System

Abstract

An object of a system according to an embodiment is to provide an optimal learning plan according to a learning situation of a user.SOLUTION: A system includes a learning state analysis part, a learning plan proposal part, an examination question generation part, a progress confirmation part, a learning resource provision part, and a motivation improvement part. The learning state analysis unit includes a generation AI. The learning plan proposing section proposes an optimum learning plan based on the user's learning state analyzed by the learning state analyzing section. The examination question generating section generates examination questions based on the learning plan proposed by the learning plan proposing section. The progress confirmation section analyzes the answer results of the examination questions answered by the user and confirms the progress of learning. The learning resource provision unit provides a learning resource according to the learning status of the user. The motivation improvement unit proposes a reward or a title for improving the learning motivation of the user.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] With conventional technology, it is difficult to provide an optimal study plan that suits a user's study situation, which may result in a decrease in study efficiency.

[0005] The system according to the embodiment aims to provide an optimal study plan according to the user's study situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning status analysis unit, a learning plan proposal unit, a test question generation unit, a progress confirmation unit, a learning resource provision unit, and a motivation improvement unit. The learning status analysis unit is equipped with a generation AI. The learning plan proposal unit proposes an optimal learning plan based on the user's learning status analyzed by the learning status analysis unit. The test question generation unit generates test questions based on the learning plan proposed by the learning plan proposal unit. The progress confirmation unit analyzes the answers to the test questions answered by the user and confirms the user's learning progress. The learning resource provision unit provides learning resources according to the user's learning status. The motivation improvement unit proposes rewards and titles to improve the user's motivation to learn. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal study plan according to the user's study situation. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 test preparation tool according to an embodiment of the present invention is a system that utilizes a generative AI to analyze a user's learning status, propose an optimal study plan, generate test questions, check learning progress, provide learning resources, and improve learning motivation. As a result, the test preparation tool can efficiently manage the user's learning status and provide effective test preparation.

[0029] The test preparation tool according to the embodiment includes a learning status analysis unit, a study plan proposal unit, a test question generation unit, a progress confirmation unit, a learning resource provision unit, and a motivation improvement unit. The learning status analysis unit is equipped with a generation AI and analyzes the user's learning status. For example, the generation AI analyzes the user's learning history and current learning status to identify weak areas and areas that should be focused on. The study plan proposal unit proposes an optimal study plan based on the user's learning status analyzed by the learning status analysis unit. For example, the generation AI generates a study plan tailored to the user's individual learning goals and learning style based on the user's learning history and current learning status. The test question generation unit generates test questions based on the study plan proposed by the study plan proposal unit. For example, the generation AI generates test questions that include many questions related to the user's weak areas. The progress confirmation unit analyzes the answers to the test questions answered by the user and confirms the user's learning progress. For example, the generation AI analyzes the correct answer rate and answer time to evaluate the user's learning progress. The learning resource provision unit provides learning resources tailored to the user's learning status. For example, the generation AI suggests reference books and video lectures related to areas in which the user is weak. The motivation improvement unit suggests rewards and titles to increase the user's motivation to study. For example, the generation AI introduces gamification elements that grant rewards and titles according to the user's learning progress. As a result, the test preparation tool according to the embodiment can efficiently manage the user's learning status and provide effective test preparation.

[0030] The learning status analysis unit analyzes not only the user's learning history but also their daily behavioral patterns to suggest optimal study times for the user. For example, the generation AI analyzes data obtained from the user's smartwatch or fitness tracker to understand the user's sleep patterns and exercise volume. This identifies the time periods when the user can concentrate best and suggests studying during those times. The learning status analysis unit also analyzes the user's smartphone usage history and calendar app data to understand their daily schedule. This identifies the optimal time periods for the user to study and incorporates study plans into those times. The generation AI also analyzes the user's eating and caffeine intake patterns to identify the time periods when energy levels are high. This suggests the time periods when the user can study most efficiently. This improves learning efficiency by analyzing the user's daily behavioral patterns and suggesting optimal study times.

[0031] The study plan suggestion unit can analyze a user's learning style and propose a study plan based on that. For example, the generation AI in the study plan suggestion unit analyzes the user's past learning history and survey results to identify which learning style is best suited to the user. For example, for a visual user, the study plan suggestion unit proposes learning materials that make extensive use of diagrams and graphs. The study plan suggestion unit also analyzes the effectiveness of learning resources used by the user in the past to identify which resources were most effective. For example, for an auditory user, the study plan suggestion unit proposes audio lectures or podcasts. The generation AI in the study plan suggestion unit also proposes a customized study plan based on the user's learning style based on user feedback. For example, for a tactile user, the study plan suggestion unit incorporates practical exercises and hands-on activities. This maximizes learning effectiveness by proposing a study plan based on the user's learning style.

[0032] The progress confirmation unit can analyze the user's answer results in detail and provide feedback on specific areas for improvement. In the progress confirmation unit, for example, the generation AI analyzes the user's answer results and identifies specific areas for improvement. For example, it points out errors in the answer to a specific problem and suggests the correct answer method. The progress confirmation unit also analyzes in detail which parts the user made mistakes in based on the user's answer data and provides feedback on specific areas for improvement. For example, it points out calculation errors or conceptual misunderstandings. In addition, the generation AI analyzes the user's answer results and provides feedback on specific areas for improvement. For example, it analyzes the answer process in detail and identifies which step the error was made in. In this way, the learning effect is improved by analyzing the user's answer results in detail and providing feedback on specific areas for improvement.

[0033] The progress confirmation unit can visualize the user's learning progress in graphs and charts and provide intuitively understandable feedback. In the progress confirmation unit, for example, the generation AI analyzes the user's learning progress and visualizes it in graphs and charts. For example, it displays the change in the correct answer rate and the distribution of study time. The progress confirmation unit also provides tools to visualize the progress status based on the user's learning data. For example, it displays the learning progress in pie charts and bar graphs. The progress confirmation unit also analyzes the user's learning progress in the generation AI and provides visually easy-to-understand feedback. For example, it displays the learning progress in a timeline format. This visualizes the user's learning progress and provides intuitively understandable feedback, thereby improving motivation to learn.

[0034] The learning resource providing unit can suggest optimal learning resources based on the user's learning history and current learning situation. In the learning resource providing unit, for example, the generation AI analyzes the user's learning history and suggests learning resources that are optimal for the current learning situation. For example, it suggests reference books and video lectures on areas in which the user is weak. The learning resource providing unit also builds a system that suggests optimal learning resources based on the user's learning data. For example, it suggests resources that will allow the user to study most effectively. In the learning resource providing unit, the generation AI analyzes the user's learning progress and suggests optimal learning resources. For example, it suggests resources related to the topic the user should study next. In this way, learning efficiency is improved by suggesting optimal learning resources based on the user's learning history and current learning situation.

[0035] The learning resource providing unit can suggest resources that match the user's learning style. For example, the learning resource providing unit uses a generation AI to analyze the user's learning style and suggest learning resources based on that. For example, for a visual user, it suggests learning materials that make extensive use of diagrams and graphs. The learning resource providing unit also builds a system that suggests resources that match the user's learning style based on the user's learning data. For example, it suggests audio lectures and podcasts for an auditory user. The learning resource providing unit also uses a generation AI to analyze the user's learning style and suggest learning resources based on that. For example, it suggests practical exercises and hands-on activities for a tactile user. In this way, learning effectiveness is maximized by suggesting resources that match the user's learning style.

[0036] The motivation improvement unit can suggest individually customized rewards and titles based on the user's learning history and current learning situation. In the motivation improvement unit, for example, the generation AI analyzes the user's learning history and suggests rewards and titles according to the current learning situation. For example, it suggests titles and rewards to be given when a specific goal is achieved. The motivation improvement unit also builds a system that suggests individually customized rewards and titles based on the user's learning data. For example, it suggests rewards and titles that the user finds most motivating. In the motivation improvement unit, the generation AI analyzes the user's learning progress and suggests individually customized rewards and titles. For example, it suggests rewards and titles for the next goal the user should achieve. In this way, by suggesting individually customized rewards and titles based on the user's learning history and current learning situation, motivation for learning is improved.

[0037] The motivation improvement unit can suggest gamification elements that match the user's learning style. For example, the generation AI in the motivation improvement unit analyzes the user's learning style and suggests gamification elements based on that. For example, it suggests visual rewards and titles for visual users. The motivation improvement unit also builds a system that suggests gamification elements that match the user's learning style based on the user's learning data. For example, it suggests audio rewards and titles for auditory users. The motivation improvement unit also analyzes the user's learning style using the generation AI and suggests gamification elements based on that. For example, it suggests practical rewards and titles for tactile users. In this way, by suggesting gamification elements that match the user's learning style, motivation for learning is improved.

[0038] The test question generation unit can generate individually customized test questions based on the user's learning history and current learning situation. For example, the test question generation unit uses a generation AI to analyze the user's past answer data and identify areas of weakness and areas that should be focused on. This generates test questions that are optimal for the user. The test question generation unit also generates test questions that match the user's current learning situation based on the user's learning history. For example, it generates test questions that include many questions for reviewing recently learned content. The test question generation unit also uses a generation AI to analyze the user's learning progress and generate individually customized test questions. For example, it generates questions with difficulty adjusted according to the user's level of understanding. This maximizes the learning effect by generating individually customized test questions based on the user's learning history and current learning situation.

[0039] The test question generation unit can analyze the user's answer patterns, identify answer trends and mistake patterns, and generate test questions based on them. In the test question generation unit, for example, the generation AI analyzes the user's past answer data and identifies answer trends and mistake patterns. This generates test questions that include many questions that the user often gets wrong. The test question generation unit also generates test questions to prevent specific mistakes based on the user's answer patterns. For example, it generates questions to prevent the user from making the same type of mistake repeatedly. In the test question generation unit, the generation AI analyzes the user's answer data and identifies answer trends and mistake patterns. This generates test questions to reinforce concepts that the user finds difficult to understand. This improves learning effectiveness by analyzing the user's answer patterns, identifying answer trends and mistake patterns, and generating test questions based on them.

[0040] The test question generation unit can generate cross-disciplinary test questions that combine knowledge from different academic fields. For example, the test question generation unit generates test questions that combine knowledge from different academic fields using a generation AI. For example, it generates questions that combine knowledge of physics and chemistry. The test question generation unit also generates test questions that combine knowledge from different academic fields based on a user's learning history. For example, it generates questions that combine knowledge of history and geography. The test question generation unit also generates cross-disciplinary test questions that combine knowledge from different academic fields using a generation AI. For example, it generates questions that combine knowledge of mathematics and economics. In this way, the breadth of learning is broadened by generating cross-disciplinary test questions that combine knowledge from different academic fields.

[0041] The test question generation unit generates questions similar to past test questions based on the user's learning history, thereby enhancing the effectiveness of review. For example, the test question generation unit uses a generation AI to analyze the user's past test questions and generate similar questions. This allows the user to review content that they have previously studied. The test question generation unit also generates questions similar to past test questions based on the user's learning history. For example, it generates questions related to the same theme or topic. The test question generation unit also uses a generation AI to analyze the user's learning progress and generate questions similar to past test questions. This allows the user to enhance the effectiveness of review. This enhances the effectiveness of review by generating questions similar to past test questions based on the user's learning history.

[0042] The motivation improvement unit can propose cross-disciplinary rewards and titles that combine knowledge from different academic fields. For example, the generation AI in the motivation improvement unit proposes rewards and titles that combine knowledge from different academic fields. For example, it proposes rewards and titles that combine knowledge of physics and chemistry. The motivation improvement unit also proposes rewards and titles that combine knowledge from different academic fields based on the user's learning history. For example, it proposes rewards and titles that combine knowledge of history and geography. The motivation improvement unit also proposes cross-disciplinary rewards and titles that combine knowledge from different academic fields. For example, it proposes rewards and titles that combine knowledge of mathematics and economics. In this way, the scope of learning is broadened by proposing cross-disciplinary rewards and titles that combine knowledge from different academic fields.

[0043] The motivation improvement unit can suggest goals similar to goals achieved in the past based on the user's learning history. For example, the motivation improvement unit uses a generation AI to analyze the user's past learning history and suggest similar goals. This increases the user's motivation to achieve goals they have achieved in the past again. The motivation improvement unit also builds a system that suggests goals similar to goals achieved in the past based on the user's learning data. For example, it re-suggests learning methods that have been successful for the user in the past. The motivation improvement unit also uses a generation AI to analyze the user's learning progress and suggest goals similar to goals achieved in the past. This allows the user to progress with their learning effectively. This improves learning motivation by suggesting goals similar to goals they have achieved in the past based on the user's learning history.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The learning status analysis unit analyzes not only the user's learning history but also their daily behavioral patterns to suggest optimal study times for the user. For example, the generation AI analyzes data obtained from the user's smartwatch or fitness tracker to understand the user's sleep patterns and exercise volume. This identifies the time periods when the user can concentrate best and suggests that they study during those times. The learning status analysis unit also analyzes the user's smartphone usage history and calendar app data to understand their daily schedule. This identifies the optimal time periods for the user to study and incorporates study plans into those times. The generation AI also analyzes the user's eating and caffeine intake patterns to identify times when energy levels are high. This suggests the most efficient time periods for the user to study. This improves learning efficiency by analyzing the user's daily behavioral patterns and suggesting optimal study times.

[0046] The learning plan suggestion unit can analyze a user's learning style and propose a learning plan based on that. For example, the generation AI can analyze a user's past learning history and survey results to determine which learning style is best suited to the user. For example, for a visual user, it can propose learning materials that make extensive use of diagrams and graphs. The learning plan suggestion unit can also analyze the effectiveness of learning resources used by the user in the past to determine which resources were most effective. For example, it can propose audio lectures and podcasts to an auditory user. The learning plan suggestion unit can also propose a customized learning plan based on user feedback according to the user's learning style. For example, it can incorporate practical exercises and hands-on activities for a tactile user. This maximizes learning effectiveness by proposing a learning plan based on the user's learning style.

[0047] The progress confirmation unit can analyze the user's answer results in detail and provide feedback on specific areas for improvement. For example, the generation AI analyzes the user's answer results and identifies specific areas for improvement. For example, it points out errors in the answer to a specific problem and suggests the correct answer method. The progress confirmation unit also analyzes in detail which parts the user made mistakes in based on the user's answer data and provides feedback on specific areas for improvement. For example, it points out calculation errors or conceptual misunderstandings. The progress confirmation unit also analyzes the user's answer results in detail and provides feedback on specific areas for improvement. For example, it analyzes the answer process in detail and identifies which step the error was made in. In this way, the learning effect is improved by analyzing the user's answer results in detail and providing feedback on specific areas for improvement.

[0048] The progress confirmation unit can visualize the user's learning progress in graphs and charts and provide intuitively understandable feedback. For example, the generation AI analyzes the user's learning progress and visualizes it in graphs and charts. For example, it displays the trend in correct answer rate and the distribution of study time. The progress confirmation unit also provides tools to visualize progress based on the user's learning data. For example, it displays learning progress in pie charts and bar graphs. The progress confirmation unit also analyzes the user's learning progress in the generation AI and provides visually easy-to-understand feedback. For example, it displays learning progress in a timeline format. This visualizes the user's learning progress and provides intuitively understandable feedback, thereby improving learning motivation.

[0049] The learning resource providing unit can suggest optimal learning resources based on the user's learning history and current learning situation. For example, the generation AI analyzes the user's learning history and suggests learning resources that are optimal for the current learning situation. For example, it may suggest reference books or video lectures related to areas in which the user is weak. The learning resource providing unit also builds a system that suggests optimal learning resources based on the user's learning data. For example, it may suggest resources that will allow the user to study most effectively. The learning resource providing unit also uses the generation AI to analyze the user's learning progress and suggest optimal learning resources. For example, it may suggest resources related to the topic the user should study next. In this way, learning efficiency is improved by suggesting optimal learning resources based on the user's learning history and current learning situation.

[0050] The learning resource provision unit can suggest resources that match the user's learning style. For example, the generation AI analyzes the user's learning style and suggests learning resources based on that. For example, for a visual user, it would suggest learning materials that make extensive use of diagrams and graphs. The learning resource provision unit also builds a system that suggests resources that match the user's learning style based on the user's learning data. For example, it would suggest audio lectures and podcasts for an auditory user. The learning resource provision unit also uses the generation AI to analyze the user's learning style and suggest learning resources based on that. For example, it would suggest practical exercises and hands-on activities for a tactile user. In this way, learning effectiveness is maximized by suggesting resources that match the user's learning style.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The learning situation analysis unit is equipped with a generation AI and analyzes the user's learning situation. For example, the generation AI analyzes the user's learning history and current learning situation to identify weak areas and areas that should be studied intensively. Step 2: The study plan suggestion unit proposes an optimal study plan based on the user's study status analyzed by the study status analysis unit. For example, the generation AI generates a study plan based on the user's study history and current study status, tailored to their individual learning goals and learning style. Step 3: The test question generation unit generates test questions based on the study plan proposed by the study plan proposal unit. For example, the generation AI generates test questions that include many questions related to areas in which the user is weak. Step 4: The progress confirmation unit analyzes the answers to the test questions answered by the user and checks the progress of the user's learning. For example, the generation AI analyzes the correct answer rate and answer time to evaluate the progress of the user's learning. Step 5: The learning resource provider provides learning resources according to the user's learning situation. For example, the generation AI suggests reference books and video lectures on areas in which the user is weak. Step 6: The motivation improvement unit proposes rewards and titles to improve the user's motivation to learn. For example, the generation AI introduces gamification elements that give rewards and titles according to the user's learning progress.

[0053] (Example 2) The test preparation tool according to an embodiment of the present invention is a system that utilizes a generative AI to analyze a user's learning status, propose an optimal study plan, generate test questions, check learning progress, provide learning resources, and improve learning motivation. As a result, the test preparation tool can efficiently manage the user's learning status and provide effective test preparation.

[0054] The test preparation tool according to the embodiment includes a learning status analysis unit, a study plan proposal unit, a test question generation unit, a progress confirmation unit, a learning resource provision unit, and a motivation improvement unit. The learning status analysis unit is equipped with a generation AI and analyzes the user's learning status. For example, the generation AI analyzes the user's learning history and current learning status to identify weak areas and areas that should be focused on. The study plan proposal unit proposes an optimal study plan based on the user's learning status analyzed by the learning status analysis unit. For example, the generation AI generates a study plan tailored to the user's individual learning goals and learning style based on the user's learning history and current learning status. The test question generation unit generates test questions based on the study plan proposed by the study plan proposal unit. For example, the generation AI generates test questions that include many questions related to the user's weak areas. The progress confirmation unit analyzes the answers to the test questions answered by the user and confirms the user's learning progress. For example, the generation AI analyzes the correct answer rate and answer time to evaluate the user's learning progress. The learning resource provision unit provides learning resources tailored to the user's learning status. For example, the generation AI suggests reference books and video lectures related to areas in which the user is weak. The motivation improvement unit suggests rewards and titles to increase the user's motivation to study. For example, the generation AI introduces gamification elements that grant rewards and titles according to the user's learning progress. As a result, the test preparation tool according to the embodiment can efficiently manage the user's learning status and provide effective test preparation.

[0055] The learning status analysis unit analyzes not only the user's learning history but also their daily behavioral patterns to suggest optimal study times for the user. For example, the generation AI analyzes data obtained from the user's smartwatch or fitness tracker to understand the user's sleep patterns and exercise volume. This identifies the time periods when the user can concentrate best and suggests studying during those times. The learning status analysis unit also analyzes the user's smartphone usage history and calendar app data to understand their daily schedule. This identifies the optimal time periods for the user to study and incorporates study plans into those times. The generation AI also analyzes the user's eating and caffeine intake patterns to identify the time periods when energy levels are high. This suggests the time periods when the user can study most efficiently. This improves learning efficiency by analyzing the user's daily behavioral patterns and suggesting optimal study times.

[0056] The study plan suggestion unit can analyze a user's learning style and propose a study plan based on that. For example, the generation AI in the study plan suggestion unit analyzes the user's past learning history and survey results to identify which learning style is best suited to the user. For example, for a visual user, the study plan suggestion unit proposes learning materials that make extensive use of diagrams and graphs. The study plan suggestion unit also analyzes the effectiveness of learning resources used by the user in the past to identify which resources were most effective. For example, for an auditory user, the study plan suggestion unit proposes audio lectures or podcasts. The generation AI in the study plan suggestion unit also proposes a customized study plan based on the user's learning style based on user feedback. For example, for a tactile user, the study plan suggestion unit incorporates practical exercises and hands-on activities. This maximizes learning effectiveness by proposing a study plan based on the user's learning style.

[0057] The study plan suggestion unit uses the emotion estimation function to analyze the user's emotions while studying and propose a low-stress study plan. For example, the study plan suggestion unit uses a generation AI to analyze the user's facial expressions and voice tone to grasp the user's emotional state while studying in real time. This allows the unit to propose a relaxing study method when stress is high. The study plan suggestion unit also monitors the user's heart rate and electrodermal activity to measure stress levels. This allows the unit to suggest studying during times of low stress. The study plan suggestion unit also uses the emotion estimation function to identify an environment in which the user can study most relaxed. For example, it suggests studying in a quiet place or while listening to specific music. This allows the unit to analyze the user's emotions while studying and propose a low-stress study plan, thereby improving continuity in study.

[0058] The progress confirmation unit can analyze the user's answer results in detail and provide feedback on specific areas for improvement. In the progress confirmation unit, for example, the generation AI analyzes the user's answer results and identifies specific areas for improvement. For example, it points out errors in the answer to a specific problem and suggests the correct answer method. The progress confirmation unit also analyzes in detail which parts the user made mistakes in based on the user's answer data and provides feedback on specific areas for improvement. For example, it points out calculation errors or conceptual misunderstandings. In addition, the generation AI analyzes the user's answer results and provides feedback on specific areas for improvement. For example, it analyzes the answer process in detail and identifies which step the error was made in. In this way, the learning effect is improved by analyzing the user's answer results in detail and providing feedback on specific areas for improvement.

[0059] The progress confirmation unit can visualize the user's learning progress in graphs and charts and provide intuitively understandable feedback. In the progress confirmation unit, for example, the generation AI analyzes the user's learning progress and visualizes it in graphs and charts. For example, it displays the change in the correct answer rate and the distribution of study time. The progress confirmation unit also provides tools to visualize the progress status based on the user's learning data. For example, it displays the learning progress in pie charts and bar graphs. The progress confirmation unit also analyzes the user's learning progress in the generation AI and provides visually easy-to-understand feedback. For example, it displays the learning progress in a timeline format. This visualizes the user's learning progress and provides intuitively understandable feedback, thereby improving motivation to learn.

[0060] The progress confirmation unit can use the emotion estimation function to analyze the emotions the user has when receiving feedback and provide feedback that elicits positive emotions. The progress confirmation unit, for example, uses the emotion estimation function to analyze the emotions the user has when receiving feedback in real time. As a result, feedback that elicits positive emotions is provided. The progress confirmation unit also adjusts the content and format of the feedback based on the user's emotion data. For example, it provides encouraging words or success stories to elicit positive emotions. The progress confirmation unit also uses the emotion estimation function to analyze the emotions the user has when receiving feedback and provide feedback that elicits positive emotions. For example, it provides feedback at a time when the user is most relaxed. As a result, by analyzing the emotions the user has when receiving feedback and providing feedback that elicits positive emotions, motivation for learning is improved.

[0061] The learning resource providing unit can suggest optimal learning resources based on the user's learning history and current learning situation. In the learning resource providing unit, for example, the generation AI analyzes the user's learning history and suggests learning resources that are optimal for the current learning situation. For example, it suggests reference books and video lectures on areas in which the user is weak. The learning resource providing unit also builds a system that suggests optimal learning resources based on the user's learning data. For example, it suggests resources that will allow the user to study most effectively. In the learning resource providing unit, the generation AI analyzes the user's learning progress and suggests optimal learning resources. For example, it suggests resources related to the topic the user should study next. In this way, learning efficiency is improved by suggesting optimal learning resources based on the user's learning history and current learning situation.

[0062] The learning resource providing unit can suggest resources that match the user's learning style. For example, the learning resource providing unit uses a generation AI to analyze the user's learning style and suggest learning resources based on that. For example, for a visual user, it suggests learning materials that make extensive use of diagrams and graphs. The learning resource providing unit also builds a system that suggests resources that match the user's learning style based on the user's learning data. For example, it suggests audio lectures and podcasts for an auditory user. The learning resource providing unit also uses a generation AI to analyze the user's learning style and suggest learning resources based on that. For example, it suggests practical exercises and hands-on activities for a tactile user. In this way, learning effectiveness is maximized by suggesting resources that match the user's learning style.

[0063] The learning resource providing unit can use the emotion estimation function to suggest resources that allow the user to study in the most relaxed manner. For example, the learning resource providing unit uses the emotion estimation function to identify resources that allow the user to study in the most relaxed manner. For example, it suggests resources that include specific music or environmental sounds. The learning resource providing unit also builds a system that suggests resources that allow the user to study in the most relaxed manner based on the user's emotion data. For example, it suggests that the user study during a time period when the user is most relaxed. The learning resource providing unit also uses the emotion estimation function to identify resources that allow the user to study in the most relaxed manner and suggest those resources. For example, it allows the user to study in the most relaxing environment. In this way, suggesting resources that allow the user to study in the most relaxed manner improves continuity of learning.

[0064] The motivation improvement unit can suggest individually customized rewards and titles based on the user's learning history and current learning situation. In the motivation improvement unit, for example, the generation AI analyzes the user's learning history and suggests rewards and titles according to the current learning situation. For example, it suggests titles and rewards to be given when a specific goal is achieved. The motivation improvement unit also builds a system that suggests individually customized rewards and titles based on the user's learning data. For example, it suggests rewards and titles that the user finds most motivating. In the motivation improvement unit, the generation AI analyzes the user's learning progress and suggests individually customized rewards and titles. For example, it suggests rewards and titles for the next goal the user should achieve. In this way, by suggesting individually customized rewards and titles based on the user's learning history and current learning situation, motivation for learning is improved.

[0065] The motivation improvement unit can suggest gamification elements that match the user's learning style. For example, the generation AI in the motivation improvement unit analyzes the user's learning style and suggests gamification elements based on that. For example, it suggests visual rewards and titles for visual users. The motivation improvement unit also builds a system that suggests gamification elements that match the user's learning style based on the user's learning data. For example, it suggests audio rewards and titles for auditory users. The motivation improvement unit also analyzes the user's learning style using the generation AI and suggests gamification elements based on that. For example, it suggests practical rewards and titles for tactile users. In this way, by suggesting gamification elements that match the user's learning style, motivation for learning is improved.

[0066] The motivation improvement unit can use the emotion estimation function to identify the reward or title that the user finds most motivating and suggest it. The motivation improvement unit, for example, uses the emotion estimation function to identify the reward or title that the user finds most motivating. For example, it identifies the reward or title that the user finds most positive. The motivation improvement unit also identifies the reward or title that the user finds most motivating based on the user's emotion data and suggests it. For example, it suggests rewards or titles at times when the user is most relaxed. The motivation improvement unit also uses the emotion estimation function to identify the reward or title that the user finds most motivating and suggest it. For example, it suggests rewards or titles that the user finds most enjoyable. In this way, the motivation for learning is improved by identifying the reward or title that the user finds most motivating and suggesting it.

[0067] The test question generation unit can generate individually customized test questions based on the user's learning history and current learning situation. For example, the test question generation unit uses a generation AI to analyze the user's past answer data and identify areas of weakness and areas that should be focused on. This generates test questions that are optimal for the user. The test question generation unit also generates test questions that match the user's current learning situation based on the user's learning history. For example, it generates test questions that include many questions for reviewing recently learned content. The test question generation unit also uses a generation AI to analyze the user's learning progress and generate individually customized test questions. For example, it generates questions with difficulty adjusted according to the user's level of understanding. This maximizes the learning effect by generating individually customized test questions based on the user's learning history and current learning situation.

[0068] The test question generation unit can analyze the user's answer patterns, identify answer trends and mistake patterns, and generate test questions based on them. In the test question generation unit, for example, the generation AI analyzes the user's past answer data and identifies answer trends and mistake patterns. This generates test questions that include many questions that the user often gets wrong. The test question generation unit also generates test questions to prevent specific mistakes based on the user's answer patterns. For example, it generates questions to prevent the user from making the same type of mistake repeatedly. In the test question generation unit, the generation AI analyzes the user's answer data and identifies answer trends and mistake patterns. This generates test questions to reinforce concepts that the user finds difficult to understand. This improves learning effectiveness by analyzing the user's answer patterns, identifying answer trends and mistake patterns, and generating test questions based on them.

[0069] The test question generation unit can use the emotion estimation function to analyze the stress level felt by the user while answering questions and generate test questions for reducing stress. The test question generation unit, for example, uses the emotion estimation function to analyze the stress level felt by the user while answering questions in real time. As a result, test questions with adjusted difficulty are generated when stress is high. The test question generation unit also generates test questions for reducing stress based on the user's emotion data. For example, it generates questions with a question format that allows the user to relax or questions with no time limit. The test question generation unit also uses the emotion estimation function to analyze the stress level felt by the user while answering questions and generate test questions for reducing stress. For example, it allows the user to answer in an environment that is most relaxing. As a result, analyzing the stress level felt by the user while answering questions and generating test questions for reducing stress improves continuity of learning.

[0070] The test question generation unit can generate cross-disciplinary test questions that combine knowledge from different academic fields. For example, the test question generation unit generates test questions that combine knowledge from different academic fields using a generation AI. For example, it generates questions that combine knowledge of physics and chemistry. The test question generation unit also generates test questions that combine knowledge from different academic fields based on a user's learning history. For example, it generates questions that combine knowledge of history and geography. The test question generation unit also generates cross-disciplinary test questions that combine knowledge from different academic fields using a generation AI. For example, it generates questions that combine knowledge of mathematics and economics. In this way, the breadth of learning is broadened by generating cross-disciplinary test questions that combine knowledge from different academic fields.

[0071] The test question generation unit generates questions similar to past test questions based on the user's learning history, thereby enhancing the effectiveness of review. For example, the test question generation unit uses a generation AI to analyze the user's past test questions and generate similar questions. This allows the user to review content that they have previously studied. The test question generation unit also generates questions similar to past test questions based on the user's learning history. For example, it generates questions related to the same theme or topic. The test question generation unit also uses a generation AI to analyze the user's learning progress and generate questions similar to past test questions. This allows the user to enhance the effectiveness of review. This enhances the effectiveness of review by generating questions similar to past test questions based on the user's learning history.

[0072] The test question generation unit can use the emotion estimation function to generate test questions related to themes that the user is most interested in. For example, the test question generation unit uses the emotion estimation function to identify themes that the user is most interested in and generate test questions related to those themes. For example, questions related to topics that the user is interested in are generated. The test question generation unit also generates test questions related to themes that the user is most interested in based on the user's emotion data. For example, questions related to themes that the user enjoys the most. The test question generation unit also uses the emotion estimation function to generate test questions related to themes that the user is most interested in. For example, questions related to fields that the user is most interested in are generated. In this way, generating test questions related to themes that the user is most interested in improves motivation to study.

[0073] The motivation improvement unit can propose cross-disciplinary rewards and titles that combine knowledge from different academic fields. For example, the generation AI in the motivation improvement unit proposes rewards and titles that combine knowledge from different academic fields. For example, it proposes rewards and titles that combine knowledge of physics and chemistry. The motivation improvement unit also proposes rewards and titles that combine knowledge from different academic fields based on the user's learning history. For example, it proposes rewards and titles that combine knowledge of history and geography. The motivation improvement unit also proposes cross-disciplinary rewards and titles that combine knowledge from different academic fields. For example, it proposes rewards and titles that combine knowledge of mathematics and economics. In this way, the scope of learning is broadened by proposing cross-disciplinary rewards and titles that combine knowledge from different academic fields.

[0074] The motivation improvement unit can suggest goals similar to goals achieved in the past based on the user's learning history. For example, the motivation improvement unit uses a generation AI to analyze the user's past learning history and suggest similar goals. This increases the user's motivation to achieve goals they have achieved in the past again. The motivation improvement unit also builds a system that suggests goals similar to goals achieved in the past based on the user's learning data. For example, it re-suggests learning methods that have been successful for the user in the past. The motivation improvement unit also uses a generation AI to analyze the user's learning progress and suggest goals similar to goals achieved in the past. This allows the user to progress with their learning effectively. This improves learning motivation by suggesting goals similar to goals they have achieved in the past based on the user's learning history.

[0075] The motivation improvement unit can use the emotion estimation function to suggest rewards and titles related to themes that the user is most interested in. For example, the motivation improvement unit uses the emotion estimation function to identify themes that the user is most interested in and suggest rewards and titles related to those themes. For example, it suggests rewards and titles related to topics that the user is interested in. The motivation improvement unit also builds a system that suggests rewards and titles related to themes that the user is most interested in based on the user's emotion data. For example, it suggests rewards and titles related to themes that the user enjoys the most. The motivation improvement unit also uses the emotion estimation function to identify rewards and titles related to themes that the user is most interested in and suggests those rewards and titles. For example, it suggests rewards and titles related to fields that the user is most interested in. In this way, by suggesting rewards and titles related to themes that the user is most interested in, motivation for learning is improved.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The learning status analysis unit analyzes not only the user's learning history but also their daily behavioral patterns to suggest optimal study times for the user. For example, the generation AI analyzes data obtained from the user's smartwatch or fitness tracker to understand the user's sleep patterns and exercise volume. This identifies the time periods when the user can concentrate best and suggests that they study during those times. The learning status analysis unit also analyzes the user's smartphone usage history and calendar app data to understand their daily schedule. This identifies the optimal time periods for the user to study and incorporates study plans into those times. The generation AI also analyzes the user's eating and caffeine intake patterns to identify times when energy levels are high. This suggests the most efficient time periods for the user to study. This improves learning efficiency by analyzing the user's daily behavioral patterns and suggesting optimal study times.

[0078] The learning plan suggestion unit can analyze a user's learning style and propose a learning plan based on that. For example, the generation AI can analyze a user's past learning history and survey results to determine which learning style is best suited to the user. For example, for a visual user, it can propose learning materials that make extensive use of diagrams and graphs. The learning plan suggestion unit can also analyze the effectiveness of learning resources used by the user in the past to determine which resources were most effective. For example, it can propose audio lectures and podcasts to an auditory user. The learning plan suggestion unit can also propose a customized learning plan based on user feedback according to the user's learning style. For example, it can incorporate practical exercises and hands-on activities for a tactile user. This maximizes learning effectiveness by proposing a learning plan based on the user's learning style.

[0079] The study plan suggestion unit uses the emotion estimation function to analyze the user's emotions while studying and propose a low-stress study plan. For example, the generation AI analyzes the user's facial expressions and voice tone to grasp the user's emotional state while studying in real time. This allows it to propose a relaxing study method when stress is high. The study plan suggestion unit also monitors the user's heart rate and electrodermal activity to measure stress levels. This allows it to propose study times when stress is low. The study plan suggestion unit also uses the emotion estimation function to identify the environment in which the user can study most relaxed. For example, it suggests studying in a quiet place or while listening to specific music. This allows it to analyze the user's emotions while studying and propose a low-stress study plan, thereby improving continuity in study.

[0080] The progress confirmation unit can analyze the user's answer results in detail and provide feedback on specific areas for improvement. For example, the generation AI analyzes the user's answer results and identifies specific areas for improvement. For example, it points out errors in the answer to a specific problem and suggests the correct answer method. The progress confirmation unit also analyzes in detail which parts the user made mistakes in based on the user's answer data and provides feedback on specific areas for improvement. For example, it points out calculation errors or conceptual misunderstandings. The progress confirmation unit also analyzes the user's answer results in detail and provides feedback on specific areas for improvement. For example, it analyzes the answer process in detail and identifies which step the error was made in. In this way, the learning effect is improved by analyzing the user's answer results in detail and providing feedback on specific areas for improvement.

[0081] The progress confirmation unit can visualize the user's learning progress in graphs and charts and provide intuitively understandable feedback. For example, the generation AI analyzes the user's learning progress and visualizes it in graphs and charts. For example, it displays the trend in correct answer rate and the distribution of study time. The progress confirmation unit also provides tools to visualize progress based on the user's learning data. For example, it displays learning progress in pie charts and bar graphs. The progress confirmation unit also analyzes the user's learning progress in the generation AI and provides visually easy-to-understand feedback. For example, it displays learning progress in a timeline format. This visualizes the user's learning progress and provides intuitively understandable feedback, thereby improving learning motivation.

[0082] The progress confirmation unit can use the emotion estimation function to analyze the emotions the user has when receiving feedback and provide feedback that elicits positive emotions. For example, the emotion estimation function is used to analyze the emotions the user has when receiving feedback in real time. As a result, feedback that elicits positive emotions is provided. The progress confirmation unit also adjusts the content and format of the feedback based on the user's emotion data. For example, it provides encouraging words or success stories that elicit positive emotions. The progress confirmation unit also uses the emotion estimation function to analyze the emotions the user has when receiving feedback and provide feedback that elicits positive emotions. For example, it provides feedback at a time when the user is most relaxed. As a result, by analyzing the emotions the user has when receiving feedback and providing feedback that elicits positive emotions, motivation for learning is improved.

[0083] The learning resource providing unit can suggest optimal learning resources based on the user's learning history and current learning situation. For example, the generation AI analyzes the user's learning history and suggests learning resources that are optimal for the current learning situation. For example, it may suggest reference books or video lectures related to areas in which the user is weak. The learning resource providing unit also builds a system that suggests optimal learning resources based on the user's learning data. For example, it may suggest resources that will allow the user to study most effectively. The learning resource providing unit also uses the generation AI to analyze the user's learning progress and suggest optimal learning resources. For example, it may suggest resources related to the topic the user should study next. In this way, learning efficiency is improved by suggesting optimal learning resources based on the user's learning history and current learning situation.

[0084] The learning resource provision unit can suggest resources that match the user's learning style. For example, the generation AI analyzes the user's learning style and suggests learning resources based on that. For example, for a visual user, it would suggest learning materials that make extensive use of diagrams and graphs. The learning resource provision unit also builds a system that suggests resources that match the user's learning style based on the user's learning data. For example, it would suggest audio lectures and podcasts for an auditory user. The learning resource provision unit also uses the generation AI to analyze the user's learning style and suggest learning resources based on that. For example, it would suggest practical exercises and hands-on activities for a tactile user. In this way, learning effectiveness is maximized by suggesting resources that match the user's learning style.

[0085] The learning resource providing unit can use the emotion estimation function to suggest resources that allow the user to study in the most relaxed manner. For example, the emotion estimation function is used to identify resources that allow the user to study in the most relaxed manner. For example, resources that include specific music or environmental sounds are suggested. The learning resource providing unit also builds a system that suggests resources that allow the user to study in the most relaxed manner based on the user's emotion data. For example, it suggests that the user study during a time period when the user is most relaxed. The learning resource providing unit also uses the emotion estimation function to identify resources that allow the user to study in the most relaxed manner and suggest those resources. For example, it allows the user to study in the most relaxing environment. In this way, suggesting resources that allow the user to study in the most relaxed manner improves continuity of learning.

[0086] The motivation improvement unit can use the emotion estimation function to identify and suggest the reward or title that the user finds most motivating. For example, the emotion estimation function is used to identify the reward or title that the user finds most motivating. For example, the reward or title that the user finds most positive is identified. The motivation improvement unit also identifies and suggests the reward or title that the user finds most motivating based on the user's emotion data. For example, it suggests rewards or titles at times when the user is most relaxed. The motivation improvement unit also uses the emotion estimation function to identify and suggest the reward or title that the user finds most motivating. For example, it suggests rewards or titles that the user finds most enjoyable. In this way, the reward or title that the user finds most motivating is identified and suggested, thereby improving learning motivation.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The learning situation analysis unit is equipped with a generation AI and analyzes the user's learning situation. For example, the generation AI analyzes the user's learning history and current learning situation to identify weak areas and areas that should be studied intensively. Step 2: The study plan suggestion unit proposes an optimal study plan based on the user's study status analyzed by the study status analysis unit. For example, the generation AI generates a study plan based on the user's study history and current study status, tailored to their individual learning goals and learning style. Step 3: The test question generation unit generates test questions based on the study plan proposed by the study plan proposal unit. For example, the generation AI generates test questions that include many questions related to areas in which the user is weak. Step 4: The progress confirmation unit analyzes the answers to the test questions answered by the user and checks the progress of the user's learning. For example, the generation AI analyzes the correct answer rate and answer time to evaluate the progress of the user's learning. Step 5: The learning resource provider provides learning resources according to the user's learning situation. For example, the generation AI suggests reference books and video lectures on areas in which the user is weak. Step 6: The motivation improvement unit proposes rewards and titles to improve the user's motivation to learn. For example, the generation AI introduces gamification elements that give rewards and titles according to the user's learning progress.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 situation analysis unit equipped with generation AI, a learning plan suggestion unit that suggests an optimal learning plan based on the user's learning situation analyzed by the learning situation analysis unit; a test question generation unit that generates test questions based on the study plan proposed by the study plan proposal unit; a progress confirmation unit that analyzes the answers to the test questions answered by the user and confirms the progress of learning; a learning resource providing unit that provides learning resources according to the user's learning situation; a motivation improvement unit that proposes rewards and titles to improve the user's motivation to learn. A system characterized by:

2. The learning situation analysis unit Analyzes not only the user's learning history but also their daily behavior patterns to suggest the optimal study time for the user 2. The system of claim 1.

3. The learning plan suggestion unit Analyze the user's learning style and propose a learning plan based on that.

2. The system of claim 1.

4. The learning plan suggestion unit Analyze the user's emotions while studying and propose a less stressful study plan 2. The system of claim 1.

5. The progress confirmation unit Analyze the user's answers in detail and provide feedback on specific areas for improvement.

2. The system of claim 1.

6. The progress confirmation unit Visualize the user's learning progress with graphs and charts to provide intuitive feedback 2. The system of claim 1.

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