system

Abstract

The system according to this embodiment aims to enable learning while on the go or doing household chores, and to track the learner's progress in real time. [Solution] The system according to the embodiment comprises a reception unit, a question-presenting unit, an analysis unit, a feedback unit, and a tracking unit. The reception unit requests a quiz question. The question-presenting unit presents the quiz question requested by the reception unit orally. The analysis unit analyzes the learner's answer. The feedback unit provides feedback based on the results analyzed by the analysis unit. The tracking unit tracks the learner's progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=3照]]In the conventional technology, there is a problem that it is difficult to learn while moving or doing housework, and it is difficult to track the progress of the learner in real time.

[0005] <00照0028>The system according to the embodiment aims to enable learning even while moving or doing housework and to track the progress of the learner in real time. [[ID=照0]] 照照

Means for Solving the Problems

[0006] Note: There seems to be some incorrect or unclear tags in the original text like "<照 ", "<照 ", etc. I translated them as they are but they might need to be corrected in the original source. Also, the translation of "特開2022 - 180282号公報" as "Japanese Patent Application Laid-Open No. 2022-180282" is a common way in patent translation.The system according to this embodiment comprises a reception unit, a question-presenting unit, an analysis unit, a feedback unit, and a tracking unit. The reception unit requests the presentation of a quiz. The question-presenting unit presents the quiz requested by the reception unit orally. The analysis unit analyzes the learner's answers. The feedback unit provides feedback based on the results analyzed by the analysis unit. The tracking unit tracks the learner's progress. [Effects of the Invention]

[0007] The system according to this embodiment allows learning to be done while on the go or doing household chores, and the learner's progress can be tracked in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interactive learning support system according to an embodiment of the present invention is a system that uses a voice assistant to orally present quizzes for certification exam preparation, and learners answer orally. This system starts when a learner requests a quiz from the voice assistant. The voice assistant presents the quiz orally, and the learner answers orally. The voice assistant uses speech recognition technology to analyze the learner's answers and provides accurate feedback. Furthermore, an AI agent automatically tracks the learner's progress and adjusts and suggests the most suitable questions and content in real time for each individual. This mechanism makes it possible to learn while on the go or doing housework, thereby enhancing the effectiveness of learning. For example, when a learner requests a quiz from the voice assistant, they can specify a particular field or difficulty level. For example, they might request, "Please give me some basic math problems." This information is entered into the voice assistant. Next, the voice assistant orally presents the quiz. For example, it might ask, "Here's the next question. What is 2 + 2?" The learner answers orally, and the voice assistant analyzes the answer using speech recognition technology. For example, if a learner answers "4," the voice assistant analyzes the answer and determines whether it is correct. Furthermore, the AI ​​agent automatically tracks the learner's progress. For instance, if a learner makes many mistakes in a particular area, the AI ​​agent generates additional quizzes in that area and presents them at the appropriate time. This helps to strengthen the learner's weak areas. This system allows learners to study while commuting or doing household chores. For example, they can use the voice assistant to take quizzes during their commute. In addition, the AI ​​agent provides real-time evaluations and personalized feedback and learning plans to enhance the effectiveness of learning. This interactive learning support system enables interactive learning by allowing learners to request quizzes and answer verbally.

[0029] The interactive learning support system according to the embodiment comprises a reception unit, a question-presenting unit, an analysis unit, a feedback unit, and a tracking unit. The reception unit receives requests from learners for quizzes. For example, learners can request quizzes by specifying a particular field or difficulty level. For example, a learner might request, "Please give me some basic math problems." This information is entered into the reception unit. The question-presenting unit verbally presents the quiz requested by the reception unit. For example, the question-presenting unit might present a quiz such as, "Here's the next question. What is 2 + 2?" The learner answers verbally, and the question-presenting unit analyzes the answer using speech recognition technology. The analysis unit analyzes the learner's answer. For example, if the learner answers "4," the analysis unit analyzes the answer and determines whether it is correct. The feedback unit provides feedback based on the results analyzed by the analysis unit. The feedback unit provides feedback such as "That's correct" if the learner answers correctly, and "Unfortunately, the correct answer is 4" if they answer incorrectly. The tracking unit tracks the learner's progress. For example, if the tracking unit finds that a learner has made many mistakes in a particular area, it generates additional quizzes in that area and presents them at an appropriate time. This helps to strengthen the learner's weak areas. As a result, the interactive learning support system according to this embodiment enables interactive learning by allowing learners to request quizzes and answer them verbally.

[0030] The reception desk receives quiz requests from learners. For example, learners can request quizzes in specific fields or difficulty levels. Specifically, learners can request things like, "Please give me basic math problems," or "Please give me intermediate-level history problems." The reception desk receives these requests and collects information to select appropriate quizzes. Learner requests are entered into the reception desk via voice or text input. In the case of voice input, speech recognition technology is used to convert the request into text data and analyze it. In the case of text input, the system analyzes the text entered by the learner directly. The reception desk provides information to select the optimal quiz, taking into account the learner's past learning history and current learning status. For example, it can select quizzes of appropriate difficulty and fields based on areas the learner has struggled with in the past or content they have recently studied. This allows the reception desk to provide quizzes tailored to the learner's needs and support effective learning. Furthermore, the reception desk can save learner requests in a database and use them for future learning support. This enables centralized management of learner learning history and provides support that meets individual learning needs.

[0031] The question-setting unit verbally presents quizzes requested by the reception unit. For example, the question-setting unit might ask, "Here's the next question: What is 2 + 2?" Specifically, the question-setting unit selects an appropriate quiz based on the learner's request and verbally presents the quiz using speech synthesis technology. The speech synthesis technology can read the quiz aloud with natural pronunciation and intonation, providing a user-friendly interface for learners. Learners verbally answer the presented quiz, and the question-setting unit analyzes the answer using speech recognition technology. The speech recognition technology converts the learner's utterance into text data and sends it to the analysis unit. The question-setting unit uses an advanced speech recognition algorithm to accurately recognize the learner's answer, taking into account background noise and differences in pronunciation during the analysis. This allows the question-setting unit to accurately recognize the learner's answer and send it to the analysis unit. Furthermore, the question-setting unit can provide immediate feedback on the learner's answer. For example, if a learner answers correctly, the system provides feedback such as "That's correct," and if they answer incorrectly, it provides feedback such as "Unfortunately, the correct answer is 4." This allows the question-setting system to provide immediate feedback to learners, thereby enhancing learning effectiveness.

[0032] The analysis unit analyzes the learner's responses. For example, if a learner answers "4," the analysis unit analyzes that answer and determines whether it is correct. Specifically, the analysis unit uses speech recognition technology to convert the learner's response into text data and compares it with the correct answer database. The correct answer database contains the correct answers for each quiz, and the analysis unit determines whether the learner's answer matches the correct answer database. If the learner's answer is correct, the analysis unit determines it as "correct," and if it is incorrect, it determines it as "incorrect." Furthermore, the analysis unit can analyze the trends and patterns of the learner's responses to understand the learner's level of understanding and areas of weakness. For example, if a learner frequently makes mistakes in a particular area, the analysis unit determines that their understanding of that area is low and provides additional quizzes. The analysis unit can also evaluate the learner's response time and accuracy to understand the learner's progress. This allows the analysis unit to understand the learner's level of understanding and progress in real time and provide appropriate feedback. Furthermore, the analysis unit can accumulate learner response data and evaluate long-term learning effectiveness. This allows the analysis unit to provide support tailored to individual learning needs based on the learner's learning history.

[0033] The feedback unit provides feedback based on the results analyzed by the analysis unit. For example, if the learner answers correctly, the feedback unit will say, "That's correct," and if they answer incorrectly, it will say, "Unfortunately, the correct answer is 4." Specifically, the feedback unit provides appropriate feedback to the learner based on the analysis results received from the analysis unit. The feedback is provided in audio or text format, instantly conveying the results to the learner. The feedback unit can provide individualized feedback according to the learner's level of understanding and progress. For example, if a learner frequently makes mistakes in a particular area, it can provide additional quizzes in that area and offer advice to improve their understanding. The feedback unit can also evaluate the learner's response time and accuracy, and grasp the learner's progress. This allows the feedback unit to grasp the learner's level of understanding and progress in real time and provide appropriate feedback. Furthermore, the feedback unit can accumulate learner feedback and evaluate long-term learning effectiveness. This enables the feedback unit to provide support tailored to individual learning needs based on the learner's learning history.

[0034] The tracking unit tracks the learner's progress. For example, if a learner makes many mistakes in a particular area, the tracking unit generates additional quizzes in that area and presents them at the appropriate time. Specifically, the tracking unit accumulates the learner's answer data and understands the learner's progress in real time. The tracking unit analyzes the learner's answer trends and patterns to understand the learner's level of understanding and areas of weakness. For example, if a learner frequently makes mistakes in a particular area, it determines that their understanding of that area is low and presents additional quizzes. The tracking unit also evaluates the learner's answer time and accuracy to understand their progress. This allows the tracking unit to understand the learner's level of understanding and progress in real time and provide appropriate feedback. Furthermore, the tracking unit accumulates the learner's answer data and can evaluate the long-term learning effect. This allows the tracking unit to provide support tailored to the learner's individual learning needs based on their learning history.

[0035] The reception section includes a specification section where users can specify a particular field or difficulty level. The specification section allows learners to request quizzes by specifying a particular field or difficulty level. For example, a learner might request, "Please give me some basic math problems." This information is entered into the specification section. This allows learners to request quizzes by specifying a particular field or difficulty level.

[0036] The tracking unit includes a generation unit that generates additional quizzes based on the learner's progress. For example, if a learner makes many mistakes in a particular area, the generation unit will generate additional quizzes in that area and present them at an appropriate time. This allows for the generation and presentation of additional quizzes according to the learner's progress.

[0037] The feedback unit provides individually customized feedback based on the learner's answers. For example, if the learner answers correctly, the feedback unit will say, "That's correct," and if they answer incorrectly, it will say, "Unfortunately, the correct answer is 4." This allows for the provision of individually optimized feedback based on the learner's answers.

[0038] The tracking unit includes a provisioning unit that provides learning plans based on the learner's progress. For example, if a learner makes many mistakes in a particular area, the provisioning unit will provide a learning plan for that area. This allows learning plans to be provided according to the learner's progress.

[0039] The analysis unit analyzes the learner's response using speech recognition technology. For example, if the learner answers "4," the analysis unit analyzes that answer using speech recognition technology and determines whether it is correct. In this way, the learner's response can be accurately analyzed using speech recognition technology.

[0040] The reception desk analyzes the learner's past quiz request history and selects the optimal question format. For example, the reception desk prioritizes quiz formats that the learner has previously requested and preferred. For example, the reception desk avoids quiz formats that the learner has previously struggled with. For example, the reception desk determines the optimal frequency of question presentation based on the learner's past request history. In this way, by analyzing past quiz request history, the reception desk can select the most suitable question format for each learner.

[0041] The reception desk filters quiz requests based on the learner's current learning status and areas of interest. For example, the reception desk prioritizes quizzes related to the area the learner is currently studying. For example, the reception desk filters quizzes based on the learner's areas of interest. For example, the reception desk presents quizzes of appropriate difficulty according to the learner's learning progress. This allows for the presentation of more appropriate quizzes by filtering them based on the learner's current learning status and areas of interest.

[0042] The reception desk prioritizes presenting quizzes that are highly relevant to the learner's geographical location when a quiz request is received. For example, if the learner is in a specific region, the reception desk will present quizzes related to that region. If the learner is traveling, the reception desk will present quizzes related to their travel destination. If the learner is at home, the reception desk will present quizzes related to their home. By presenting highly relevant quizzes based on the learner's geographical location, more effective learning becomes possible.

[0043] The reception desk analyzes the learner's social media activity when a quiz request is received and presents relevant quizzes. For example, the reception desk may present quizzes related to topics the learner has shown interest in on social media. For example, the reception desk may present quizzes related to accounts the learner follows on social media. For example, the reception desk may present quizzes related to content the learner has shared on social media. In this way, relevant quizzes can be presented by analyzing the learner's social media activity.

[0044] The question-setting unit adjusts the level of detail in quiz questions based on their importance. For example, it provides detailed explanations for important questions, and concise explanations for less important questions. It also adjusts the level of detail according to the learner's level of understanding. By adjusting the level of detail based on the importance of each question, the system can provide learners with the most suitable quiz.

[0045] The question-generating unit applies different question-generating algorithms depending on the category of the question when generating quiz questions. For example, in the case of a mathematics question, the unit applies an algorithm that emphasizes the calculation process. For example, in the case of a history question, the unit applies an algorithm that presents questions in chronological order. For example, in the case of a science question, the unit applies an algorithm that emphasizes experimental results. By applying different question-generating algorithms depending on the category of the question, the unit can provide learners with the most optimal quiz.

[0046] The question-setting department determines the priority of questions based on when they were submitted. For example, it might prioritize questions submitted most recently, or postpone questions submitted later. It might also present questions at an appropriate time depending on when they were submitted. By prioritizing questions based on their submission date, the department can provide learners with the most optimal quiz.

[0047] The question-setting unit adjusts the order of questions based on their relevance. For example, it might present highly relevant questions consecutively, or less relevant questions later. It might also prioritize highly relevant questions based on the learner's level of understanding. By adjusting the order of questions based on their relevance, the system can provide learners with the most optimal quiz.

[0048] The analysis unit improves the accuracy of the analysis by considering the relationships between learners during response analysis. For example, if learners are studying in a group, the analysis unit considers the responses of the entire group. For example, if learners are studying individually, the analysis unit gives more emphasis to individual responses. The analysis unit improves the accuracy of the analysis based on the relationships between learners. In this way, the accuracy of the analysis is improved by considering the relationships between learners.

[0049] The analysis unit performs analysis while considering the learner's attribute information during response analysis. For example, the analysis unit performs analysis using appropriate criteria based on the learner's age. For example, the analysis unit improves the accuracy of the analysis based on the learner's learning history. For example, the analysis unit provides analysis results while considering the learner's attribute information. This improves the accuracy of the analysis by considering the learner's attribute information.

[0050] The analysis unit considers the geographical distribution of learners when analyzing responses. For example, if learners are concentrated in a particular area, the analysis unit considers the characteristics of that area. For example, if learners are widely distributed, the analysis unit considers the characteristics of each region. For example, the analysis unit provides analysis results based on the geographical distribution of learners. This improves the accuracy of the analysis by considering the geographical distribution of learners.

[0051] The analysis unit improves the accuracy of the analysis by referring to relevant literature during response analysis. For example, the analysis unit analyzes by referring to literature related to the learner's responses. For example, the analysis unit searches for relevant literature based on the learner's responses to improve the accuracy of the analysis. For example, the analysis unit compares the learner's responses with relevant literature and provides analysis results. In this way, the accuracy of the analysis is improved by referring to relevant literature.

[0052] The feedback unit optimizes the current feedback by referring to past feedback data when providing feedback. For example, the feedback unit optimizes the current feedback based on feedback the learner has received in the past. For example, the feedback unit analyzes the learner's past feedback data to provide optimal feedback. For example, the feedback unit adjusts the current feedback by referring to the learner's past feedback history. This allows for the optimization of the current feedback by referring to past feedback data.

[0053] The feedback system applies different feedback methods depending on the learner's category when providing feedback. For example, it provides basic feedback to beginner learners, advanced feedback to intermediate learners, and expert feedback to advanced learners. By applying different feedback methods to each learner's category, more effective feedback becomes possible.

[0054] The feedback unit analyzes changes in feedback based on the learner's submission timing when providing feedback. For example, the feedback unit provides prompt feedback on the learner's most recent submission. For example, the feedback unit provides detailed feedback on the learner's past submissions. For example, the feedback unit adjusts the content of the feedback based on the learner's submission timing. This allows for more appropriate feedback by analyzing changes in feedback based on the learner's submission timing.

[0055] The feedback unit analyzes the feedback by referring to relevant market data when providing it. For example, the feedback unit provides feedback by referring to market data related to the learner's responses. For example, the feedback unit analyzes relevant market data based on the learner's responses to improve the accuracy of the feedback. For example, the feedback unit provides feedback by comparing the learner's responses with market data. This improves the accuracy of the feedback by referring to relevant market data.

[0056] The tracking unit predicts current progress by referring to past progress data during progress tracking. For example, the tracking unit predicts current progress based on the learner's past progress data. For example, the tracking unit analyzes the learner's past progress data to provide an optimal progress prediction. For example, the tracking unit predicts current progress by referring to the learner's past progress history. This allows for prediction of current progress by referring to past progress data.

[0057] The tracking unit applies different progress analysis methods to each learner category during progress tracking. For example, it applies basic progress analysis methods to beginner learners, advanced progress analysis methods to intermediate learners, and specialized progress analysis methods to advanced learners. By applying different progress analysis methods to each learner category, more effective progress management becomes possible.

[0058] The tracking unit analyzes changes in progress based on the learner's submission timing during progress tracking. For example, the tracking unit analyzes changes in progress based on the learner's most recent submitted progress data. For example, the tracking unit analyzes changes in progress based on the learner's past submitted progress data. For example, the tracking unit analyzes changes in progress based on the learner's submission timing. This allows for more appropriate progress management by analyzing changes in progress based on the learner's submission timing.

[0059] The tracking unit analyzes progress by referring to relevant market data during progress tracking. For example, the tracking unit analyzes progress by referring to market data related to learner progress data. For example, the tracking unit improves the accuracy of progress by analyzing relevant market data based on learner progress data. For example, the tracking unit analyzes progress by comparing learner progress data with market data. This improves the accuracy of progress by referring to relevant market data.

[0060] The designation unit selects the optimal designation method by referring to past designation data when specifying fields and difficulty levels. For example, the designation unit designates the optimal fields and difficulty levels based on the learner's past designation data. For example, the designation unit analyzes the learner's past designation data and provides the optimal designation method. For example, the designation unit designates the optimal fields and difficulty levels by referring to the learner's past designation history. In this way, the optimal fields and difficulty levels can be designated by referring to past designation data.

[0061] The designation function selects the optimal designation method when specifying fields and difficulty levels, taking into account the learner's geographical location. For example, if the learner is in a specific region, the designation function will specify fields and difficulty levels related to that region. For example, if the learner is traveling, the designation function will specify fields and difficulty levels related to the travel destination. For example, if the learner is at home, the designation function will specify fields and difficulty levels related to home. In this way, the optimal fields and difficulty levels can be specified by taking the learner's geographical location into consideration.

[0062] The generation unit selects the optimal generation method by referring to past generation data when generating additional quizzes. For example, the generation unit generates the optimal additional quiz based on the learner's past generation data. For example, the generation unit analyzes the learner's past generation data and provides the optimal generation method. For example, the generation unit generates the optimal additional quiz by referring to the learner's past generation history. In this way, the optimal additional quiz can be generated by referring to past generation data.

[0063] The generation unit selects the optimal generation method when generating additional quizzes, taking into account the learner's geographical location. For example, if the learner is in a specific region, the generation unit generates additional quizzes related to that region. For example, if the learner is traveling, the generation unit generates additional quizzes related to the travel destination. For example, if the learner is at home, the generation unit generates additional quizzes related to home. In this way, the optimal additional quizzes can be generated by taking the learner's geographical location into consideration.

[0064] The service provider selects the optimal delivery method by referring to past delivery data when providing a learning plan. For example, the service provider provides the optimal learning plan based on the learner's past delivery data. For example, the service provider analyzes the learner's past delivery data to provide the optimal delivery method. For example, the service provider provides the optimal learning plan by referring to the learner's past delivery history. In this way, the optimal learning plan can be provided by referring to past delivery data.

[0065] The service provider selects the optimal delivery method when providing learning plans, taking into account the learner's geographical location. For example, if the learner is in a specific region, the service provider will provide a learning plan related to that region. For example, if the learner is traveling, the service provider will provide a learning plan related to their travel destination. For example, if the learner is at home, the service provider will provide a learning plan related to their home. In this way, the service provider can provide the most suitable learning plan by taking into account the learner's geographical location.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The reception desk can analyze learners' past learning history and determine the optimal order in which quiz questions are presented. For example, by prioritizing questions that learners have struggled with in the past, it can promote learning that overcomes weaknesses. Conversely, by postponing questions in areas where learners excel, it can help balance learning. Furthermore, it is possible to adjust the difficulty level of the quizzes based on the learner's past learning history. This enables effective learning tailored to the individual needs of each learner.

[0068] The analysis unit can also consider the speed of the learner's response when analyzing their answers. For example, if a learner answers correctly quickly, it can be determined that they have a high level of understanding in that area, and the difficulty level of the next question can be increased. On the other hand, if a learner takes a long time to answer, it can be determined that their understanding in that area is low, and additional explanations or hints can be provided. Furthermore, based on the speed of the response, it is possible to estimate the learner's concentration and fatigue level and suggest a break at an appropriate time. This allows for flexible learning support tailored to the learner's level of understanding and concentration.

[0069] The feedback system can also consider the learner's learning style when providing feedback based on their responses. For example, visual learners can be provided with feedback using diagrams and graphs, while auditory learners can receive audio feedback. Furthermore, practical learners can receive feedback that includes specific examples and practical advice. This allows for the provision of effective feedback tailored to each learner's individual learning style.

[0070] The tracking unit can also take into account the learner's lifestyle when tracking their progress. For example, if a learner has a morning routine, quizzes can be presented during the morning hours to maximize their concentration. Conversely, if a learner has a night owl routine, quizzes can be presented during the evening hours to enhance learning effectiveness. It can also suggest appropriate break times and study times based on the learner's lifestyle. This allows for the provision of effective learning support tailored to the learner's lifestyle.

[0071] The reception desk can analyze learners' past learning history and determine the optimal timing for presenting quizzes. For example, if a learner has shown high performance during a specific time period in the past, presenting quizzes during that time can enhance learning effectiveness. Conversely, if a learner has shown low performance during a specific time period in the past, quizzes can be avoided during that time. Furthermore, it is possible to suggest appropriate break times based on the learner's past learning history. This enables effective learning tailored to the individual needs of each learner.

[0072] The feedback system can also consider the learner's learning objectives when providing feedback based on their responses. For example, if a learner's goal is to pass a specific exam, feedback related to that exam can be provided. On the other hand, if a learner's goal is to acquire a specific skill, feedback related to that skill can be provided. Furthermore, if a learner's goal is self-development, feedback related to self-development can be provided. This allows for the provision of effective feedback tailored to each learner's individual learning objectives.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reception desk receives quiz requests from learners. Learners can request quizzes by specifying a particular field or difficulty level. For example, a learner might request "basic math problems." This information is entered into the reception desk. Step 2: The question-setting team verbally presents the quiz requested by the reception team. For example, they might ask, "Here's the next question: What is 2 + 2?" Step 3: The analysis unit analyzes the learner's response. For example, if the learner answers "4", the unit analyzes that response and determines whether it is correct. Step 4: The feedback unit provides feedback based on the results analyzed by the analysis unit. For example, if the learner answers correctly, it provides feedback saying "That's correct," and if they answer incorrectly, it provides feedback saying "Unfortunately, the correct answer is 4." Step 5: The tracking unit tracks the learner's progress. For example, if a learner makes many mistakes in a particular area, it generates additional quizzes in that area and presents them at the appropriate time. This helps to strengthen the learner's weak areas.

[0075] (Example of form 2) An interactive learning support system according to an embodiment of the present invention is a system that uses a voice assistant to orally present quizzes for certification exam preparation, and learners answer orally. This system starts when a learner requests a quiz from the voice assistant. The voice assistant presents the quiz orally, and the learner answers orally. The voice assistant uses speech recognition technology to analyze the learner's answers and provides accurate feedback. Furthermore, an AI agent automatically tracks the learner's progress and adjusts and suggests the most suitable questions and content in real time for each individual. This mechanism makes it possible to learn while on the go or doing housework, thereby enhancing the effectiveness of learning. For example, when a learner requests a quiz from the voice assistant, they can specify a particular field or difficulty level. For example, they might request, "Please give me some basic math problems." This information is entered into the voice assistant. Next, the voice assistant orally presents the quiz. For example, it might ask, "Here's the next question. What is 2 + 2?" The learner answers orally, and the voice assistant analyzes the answer using speech recognition technology. For example, if a learner answers "4," the voice assistant analyzes the answer and determines whether it is correct. Furthermore, the AI ​​agent automatically tracks the learner's progress. For instance, if a learner makes many mistakes in a particular area, the AI ​​agent generates additional quizzes in that area and presents them at the appropriate time. This helps to strengthen the learner's weak areas. This system allows learners to study while commuting or doing household chores. For example, they can use the voice assistant to take quizzes during their commute. In addition, the AI ​​agent provides real-time evaluations and personalized feedback and learning plans to enhance the effectiveness of learning. This interactive learning support system enables interactive learning by allowing learners to request quizzes and answer verbally.

[0076] The interactive learning support system according to the embodiment comprises a reception unit, a question-presenting unit, an analysis unit, a feedback unit, and a tracking unit. The reception unit receives requests from learners for quizzes. For example, learners can request quizzes by specifying a particular field or difficulty level. For example, a learner might request, "Please give me some basic math problems." This information is entered into the reception unit. The question-presenting unit verbally presents the quiz requested by the reception unit. For example, the question-presenting unit might present a quiz such as, "Here's the next question. What is 2 + 2?" The learner answers verbally, and the question-presenting unit analyzes the answer using speech recognition technology. The analysis unit analyzes the learner's answer. For example, if the learner answers "4," the analysis unit analyzes the answer and determines whether it is correct. The feedback unit provides feedback based on the results analyzed by the analysis unit. The feedback unit provides feedback such as "That's correct" if the learner answers correctly, and "Unfortunately, the correct answer is 4" if they answer incorrectly. The tracking unit tracks the learner's progress. For example, if the tracking unit finds that a learner has made many mistakes in a particular area, it generates additional quizzes in that area and presents them at an appropriate time. This helps to strengthen the learner's weak areas. As a result, the interactive learning support system according to this embodiment enables interactive learning by allowing learners to request quizzes and answer them verbally.

[0077] The reception desk receives quiz requests from learners. For example, learners can request quizzes in specific fields or difficulty levels. Specifically, learners can request things like, "Please give me basic math problems," or "Please give me intermediate-level history problems." The reception desk receives these requests and collects information to select appropriate quizzes. Learner requests are entered into the reception desk via voice or text input. In the case of voice input, speech recognition technology is used to convert the request into text data and analyze it. In the case of text input, the system analyzes the text entered by the learner directly. The reception desk provides information to select the optimal quiz, taking into account the learner's past learning history and current learning status. For example, it can select quizzes of appropriate difficulty and fields based on areas the learner has struggled with in the past or content they have recently studied. This allows the reception desk to provide quizzes tailored to the learner's needs and support effective learning. Furthermore, the reception desk can save learner requests in a database and use them for future learning support. This enables centralized management of learner learning history and provides support that meets individual learning needs.

[0078] The question-setting unit verbally presents quizzes requested by the reception unit. For example, the question-setting unit might ask, "Here's the next question: What is 2 + 2?" Specifically, the question-setting unit selects an appropriate quiz based on the learner's request and verbally presents the quiz using speech synthesis technology. The speech synthesis technology can read the quiz aloud with natural pronunciation and intonation, providing a user-friendly interface for learners. Learners verbally answer the presented quiz, and the question-setting unit analyzes the answer using speech recognition technology. The speech recognition technology converts the learner's utterance into text data and sends it to the analysis unit. The question-setting unit uses an advanced speech recognition algorithm to accurately recognize the learner's answer, taking into account background noise and differences in pronunciation during the analysis. This allows the question-setting unit to accurately recognize the learner's answer and send it to the analysis unit. Furthermore, the question-setting unit can provide immediate feedback on the learner's answer. For example, if a learner answers correctly, the system provides feedback such as "That's correct," and if they answer incorrectly, it provides feedback such as "Unfortunately, the correct answer is 4." This allows the question-setting system to provide immediate feedback to learners, thereby enhancing learning effectiveness.

[0079] The analysis unit analyzes the learner's responses. For example, if a learner answers "4," the analysis unit analyzes that answer and determines whether it is correct. Specifically, the analysis unit uses speech recognition technology to convert the learner's response into text data and compares it with the correct answer database. The correct answer database contains the correct answers for each quiz, and the analysis unit determines whether the learner's answer matches the correct answer database. If the learner's answer is correct, the analysis unit determines it as "correct," and if it is incorrect, it determines it as "incorrect." Furthermore, the analysis unit can analyze the trends and patterns of the learner's responses to understand the learner's level of understanding and areas of weakness. For example, if a learner frequently makes mistakes in a particular area, the analysis unit determines that their understanding of that area is low and provides additional quizzes. The analysis unit can also evaluate the learner's response time and accuracy to understand the learner's progress. This allows the analysis unit to understand the learner's level of understanding and progress in real time and provide appropriate feedback. Furthermore, the analysis unit can accumulate learner response data and evaluate long-term learning effectiveness. This allows the analysis unit to provide support tailored to individual learning needs based on the learner's learning history.

[0080] The feedback unit provides feedback based on the results analyzed by the analysis unit. For example, if the learner answers correctly, the feedback unit will say, "That's correct," and if they answer incorrectly, it will say, "Unfortunately, the correct answer is 4." Specifically, the feedback unit provides appropriate feedback to the learner based on the analysis results received from the analysis unit. The feedback is provided in audio or text format, instantly conveying the results to the learner. The feedback unit can provide individualized feedback according to the learner's level of understanding and progress. For example, if a learner frequently makes mistakes in a particular area, it can provide additional quizzes in that area and offer advice to improve their understanding. The feedback unit can also evaluate the learner's response time and accuracy, and grasp the learner's progress. This allows the feedback unit to grasp the learner's level of understanding and progress in real time and provide appropriate feedback. Furthermore, the feedback unit can accumulate learner feedback and evaluate long-term learning effectiveness. This enables the feedback unit to provide support tailored to individual learning needs based on the learner's learning history.

[0081] The tracking unit tracks the learner's progress. For example, if a learner makes many mistakes in a particular area, the tracking unit generates additional quizzes in that area and presents them at the appropriate time. Specifically, the tracking unit accumulates the learner's answer data and understands the learner's progress in real time. The tracking unit analyzes the learner's answer trends and patterns to understand the learner's level of understanding and areas of weakness. For example, if a learner frequently makes mistakes in a particular area, it determines that their understanding of that area is low and presents additional quizzes. The tracking unit also evaluates the learner's answer time and accuracy to understand their progress. This allows the tracking unit to understand the learner's level of understanding and progress in real time and provide appropriate feedback. Furthermore, the tracking unit accumulates the learner's answer data and can evaluate the long-term learning effect. This allows the tracking unit to provide support tailored to the learner's individual learning needs based on their learning history.

[0082] The reception section includes a specification section where users can specify a particular field or difficulty level. The specification section allows learners to request quizzes by specifying a particular field or difficulty level. For example, a learner might request, "Please give me some basic math problems." This information is entered into the specification section. This allows learners to request quizzes by specifying a particular field or difficulty level.

[0083] The tracking unit includes a generation unit that generates additional quizzes based on the learner's progress. For example, if a learner makes many mistakes in a particular area, the generation unit will generate additional quizzes in that area and present them at an appropriate time. This allows for the generation and presentation of additional quizzes according to the learner's progress.

[0084] The feedback unit provides individually customized feedback based on the learner's answers. For example, if the learner answers correctly, the feedback unit will say, "That's correct," and if they answer incorrectly, it will say, "Unfortunately, the correct answer is 4." This allows for the provision of individually optimized feedback based on the learner's answers.

[0085] The tracking unit includes a provisioning unit that provides learning plans based on the learner's progress. For example, if a learner makes many mistakes in a particular area, the provisioning unit will provide a learning plan for that area. This allows learning plans to be provided according to the learner's progress.

[0086] The analysis unit analyzes the learner's response using speech recognition technology. For example, if the learner answers "4," the analysis unit analyzes that answer using speech recognition technology and determines whether it is correct. In this way, the learner's response can be accurately analyzed using speech recognition technology.

[0087] The reception desk estimates the learner's emotions and adjusts the timing of quiz presentations based on the estimated emotions. For example, if the learner is stressed, the reception desk will present quizzes at a time when the learner can relax. For example, if the learner is focused, the reception desk will present quizzes continuously. For example, if the learner is tired, the reception desk will present quizzes after a break. By adjusting the timing of quizzes according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk analyzes the learner's past quiz request history and selects the optimal question format. For example, the reception desk prioritizes quiz formats that the learner has previously requested and preferred. For example, the reception desk avoids quiz formats that the learner has previously struggled with. For example, the reception desk determines the optimal frequency of question presentation based on the learner's past request history. In this way, by analyzing past quiz request history, the reception desk can select the most suitable question format for each learner.

[0089] The reception desk filters quiz requests based on the learner's current learning status and areas of interest. For example, the reception desk prioritizes quizzes related to the area the learner is currently studying. For example, the reception desk filters quizzes based on the learner's areas of interest. For example, the reception desk presents quizzes of appropriate difficulty according to the learner's learning progress. This allows for the presentation of more appropriate quizzes by filtering them based on the learner's current learning status and areas of interest.

[0090] The reception desk estimates the learner's emotions and determines the priority of quizzes based on the estimated emotions. For example, if the learner is relaxed, the reception desk will prioritize more difficult quizzes. If the learner is tired, the reception desk will prioritize easier quizzes. If the learner is excited, the reception desk will prioritize interesting quizzes. By prioritizing quizzes according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The reception desk prioritizes presenting quizzes that are highly relevant to the learner's geographical location when a quiz request is received. For example, if the learner is in a specific region, the reception desk will present quizzes related to that region. If the learner is traveling, the reception desk will present quizzes related to their travel destination. If the learner is at home, the reception desk will present quizzes related to their home. By presenting highly relevant quizzes based on the learner's geographical location, more effective learning becomes possible.

[0092] The reception desk analyzes the learner's social media activity when a quiz request is received and presents relevant quizzes. For example, the reception desk may present quizzes related to topics the learner has shown interest in on social media. For example, the reception desk may present quizzes related to accounts the learner follows on social media. For example, the reception desk may present quizzes related to content the learner has shared on social media. In this way, relevant quizzes can be presented by analyzing the learner's social media activity.

[0093] The question-setting unit estimates the learner's emotions and adjusts the quiz presentation based on the estimated emotions. For example, if the learner is nervous, the question-setting unit will present the quiz in a gentle tone. If the learner is relaxed, the question-setting unit will present the quiz in a friendly tone. If the learner is excited, the question-setting unit will present the quiz in an energetic tone. By adjusting the quiz presentation according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The question-setting unit adjusts the level of detail in quiz questions based on their importance. For example, it provides detailed explanations for important questions, and concise explanations for less important questions. It also adjusts the level of detail according to the learner's level of understanding. By adjusting the level of detail based on the importance of each question, the system can provide learners with the most suitable quiz.

[0095] The question-generating unit applies different question-generating algorithms depending on the category of the question when generating quiz questions. For example, in the case of a mathematics question, the unit applies an algorithm that emphasizes the calculation process. For example, in the case of a history question, the unit applies an algorithm that presents questions in chronological order. For example, in the case of a science question, the unit applies an algorithm that emphasizes experimental results. By applying different question-generating algorithms depending on the category of the question, the unit can provide learners with the most optimal quiz.

[0096] The question generator estimates the learner's emotions and adjusts the length of the quiz based on the estimated emotions. For example, if the learner is in a hurry, the generator will present a short quiz. If the learner is relaxed, the generator will present a longer quiz. If the learner is focused, the generator will present a quiz of appropriate length. By adjusting the length of the quiz according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The question-setting department determines the priority of questions based on when they were submitted. For example, it might prioritize questions submitted most recently, or postpone questions submitted later. It might also present questions at an appropriate time depending on when they were submitted. By prioritizing questions based on their submission date, the department can provide learners with the most optimal quiz.

[0098] The question-setting unit adjusts the order of questions based on their relevance. For example, it might present highly relevant questions consecutively, or less relevant questions later. It might also prioritize highly relevant questions based on the learner's level of understanding. By adjusting the order of questions based on their relevance, the system can provide learners with the most optimal quiz.

[0099] The analysis unit estimates the learner's emotions and adjusts the criteria for response analysis based on the estimated learner's emotions. For example, if the learner is nervous, the analysis unit will analyze using lenient criteria. For example, if the learner is relaxed, the analysis unit will analyze using strict criteria. For example, if the learner is excited, the analysis unit will analyze using standard criteria. By adjusting the criteria for response analysis according to the learner's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The analysis unit improves the accuracy of the analysis by considering the relationships between learners during response analysis. For example, if learners are studying in a group, the analysis unit considers the responses of the entire group. For example, if learners are studying individually, the analysis unit gives more emphasis to individual responses. The analysis unit improves the accuracy of the analysis based on the relationships between learners. In this way, the accuracy of the analysis is improved by considering the relationships between learners.

[0101] The analysis unit performs analysis while considering the learner's attribute information during response analysis. For example, the analysis unit performs analysis using appropriate criteria based on the learner's age. For example, the analysis unit improves the accuracy of the analysis based on the learner's learning history. For example, the analysis unit provides analysis results while considering the learner's attribute information. This improves the accuracy of the analysis by considering the learner's attribute information.

[0102] The analysis unit estimates the learner's emotions and adjusts the display order of the analysis results based on the estimated emotions. For example, if the learner is nervous, the analysis unit displays important analysis results first. For example, if the learner is relaxed, the analysis unit displays detailed analysis results sequentially. For example, if the learner is excited, the analysis unit prioritizes displaying interesting analysis results. By adjusting the display order of analysis results according to the learner's emotions, more effective feedback becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The analysis unit considers the geographical distribution of learners when analyzing responses. For example, if learners are concentrated in a particular area, the analysis unit considers the characteristics of that area. For example, if learners are widely distributed, the analysis unit considers the characteristics of each region. For example, the analysis unit provides analysis results based on the geographical distribution of learners. This improves the accuracy of the analysis by considering the geographical distribution of learners.

[0104] The analysis unit improves the accuracy of the analysis by referring to relevant literature during response analysis. For example, the analysis unit analyzes by referring to literature related to the learner's responses. For example, the analysis unit searches for relevant literature based on the learner's responses to improve the accuracy of the analysis. For example, the analysis unit compares the learner's responses with relevant literature and provides analysis results. In this way, the accuracy of the analysis is improved by referring to relevant literature.

[0105] The feedback unit estimates the learner's emotions and adjusts how the feedback is displayed based on the estimated emotions. For example, if the learner is nervous, the feedback unit provides feedback in a gentle tone. For example, if the learner is relaxed, the feedback unit provides detailed feedback. For example, if the learner is excited, the feedback unit provides feedback in an energetic tone. This allows for more effective feedback by adjusting how the feedback is displayed according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The feedback unit optimizes the current feedback by referring to past feedback data when providing feedback. For example, the feedback unit optimizes the current feedback based on feedback the learner has received in the past. For example, the feedback unit analyzes the learner's past feedback data to provide optimal feedback. For example, the feedback unit adjusts the current feedback by referring to the learner's past feedback history. This allows for the optimization of the current feedback by referring to past feedback data.

[0107] The feedback system applies different feedback methods depending on the learner's category when providing feedback. For example, it provides basic feedback to beginner learners, advanced feedback to intermediate learners, and expert feedback to advanced learners. By applying different feedback methods to each learner's category, more effective feedback becomes possible.

[0108] The feedback unit estimates the learner's emotions and adjusts the importance of feedback based on the estimated emotions. For example, if the learner is nervous, the feedback unit prioritizes important feedback. For example, if the learner is relaxed, the feedback unit provides detailed feedback. For example, if the learner is excited, the feedback unit prioritizes interesting feedback. This allows for more effective feedback by adjusting the importance of feedback according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The feedback unit analyzes changes in feedback based on the learner's submission timing when providing feedback. For example, the feedback unit provides prompt feedback on the learner's most recent submission. For example, the feedback unit provides detailed feedback on the learner's past submissions. For example, the feedback unit adjusts the content of the feedback based on the learner's submission timing. This allows for more appropriate feedback by analyzing changes in feedback based on the learner's submission timing.

[0110] The feedback unit analyzes the feedback by referring to relevant market data when providing it. For example, the feedback unit provides feedback by referring to market data related to the learner's responses. For example, the feedback unit analyzes relevant market data based on the learner's responses to improve the accuracy of the feedback. For example, the feedback unit provides feedback by comparing the learner's responses with market data. This improves the accuracy of the feedback by referring to relevant market data.

[0111] The tracking unit estimates the learner's emotions and adjusts the progress display method based on the estimated learner's emotions. For example, if the learner is nervous, the tracking unit provides a simple and highly visible display method. For example, if the learner is relaxed, the tracking unit provides detailed progress information. For example, if the learner is excited, the tracking unit provides a visually stimulating display method. This allows for more effective progress management by adjusting the progress display method according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The tracking unit predicts current progress by referring to past progress data during progress tracking. For example, the tracking unit predicts current progress based on the learner's past progress data. For example, the tracking unit analyzes the learner's past progress data to provide an optimal progress prediction. For example, the tracking unit predicts current progress by referring to the learner's past progress history. This allows for prediction of current progress by referring to past progress data.

[0113] The tracking unit applies different progress analysis methods to each learner category during progress tracking. For example, it applies basic progress analysis methods to beginner learners, advanced progress analysis methods to intermediate learners, and specialized progress analysis methods to advanced learners. By applying different progress analysis methods to each learner category, more effective progress management becomes possible.

[0114] The tracking unit estimates the learner's emotions and adjusts the importance of progress based on the estimated emotions. For example, if the learner is stressed, the tracking unit prioritizes displaying important progress information. For example, if the learner is relaxed, the tracking unit provides detailed progress information. For example, if the learner is excited, the tracking unit prioritizes displaying interesting progress information. This allows for more effective progress management by adjusting the importance of progress according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The tracking unit analyzes changes in progress based on the learner's submission timing during progress tracking. For example, the tracking unit analyzes changes in progress based on the learner's most recent submitted progress data. For example, the tracking unit analyzes changes in progress based on the learner's past submitted progress data. For example, the tracking unit analyzes changes in progress based on the learner's submission timing. This allows for more appropriate progress management by analyzing changes in progress based on the learner's submission timing.

[0116] The tracking unit analyzes progress by referring to relevant market data during progress tracking. For example, the tracking unit analyzes progress by referring to market data related to learner progress data. For example, the tracking unit improves the accuracy of progress by analyzing relevant market data based on learner progress data. For example, the tracking unit analyzes progress by comparing learner progress data with market data. This improves the accuracy of progress by referring to relevant market data.

[0117] The selection unit estimates the learner's emotions and adjusts the method of specifying the subject matter and difficulty level based on the estimated emotions. For example, if the learner is nervous, the selection unit will prioritize easy subject matters and difficulty levels. For example, if the learner is relaxed, the selection unit will specify more difficult subject matters. For example, if the learner is excited, the selection unit will specify interesting subject matters. This allows for more effective learning by adjusting the method of specifying subject matters and difficulty levels according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The designation unit selects the optimal designation method by referring to past designation data when specifying fields and difficulty levels. For example, the designation unit designates the optimal fields and difficulty levels based on the learner's past designation data. For example, the designation unit analyzes the learner's past designation data and provides the optimal designation method. For example, the designation unit designates the optimal fields and difficulty levels by referring to the learner's past designation history. In this way, the optimal fields and difficulty levels can be designated by referring to past designation data.

[0119] The selection unit estimates the learner's emotions and determines the priority of subjects and difficulty levels based on the estimated emotions. For example, if the learner is nervous, the selection unit will prioritize easy subjects and difficulty levels. For example, if the learner is relaxed, the selection unit will prioritize difficult subjects. For example, if the learner is excited, the selection unit will prioritize interesting subjects. This allows for more effective learning by prioritizing subjects and difficulty levels according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The designation function selects the optimal designation method when specifying fields and difficulty levels, taking into account the learner's geographical location. For example, if the learner is in a specific region, the designation function will specify fields and difficulty levels related to that region. For example, if the learner is traveling, the designation function will specify fields and difficulty levels related to the travel destination. For example, if the learner is at home, the designation function will specify fields and difficulty levels related to home. In this way, the optimal fields and difficulty levels can be specified by taking the learner's geographical location into consideration.

[0121] The generation unit estimates the learner's emotions and adjusts the method of generating additional quizzes based on the estimated emotions. For example, if the learner is nervous, the generation unit generates easy additional quizzes. For example, if the learner is relaxed, the generation unit generates difficult additional quizzes. For example, if the learner is excited, the generation unit generates interesting additional quizzes. By adjusting the method of generating additional quizzes according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0122] The generation unit selects the optimal generation method by referring to past generation data when generating additional quizzes. For example, the generation unit generates the optimal additional quiz based on the learner's past generation data. For example, the generation unit analyzes the learner's past generation data and provides the optimal generation method. For example, the generation unit generates the optimal additional quiz by referring to the learner's past generation history. In this way, the optimal additional quiz can be generated by referring to past generation data.

[0123] The generation unit estimates the learner's emotions and determines the priority of additional quizzes based on the estimated emotions. For example, if the learner is nervous, the generation unit prioritizes generating easy additional quizzes. For example, if the learner is relaxed, the generation unit prioritizes generating difficult additional quizzes. For example, if the learner is excited, the generation unit prioritizes generating interesting additional quizzes. This allows for more effective learning by prioritizing additional quizzes according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The generation unit selects the optimal generation method when generating additional quizzes, taking into account the learner's geographical location. For example, if the learner is in a specific region, the generation unit generates additional quizzes related to that region. For example, if the learner is traveling, the generation unit generates additional quizzes related to the travel destination. For example, if the learner is at home, the generation unit generates additional quizzes related to home. In this way, the optimal additional quizzes can be generated by taking the learner's geographical location into consideration.

[0125] The system estimates the learner's emotions and adjusts how the learning plan is delivered based on the estimated emotions. For example, if the learner is nervous, the system provides a simple learning plan. If the learner is relaxed, the system provides a detailed learning plan. If the learner is excited, the system provides an engaging learning plan. By adjusting how the learning plan is delivered according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The service provider selects the optimal delivery method by referring to past delivery data when providing a learning plan. For example, the service provider provides the optimal learning plan based on the learner's past delivery data. For example, the service provider analyzes the learner's past delivery data to provide the optimal delivery method. For example, the service provider provides the optimal learning plan by referring to the learner's past delivery history. In this way, the optimal learning plan can be provided by referring to past delivery data.

[0127] The learning platform estimates the learner's emotions and prioritizes learning plans based on these estimates. For example, if the learner is nervous, the platform prioritizes providing easier learning plans. If the learner is relaxed, the platform prioritizes providing more challenging learning plans. If the learner is excited, the platform prioritizes providing interesting learning plans. By prioritizing learning plans according to the learner's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The service provider selects the optimal delivery method when providing learning plans, taking into account the learner's geographical location. For example, if the learner is in a specific region, the service provider will provide a learning plan related to that region. For example, if the learner is traveling, the service provider will provide a learning plan related to their travel destination. For example, if the learner is at home, the service provider will provide a learning plan related to their home. In this way, the service provider can provide the most suitable learning plan by taking into account the learner's geographical location.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The reception desk can analyze learners' past learning history and determine the optimal order in which quiz questions are presented. For example, by prioritizing questions that learners have struggled with in the past, it can promote learning that overcomes weaknesses. Conversely, by postponing questions in areas where learners excel, it can help balance learning. Furthermore, it is possible to adjust the difficulty level of the quizzes based on the learner's past learning history. This enables effective learning tailored to the individual needs of each learner.

[0131] The analysis unit can also consider the speed of the learner's response when analyzing their answers. For example, if a learner answers correctly quickly, it can be determined that they have a high level of understanding in that area, and the difficulty level of the next question can be increased. On the other hand, if a learner takes a long time to answer, it can be determined that their understanding in that area is low, and additional explanations or hints can be provided. Furthermore, based on the speed of the response, it is possible to estimate the learner's concentration and fatigue level and suggest a break at an appropriate time. This allows for flexible learning support tailored to the learner's level of understanding and concentration.

[0132] The tracking function can also consider the learner's motivation level when tracking their progress. For example, if a learner is highly motivated, challenging problems can be presented to maintain their enthusiasm. On the other hand, if motivation is low, easier problems or interesting topics can be presented to rekindle their interest in learning. It can also provide appropriate feedback and encouraging messages according to the learner's motivation level. This allows for effective learning while maintaining the learner's motivation.

[0133] The feedback system can also consider the learner's learning style when providing feedback based on their responses. For example, visual learners can be provided with feedback using diagrams and graphs, while auditory learners can receive audio feedback. Furthermore, practical learners can receive feedback that includes specific examples and practical advice. This allows for the provision of effective feedback tailored to each learner's individual learning style.

[0134] The tracking unit can also take into account the learner's lifestyle when tracking their progress. For example, if a learner has a morning routine, quizzes can be presented during the morning hours to maximize their concentration. Conversely, if a learner has a night owl routine, quizzes can be presented during the evening hours to enhance learning effectiveness. It can also suggest appropriate break times and study times based on the learner's lifestyle. This allows for the provision of effective learning support tailored to the learner's lifestyle.

[0135] The analysis unit can also estimate the learner's emotions when analyzing their responses and provide analysis results based on those estimated emotions. For example, if a learner is feeling anxious, the analysis results can be provided in a gentle tone to provide reassurance. On the other hand, if a learner is confident, the analysis results can be provided strictly to encourage further challenges. Furthermore, if a learner is excited, the analysis results can be provided in an energetic tone to increase their motivation to learn. By providing appropriate analysis results tailored to the learner's emotions, more effective learning becomes possible.

[0136] The reception desk can estimate the learner's emotions and adjust the quiz format based on those estimates. For example, if the learner is relaxed, the quiz can be presented in a relaxed atmosphere. On the other hand, if the learner is nervous, the quiz can be presented with humor to ease their tension. If the learner is excited, the quiz can be presented in an energetic format to increase their motivation to learn. By adjusting the quiz format according to the learner's emotions, more effective learning becomes possible.

[0137] The reception desk can analyze learners' past learning history and determine the optimal timing for presenting quizzes. For example, if a learner has shown high performance during a specific time period in the past, presenting quizzes during that time can enhance learning effectiveness. Conversely, if a learner has shown low performance during a specific time period in the past, quizzes can be avoided during that time. Furthermore, it is possible to suggest appropriate break times based on the learner's past learning history. This enables effective learning tailored to the individual needs of each learner.

[0138] The question-setting unit can also estimate the learner's emotions and adjust the difficulty of the quiz based on those emotions. For example, if a learner is relaxed, a more difficult quiz can be presented to encourage a learning challenge. On the other hand, if a learner is nervous, an easier quiz can be presented to boost their confidence in learning. Furthermore, if a learner is excited, a quiz on an interesting topic can be presented to increase their motivation to learn. In this way, adjusting the difficulty of the quiz according to the learner's emotions enables more effective learning.

[0139] The feedback system can also consider the learner's learning objectives when providing feedback based on their responses. For example, if a learner's goal is to pass a specific exam, feedback related to that exam can be provided. On the other hand, if a learner's goal is to acquire a specific skill, feedback related to that skill can be provided. Furthermore, if a learner's goal is self-development, feedback related to self-development can be provided. This allows for the provision of effective feedback tailored to each learner's individual learning objectives.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The reception desk receives quiz requests from learners. Learners can request quizzes by specifying a particular field or difficulty level. For example, a learner might request "basic math problems." This information is entered into the reception desk. Step 2: The question-setting team verbally presents the quiz requested by the reception team. For example, they might ask, "Here's the next question: What is 2 + 2?" Step 3: The analysis unit analyzes the learner's response. For example, if the learner answers "4", the unit analyzes that response and determines whether it is correct. Step 4: The feedback unit provides feedback based on the results analyzed by the analysis unit. For example, if the learner answers correctly, it provides feedback saying "That's correct," and if they answer incorrectly, it provides feedback saying "Unfortunately, the correct answer is 4." Step 5: The tracking unit tracks the learner's progress. For example, if a learner makes many mistakes in a particular area, it generates additional quizzes in that area and presents them at the appropriate time. This helps to strengthen the learner's weak areas.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0144] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, question unit, analysis unit, feedback unit, and tracking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and is used when a learner requests a quiz question. The question unit is implemented by the control unit 46A of the smart device 14 and presents the quiz question orally. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's answer. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback based on the analysis results. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the learner's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, question unit, analysis unit, feedback unit, and tracking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and is used when a learner requests a quiz question. The question unit is implemented by the control unit 46A of the smart glasses 214 and presents the quiz question orally. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's answer. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback based on the analysis results. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the learner's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the reception unit, question unit, analysis unit, feedback unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and is used when a learner requests a quiz question. The question unit is implemented by the control unit 46A of the headset terminal 314 and presents the quiz question orally. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's answer. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback based on the analysis results. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the learner's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0180] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0184] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0185] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0186] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0187] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0188] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0189] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0190] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0192] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0194] Each of the multiple elements described above, including the reception unit, question unit, analysis unit, feedback unit, and tracking unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and is used when a learner requests a quiz question. The question unit is implemented by the control unit 46A of the robot 414 and presents the quiz question orally. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learner's answer. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides feedback based on the analysis results. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the learner's progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0195] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0197] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0198] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0199] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0203] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0204] 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.

[0205] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0206] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0207] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0208] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0209] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0210] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0211] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0212] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0213] (Note 1) The reception desk accepts requests for quiz questions, The question-setting department will orally present the quiz requested by the reception department, An analysis unit that analyzes the learner's responses, A feedback unit provides feedback based on the results of the analysis performed by the aforementioned analysis unit, It comprises a tracking unit that tracks the learner's progress, A system characterized by the following features. (Note 2) The aforementioned reception unit is It includes a section for specifying a particular field or difficulty level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned tracking unit is It includes a generation unit that generates additional quizzes based on the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provide personalized feedback based on learners' responses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned tracking unit is It includes a service that provides learning plans based on the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze learners' responses using speech recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the learner's emotions and adjusts the timing of quiz questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the learner's past quiz request history and select the most appropriate question format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a quiz question is requested, filtering is performed based on the learner's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the learner's emotions and determines the priority of quiz questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a quiz is requested, the system prioritizes presenting quizzes that are highly relevant based on the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a quiz is requested, the system analyzes the learner's social media activity and presents relevant quizzes. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question section is, The system estimates the learner's emotions and adjusts the quiz presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question section is, When creating quiz questions, adjust the level of detail based on the importance of each question. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question section is, When creating quiz questions, different question-creation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question section is, The system estimates the learner's emotions and adjusts the length of the quiz based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question section is, When creating quiz questions, the priority of questions is determined based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question section is, When presenting quiz questions, adjust the order of the questions based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, We estimate learners' emotions and adjust the criteria for response analysis based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing responses, consider the relationships between learners to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing responses, the analysis takes into account the learner's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, The system estimates the learner's emotions and adjusts the display order of the analysis results based on the estimated learner emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing responses, the analysis will take into account the geographical distribution of learners. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When analyzing responses, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the learner's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, past feedback data is referenced to make the current feedback the most appropriate. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, different feedback methods will be applied depending on the learner category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the importance of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, analyze how the feedback changes based on when the learner submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we analyze the feedback by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tracking unit is The system estimates the learner's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned tracking unit is When tracking progress, past progress data is used to predict current progress. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned tracking unit is When tracking progress, apply different progress analysis methods to each learner category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned tracking unit is The system estimates learners' emotions and adjusts the importance of progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned tracking unit is When tracking progress, analyze changes in progress based on when learners submit their work. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned tracking unit is When tracking progress, analyze progress by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The designated part is, The system estimates learners' emotions and adjusts the method of specifying subjects and difficulty levels based on the estimated learners' emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The designated part is, When specifying fields and difficulty levels, the system selects the optimal specification method by referring to past specification data. The system described in Appendix 2, characterized by the features described herein. (Note 39) The designated part is, The system estimates learners' emotions and determines the priority of subjects and difficulty levels based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The designated part is, When specifying fields and difficulty levels, the optimal specification method is selected by considering the learner's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 41) The generating unit is The system estimates the learner's emotions and adjusts how additional quizzes are generated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The generating unit is When generating additional quizzes, the system selects the optimal generation method by referring to past generation data. The system described in Appendix 3, characterized by the features described herein. (Note 43) The generating unit is The system estimates the learner's emotions and prioritizes additional quizzes based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The generating unit is When generating additional quizzes, the optimal generation method is selected by considering the learner's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned supply unit is, The system estimates learners' emotions and adjusts how learning plans are delivered based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned supply unit is, When providing a learning plan, we will refer to past delivery data to select the most suitable delivery method. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned supply unit is, The system estimates learners' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned supply unit is, When providing a learning plan, the optimal delivery method will be selected considering the learner's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts requests for quiz questions, The question-setting department will orally present the quiz requested by the reception department, An analysis unit that analyzes the learner's responses, A feedback unit provides feedback based on the results of the analysis performed by the aforementioned analysis unit, It comprises a tracking unit that tracks the learner's progress, A system characterized by the following features.

2. The aforementioned reception unit is It includes a section for specifying a particular field or difficulty level. The system according to feature 1.

3. The aforementioned tracking unit is It includes a generation unit that generates additional quizzes based on the learner's progress. The system according to feature 1.

4. The aforementioned feedback unit is Provide personalized feedback based on learners' responses. The system according to feature 1.

5. The aforementioned tracking unit is It includes a service that provides learning plans based on the learner's progress. The system according to feature 1.

6. The aforementioned analysis unit, We analyze learners' responses using speech recognition technology. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the learner's emotions and adjusts the timing of quiz questions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the learner's past quiz request history and select the most appropriate question format. The system according to feature 1.

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