a user feels from learning. For example, it analyzes smiles and tone of voice. The continuous learning support unit also uses emotion analysis technology to analyze the user's sense of accomplishment and satisfaction. For example, the generation AI analyzes the user's facial expressions and voice data to evaluate the level of the sense of accomplishment and satisfaction. This allows the unit to analyze the sense of accomplishment and satisfaction a user feels from learning and suggest the next learning step based on that emotion, thereby maintaining the user's motivation to learn.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The learning progress analysis unit can analyze the user's posture and movements while studying and evaluate fluctuations in concentration. For example, if the user maintains the same posture for a long period of time, it can estimate that the user's concentration is declining. The learning progress analysis unit also uses posture recognition technology and motion analysis technology to analyze the user's posture and movements. For example, the generation AI can analyze changes in the user's posture and movements to evaluate fluctuations in concentration. This allows for a more accurate evaluation of fluctuations in concentration by analyzing the user's posture and movements.

[0092] The learning progress analysis unit analyzes the user's facial expressions or voice in real time while they are studying, capturing changes in their emotions and estimating their level of understanding. For example, it analyzes changes in facial expressions and tone of voice when the user is solving a problem to estimate their level of understanding. The learning progress analysis unit also uses facial expression recognition technology and voice analysis technology to capture changes in the user's emotions. For example, the generation AI analyzes patterns of changes in the user's facial expressions and voice and calculates an emotion score. This allows for more accurate learning support by capturing changes in the user's emotions and estimating their level of understanding.

[0093] The learning progress analysis unit can also analyze the content of related materials or reference books viewed by the user to evaluate the depth of the user's learning. For example, it can analyze which chapters or pages the user frequently views to evaluate the depth of learning. The learning progress analysis unit also uses text analysis technology to analyze the content of related materials and reference books. For example, the generation AI can extract keywords and phrases from the materials viewed by the user to evaluate the depth of learning. This allows for a more accurate evaluation of the depth of learning by analyzing the content of the materials viewed by the user.

[0094] The learning progress analysis unit can analyze the stress or anxiety a user feels about a particular problem and evaluate the user's learning progress based on those emotions. For example, biometric data (heart rate and electrodermal activity) is collected when the user solves a problem, and the generation AI analyzes that data. For example, if the heart rate increases, it is assumed that the user is feeling stressed. The learning progress analysis unit also uses physiological indicators and behavioral data to analyze the user's stress and anxiety. For example, the generation AI analyzes changes in the user's heart rate and electrodermal activity to evaluate the level of stress and anxiety. This allows for a more accurate evaluation of the user's learning progress by analyzing the user's stress and anxiety.

[0095] The learning progress analysis unit can compare the user's learning data with that of other users to evaluate the user's relative progress. For example, the generation AI collects learning data from other users and evaluates the user's relative progress based on that data. For example, it evaluates the degree of progress compared to other users taking the same exam. The learning progress analysis unit also uses statistical analysis techniques to evaluate the user's relative progress. For example, the generation AI statistically analyzes the learning data of other users to evaluate the user's progress. This makes it possible to evaluate the user's relative progress by comparing it with the learning data of other users.

[0096] The learning progress analysis unit can provide the data in a visually easy-to-understand dashboard format, allowing the user to intuitively grasp their progress. For example, a dashboard that visually displays the user's learning progress can be developed. For example, progress can be displayed using graphs and charts. The learning progress analysis unit also uses data visualization technology to display progress in dashboard format. For example, the generation AI converts the user's learning data into graphs and charts and displays them visually. This allows the user to grasp progress in a visually easy-to-understand dashboard format.

[0097] The learning progress analysis unit analyzes fluctuations in the user's motivation while studying and can adjust the user's study plan based on the results. For example, behavioral data on the user while studying (e.g., study time and break time) is collected, and the generation AI analyzes that data. For example, if study time becomes shorter, it is assumed that motivation is declining. The learning progress analysis unit also uses behavioral data and psychological indicators to analyze fluctuations in the user's motivation. For example, the generation AI analyzes changes in the user's study time and break time and evaluates fluctuations in motivation. This enables effective study support by analyzing fluctuations in the user's motivation and adjusting the study plan.

[0098] The question generation unit can analyze trends in past exam questions and predict and generate questions that are likely to appear in future exams. For example, past exam question data is collected and the generation AI analyzes that data. For example, frequently appearing topics and question formats are identified. The question generation unit also uses a predictive algorithm to predict questions that are likely to appear in future exams. For example, the generation AI generates questions that are likely to appear in future exams based on trends in past exam questions. This allows the system to analyze trends in past exam questions and predict and generate questions that are likely to appear in future exams, thereby effectively supporting users' exam preparation.

[0099] The question generation unit can generate questions in the optimal format according to the user's learning style. For example, it can analyze the user's learning style (visual, auditory, experiential) and generate a question format that suits it. For example, it can generate questions that make extensive use of diagrams for visual users. The question generation unit also uses learning style analysis technology to generate questions that suit the user's learning style. For example, the generation AI can analyze the user's learning style and generate questions in the optimal format based on that. This can improve learning effectiveness by generating questions in the optimal format according to the user's learning style.

[0100] The question generation unit can generate question formats that reduce stress based on emotional data from questions that the user found difficult in the past. For example, emotional data from questions that the user found difficult in the past is collected, and the generation AI analyzes the data. For example, the question formats that caused stress are identified. The question generation unit also uses emotion analysis technology to generate question formats that reduce stress based on the user's emotional data. For example, the generation AI analyzes the user's emotional data and generates question formats that reduce stress. This reduces the burden of learning by generating question formats that reduce stress based on emotional data from questions that the user found difficult in the past.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The learning progress analysis unit analyzes the user's learning progress and level of understanding. For example, data such as the user's accuracy rate for past problems, answering time, and learning history is collected, and the generation AI analyzes this data. The learning progress analysis unit also identifies areas and topics in which the user has strengths and weaknesses. For example, the generation AI performs analysis based on prompts that include the user's learning history and answer data. Step 2: The question generator generates optimal mock test questions based on the user's learning progress and level of understanding analyzed by the learning progress analyzer. For example, it generates questions that focus on areas the user is weak in, or questions similar to questions that the user got wrong in the past. The question generator also generates questions based on prompts provided by the generation AI that reflect the user's learning progress and level of understanding. Step 3: The test implementation unit provides the mock test questions generated by the question generation unit to the user and conducts the mock test. For example, the generation AI provides the generated mock test questions to the user, and after the user answers the mock test, analyzes the answers and provides feedback.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a learning progress analysis unit that analyzes a user's learning progress and level of understanding; a question generation unit that generates optimal mock test questions based on the user's learning progress and level of understanding analyzed by the learning progress analysis unit; a test implementation unit that provides the mock test questions generated by the question generation unit to the user and implements the mock test. A system characterized by:

2. The learning progress analysis unit Provide a visually easy-to-understand dashboard format, allowing users to intuitively grasp their own progress 2. The system of claim 1.

3. The question generator Analyze trends in past exam questions and generate predicted questions that are likely to appear in future exams 2. The system of claim 1.

4. The test implementation unit The speed and order of the user's answers are analyzed in real time, and feedback is provided to the user on how to improve their answering strategy.

2. The system of claim 1.

5. The Learning Plan Optimization Department Analyzing fluctuations in the motivation felt by the user for learning and adjusting the user's learning plan based on the results of the analysis 2. The system of claim 1.

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