system

Abstract

The system according to this embodiment aims to provide appropriate answers to learners' questions and concerns and to effectively monitor their learning progress. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The reception unit receives questions and inquiries from learners. The analysis unit analyzes the questions and inquiries received by the reception unit. The provision unit provides appropriate answers based on the results analyzed by the analysis unit. The monitoring unit monitors the learner's progress based on the answers provided by the provision unit. The notification unit notifies the teacher of the progress information monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the support for learners to solve doubts and questions is not sufficient, and it is difficult to effectively monitor the progress of learning.

[0005] The system according to the embodiment aims to provide appropriate answers to the doubts and questions of learners and effectively monitor the progress of learning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The reception unit receives questions and inquiries from learners. The analysis unit analyzes the questions and inquiries received by the reception unit. The provision unit provides appropriate answers based on the results analyzed by the analysis unit. The monitoring unit monitors the learner's progress based on the answers provided by the provision unit. The notification unit notifies the teacher of the progress information monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate answers to learners' questions and concerns and effectively monitor their learning progress. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart learning assistant system according to an embodiment of the present invention is an innovative AI tool designed to support learners. This smart learning assistant system allows learners to input questions or doubts, which the AI ​​then analyzes and provides appropriate answers. The AI ​​provides optimal support, taking into account the learner's learning style and progress. For example, if a learner has difficulty understanding a mathematical problem, the AI ​​provides a detailed explanation to help them understand. The AI ​​also monitors the learner's progress and provides individualized advice and feedback as needed. This allows learners to learn at their own pace and maximize their learning effectiveness. Furthermore, the smart learning assistant system also includes a teacher collaboration function, enabling teachers to provide more effective instruction based on the learner's progress information provided by the AI. This reduces the burden on teachers while providing appropriate support to each individual learner. The smart learning assistant system provides an environment where learners can receive the necessary support 24 hours a day, allowing them to learn at their own pace. For example, learners input questions or doubts. These can be entered in various formats, such as text, audio, or image. The AI ​​then analyzes the question and provides an appropriate answer. For example, AI uses natural language processing technology to analyze questions and provide accurate and relevant answers. Furthermore, AI monitors learners' progress and provides personalized advice and feedback as needed. For instance, AI considers learners' learning styles and progress to provide optimal support. This allows learners to learn at their own pace and maximize learning effectiveness. As a result, smart learning assistant systems can efficiently receive, analyze, and provide appropriate answers to learners' questions and concerns, monitor progress, and notify teachers.

[0029] The smart learning assistant system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The reception unit receives questions and inquiries from learners. These questions and inquiries may include, but are not limited to, text format, audio format, or image format. For example, the reception unit can receive questions in text format. The reception unit can also receive questions in audio format. Furthermore, the reception unit can also receive questions in image format. For example, the reception unit can receive questions entered by learners in text format. For questions in audio format, for example, audio can be entered using a microphone. For questions in image format, for example, images can be entered using a camera. The analysis unit analyzes the questions and inquiries received by the reception unit. The analysis may be performed using, but is not limited to, natural language processing techniques or machine learning algorithms. For example, the analysis unit can analyze questions in text format using natural language processing techniques. Furthermore, the analysis unit can also analyze questions in audio format using machine learning algorithms. Furthermore, the analysis unit can also analyze questions in image format using image analysis techniques. For example, the analysis unit analyzes text-based questions using natural language processing technology and provides appropriate answers. Machine learning algorithms analyze audio-based questions and provide appropriate answers. Image analysis technology analyzes image-based questions and provides appropriate answers. The delivery unit provides appropriate answers based on the results analyzed by the analysis unit. Appropriate answers are provided based on criteria such as accuracy, relevance, and ease of understanding, but are not limited to these examples. For example, the delivery unit provides accurate and relevant answers based on the results analyzed by the analysis unit. The delivery unit can also provide individualized advice and feedback, taking into account the learner's learning style and progress. For example, the delivery unit provides answers tailored to the learner's learning style, such as visual learning, auditory learning, or experiential learning. The monitoring unit monitors the learner's progress based on the answers provided by the delivery unit. Progress is monitored based on criteria such as learning achievement, time elapsed, and task completion, but is not limited to these examples.For example, the monitoring unit monitors the learner's learning achievement. The monitoring unit can also monitor the elapsed learning time of the learner. Furthermore, the monitoring unit can also monitor the learner's completion status of assignments. For example, the monitoring unit monitors the learner's learning achievement and provides progress information. The notification unit notifies the teacher of the progress information monitored by the monitoring unit. Notifications are made by methods such as email, push notifications, and dashboard displays, but are not limited to these examples. For example, the notification unit notifies the teacher of progress information using email. The notification unit can also notify the teacher of progress information using push notifications. Furthermore, the notification unit can also notify the teacher of progress information using a dashboard display. For example, the notification unit notifies the teacher of progress information using email so that the teacher can grasp the learner's progress. As a result, the smart learning assistant system according to the embodiment can efficiently receive, analyze, provide appropriate answers to learners' questions and inquiries, monitor progress, and notify the teacher.

[0030] The reception desk receives learners' questions and inquiries. These questions and inquiries may include, but are not limited to, text, audio, and image formats. For example, the reception desk can accept questions in text format. It can also accept questions in audio format. Furthermore, it can also accept questions in image format. For example, the reception desk can accept questions entered by learners in text format. Questions in audio format can be entered using a microphone, for example. Questions in image format can be entered using a camera, for example. The reception desk works in conjunction with various input devices to accept these diverse question formats. For example, questions in text format are entered using a keyboard or touchscreen, questions in audio format are received as audio data via a microphone, and questions in image format are received as image data captured using a camera. This allows learners to submit questions in the way that is most convenient for them, and the system can efficiently receive them. Furthermore, the reception desk also plays a role in converting the received questions into the appropriate format and sending them to the analysis unit. For example, questions in audio format are converted to text format using speech recognition technology, and questions in image format are converted to text information using image analysis technology. This allows the analysis unit to analyze questions in a consistent format. The reception unit takes learner convenience into maximum consideration and supports a variety of input formats, enabling it to quickly and accurately receive learners' questions and concerns.

[0031] The analysis unit analyzes questions and inquiries received by the reception unit. Analysis is performed using, for example, natural language processing techniques and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit can analyze text-based questions using natural language processing techniques. The analysis unit can also analyze voice-based questions using machine learning algorithms. Furthermore, the analysis unit can analyze image-based questions using image analysis techniques. For example, the analysis unit can analyze text-based questions using natural language processing techniques and provide appropriate answers. Machine learning algorithms can, for example, analyze voice-based questions and provide appropriate answers. Image analysis techniques can, for example, analyze image-based questions and provide appropriate answers. The analysis unit utilizes these techniques to accurately understand the intent of the learner's questions and derive the optimal answer. Specifically, when analyzing text-based questions using natural language processing techniques, the analysis unit extracts the context and keywords of the question and searches for appropriate information from relevant knowledge bases and databases. When analyzing voice-based questions, the analysis unit converts the voice data into text using speech recognition techniques, and then applies natural language processing techniques for analysis. When analyzing questions in image format, image recognition technology is used to identify text and objects within the image, and the intent of the question is understood based on this. The analysis unit combines these technologies to provide quick and accurate answers to learners' questions. Furthermore, the analysis unit can learn from past question and answer data and build a feedback loop to improve analysis accuracy. As a result, the analysis unit can respond to a wide range of learners' questions with high accuracy and maximize learning effectiveness.

[0032] The provider unit provides appropriate answers based on the results analyzed by the analysis unit. Appropriate answers are provided based on criteria such as accuracy, relevance, and ease of understanding, but are not limited to these examples. For example, the provider unit provides accurate and relevant answers based on the results analyzed by the analysis unit. The provider unit can also provide individual advice and feedback, taking into account the learner's learning style and progress. For example, the provider unit provides answers tailored to the learner's learning style, such as visual learning, auditory learning, or experiential learning. Based on the analysis results received from the analysis unit, the provider unit provides answers in the format best suited to the learner. For example, visual learners are provided with visual explanations using charts and graphs, and auditory learners are provided with explanations using audio and video. Experiential learners can deepen their understanding by showing actual operating procedures or experimental procedures. The provider unit also provides feedback at the appropriate time, taking into account the learner's progress. For example, when a learner completes a particular task, the provider unit evaluates the results and provides advice for moving on to the next step. Furthermore, the provider unit can provide additional supplementary information and reference materials depending on the learner's level of understanding. This allows learners to progress at their own pace, leading to a deeper understanding. Furthermore, the service provider collects learner feedback and accumulates data to continuously improve the quality of the answers provided. This enables the service provider to consistently offer learners the most optimal answers, maximizing learning effectiveness.

[0033] The monitoring unit monitors learners' progress based on responses provided by the provision unit. Progress is monitored based on criteria such as learning achievement, elapsed time, and assignment completion status, but is not limited to these examples. For example, the monitoring unit monitors learners' learning achievement. The monitoring unit can also monitor the elapsed time of learners' learning. Furthermore, the monitoring unit can also monitor the completion status of learners' assignments. For example, the monitoring unit monitors learners' learning achievement and provides progress information. The monitoring unit tracks learners' learning activities in real time and records learning progress in detail. Specifically, it monitors which assignments learners have completed, to what extent, how much time they have spent on them, and what level of understanding they have shown. This allows for the collection of data to understand learners' learning patterns and trends and to provide individualized learning support. Furthermore, the monitoring unit visualizes learners' progress, allowing learners to check their own progress. For example, it displays progress in graphs and charts, allowing learners to grasp their achievement level and assignment progress at a glance. Furthermore, the monitoring department provides timely feedback based on the learner's progress, supporting them in maintaining their motivation to learn. This allows learners to constantly understand their own learning status and proceed with their studies effectively.

[0034] The notification unit notifies teachers of progress information monitored by the monitoring unit. Notifications are made by methods such as email, push notifications, and dashboard displays, but are not limited to these. For example, the notification unit can notify teachers of progress information via email. The notification unit can also notify teachers of progress information via push notifications. Furthermore, the notification unit can notify teachers of progress information via dashboard displays. For example, the notification unit can notify teachers of progress information via email so that teachers can keep track of learners' progress. The notification unit plays a crucial role in quickly and accurately conveying learner progress information to teachers. Specifically, it organizes the progress data received from the monitoring unit and notifies teachers in a format that is easy for teachers to understand. For example, it displays a list of each learner's progress, clearly indicating which tasks a particular learner is struggling with and what level of progress they have made. The notification unit also provides a dashboard so that teachers can check progress information in real time. The dashboard visually displays learners' progress, achievement levels, and task completion status, enabling teachers to respond quickly. Furthermore, the notification unit can set up alerts for specific events and important progress, and notify teachers immediately. This allows teachers to constantly monitor learners' progress and provide necessary support quickly. The notification system effectively manages learners' progress information and facilitates smooth communication between teachers and learners, thereby maximizing learning effectiveness.

[0035] The service provider can offer individualized advice and feedback, taking into account the learner's learning style and progress. For example, the service provider can provide advice and feedback tailored to the learner's learning style, such as visual learning, auditory learning, or experiential learning. For instance, the service provider can provide visual advice using diagrams and graphs to visual learners. It can also provide auditory advice using audio to auditory learners. Furthermore, it can provide advice based on actual experiences to experiential learners. This allows for the maximization of learning effectiveness by providing individualized advice and feedback tailored to the learner's learning style and progress.

[0036] The reception desk can analyze the learner's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions the content of questions the learner has frequently asked in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the learner has used in the past. Furthermore, the reception desk can predict and suggest the content of questions that will be used during specific time periods based on the learner's past question history. For example, the reception desk can automatically display as suggestions the content of questions the learner has frequently asked in the past, allowing the learner to smoothly input questions and doubts. By prioritizing suggesting reception methods that have been used in the past, the learner can input questions and doubts in a familiar way. By predicting and suggesting the content of questions that will be used during specific time periods based on past question history, the learner can receive support efficiently. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the learner's past question history into AI and have the AI ​​select the optimal reception method. This allows the reception desk to select the optimal reception method by analyzing the learner's past question history and provide efficient support.

[0037] The reception desk can filter questions and inquiries based on the learner's current learning status and areas of interest. For example, the reception desk can prioritize receiving relevant questions based on the learner's current learning progress. It can also prioritize receiving highly relevant questions based on the learner's areas of interest. Furthermore, the reception desk can filter and receive appropriate questions considering the learner's current learning status. For example, by prioritizing the reception desk based on the learner's current learning progress, the learner can receive support efficiently. By prioritizing highly relevant questions based on areas of interest, the learner can proceed with their learning with interest. By filtering and receiving appropriate questions considering the current learning status, the learner can proceed with their learning effectively. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the learner's current learning status and areas of interest into the AI ​​and have the AI ​​perform the filtering. This allows for prioritizing the reception of highly relevant questions by filtering based on the learner's current learning status and areas of interest.

[0038] The reception desk can prioritize receiving questions that are highly relevant, taking into account the learner's geographical location when receiving inquiries. For example, if the learner is in a specific region, the reception desk will prioritize receiving questions related to that region. Furthermore, if the learner is on the move, the reception desk can prioritize receiving questions related to their current location. Additionally, if the learner is in a specific location, the reception desk can prioritize receiving questions related to that location, enabling the learner to efficiently obtain region-related information. If the learner is on the move, prioritizing questions related to their current location ensures they receive appropriate support even while traveling. If the learner is in a specific location, prioritizing questions related to that location ensures they efficiently obtain location-related information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the learner's geographical location information into the AI ​​and have the AI ​​select highly relevant questions. This allows for prioritizing the reception of highly relevant questions by taking into account the learner's geographical location.

[0039] The reception desk can analyze learners' social media activity when receiving questions and inquiries, and accept relevant questions. For example, the reception desk can prioritize questions related to topics that learners frequently mention on social media. The reception desk can also analyze learners' current interests from their social media activity and accept relevant questions. Furthermore, the reception desk can prioritize questions related to accounts that learners follow on social media. For example, by prioritizing questions related to topics that learners frequently mention on social media, the reception desk can efficiently obtain information on topics that interest learners. By analyzing current interests from social media activity and accepting relevant questions, learners can proceed with their learning in an engaging manner. By prioritizing questions related to accounts that learners follow on social media, learners can efficiently obtain information related to those accounts. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input learners' social media activity data into AI and have the AI ​​select relevant questions. This allows the reception desk to prioritize the acceptance of relevant questions by analyzing learners' social media activity.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and doubts during the analysis. For example, the analysis unit provides detailed analysis results for high-importance questions. It can also provide concise analysis results for low-importance questions. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the questions. For example, the analysis unit provides detailed analysis results for high-importance questions to enable learners to understand deeply. For low-importance questions, it provides concise analysis results to enable learners to understand efficiently. By dynamically adjusting the level of detail of the analysis according to the importance of the questions, learners can receive appropriate support. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the questions and doubts into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the questions and doubts.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the question or inquiry during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to mathematical questions. It can also apply a text analysis algorithm to historical questions. Furthermore, it can apply a data analysis algorithm to scientific questions. For example, the analysis unit applies a mathematical formula analysis algorithm to mathematical questions to provide accurate analysis results. It applies a text analysis algorithm to historical questions to provide highly relevant analysis results. It applies a data analysis algorithm to scientific questions to provide detailed analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question or inquiry category data into the AI ​​and have the AI ​​select an appropriate analysis algorithm. This allows for the application of different analysis algorithms depending on the category of the question or inquiry, thereby providing more appropriate analysis results.

[0042] The analysis unit can determine the priority of analysis based on when questions and inquiries were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions. It can also postpone older questions. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. For example, by prioritizing the analysis of recently submitted questions, the analysis unit can ensure that learners receive support quickly. By postponing older questions, learners can receive the latest information first. By dynamically adjusting the analysis priority based on the submission date, learners can receive support efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the submission date data of questions and inquiries into the AI ​​and have the AI ​​determine the analysis priority. This allows for efficient analysis by determining the analysis priority based on the submission date of questions and inquiries.

[0043] The analysis unit can adjust the order of analysis based on the relevance of questions and inquiries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions. It can also postpone the analysis of less relevant questions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of questions. For example, by prioritizing the analysis of highly relevant questions, the analysis unit can ensure that learners receive efficient support. By postponing less relevant questions, learners can prioritize obtaining important information. By dynamically adjusting the order of analysis based on the relevance of questions, learners can receive appropriate support. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of questions and inquiries into the AI ​​and have the AI ​​adjust the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of questions and inquiries.

[0044] The information provider can adjust the level of detail in its answers based on the importance of the questions. For example, it can provide detailed answers to high-importance questions and concise answers to low-importance questions. Furthermore, it can dynamically adjust the level of detail in its answers according to the importance of the questions. For example, it can provide detailed answers to high-importance questions to enable learners to understand deeply, and concise answers to low-importance questions to enable learners to understand efficiently. By dynamically adjusting the level of detail in its answers according to the importance of the questions, it can ensure that learners receive appropriate support. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can input data on the importance of questions into the AI ​​and have the AI ​​adjust the level of detail in its answers. This allows for the efficient provision of answers by adjusting the level of detail in the answers based on the importance of the questions.

[0045] The answering unit can apply different answering algorithms depending on the category of the question or inquiry when providing answers. For example, the answering unit can apply a mathematical formula analysis algorithm to mathematical questions. It can also apply a text analysis algorithm to historical questions. Furthermore, it can apply a data analysis algorithm to scientific questions. For example, the answering unit can apply a mathematical formula analysis algorithm to mathematical questions to provide accurate answers. It can apply a text analysis algorithm to historical questions to provide highly relevant answers. It can apply a data analysis algorithm to scientific questions to provide detailed answers. Some or all of the above processing in the answering unit may be performed using AI, for example, or not. For example, the answering unit can input question or inquiry category data into an AI and have the AI ​​select an appropriate answering algorithm. This allows for the provision of more appropriate answers by applying different answering algorithms depending on the category of the question or inquiry.

[0046] The service provider can prioritize answers based on when the questions were submitted. For example, it might prioritize answers to recently submitted questions. It can also postpone older questions. Furthermore, the service provider can dynamically adjust the priority of answers based on the submission date. For example, by prioritizing answers to recently submitted questions, the service provider can ensure learners receive support quickly. By postponing older questions, learners can receive the latest information first. By dynamically adjusting the priority of answers based on the submission date, learners can receive support efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the submission date data of questions into an AI and have the AI ​​determine the priority of answers. This allows for efficient answers to be provided by prioritizing answers based on when the questions were submitted.

[0047] The information provider can adjust the order of answers based on the relevance of the questions when providing responses. For example, the information provider can prioritize answering highly relevant questions. It can also postpone less relevant questions. Furthermore, the information provider can dynamically adjust the order of answers based on the relevance of the questions. For example, by prioritizing highly relevant questions, the information provider can ensure that learners receive efficient support. By postponing less relevant questions, learners can receive important information preferentially. By dynamically adjusting the order of answers based on the relevance of the questions, learners can receive appropriate support. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can input the relevance data of questions into AI and have the AI ​​adjust the order of answers. This allows for efficient response provision by adjusting the order of answers based on the relevance of the questions.

[0048] The monitoring unit can predict current progress by referring to past progress data during monitoring. For example, the monitoring unit predicts current progress based on the learner's past progress data. The monitoring unit can also predict current progress by analyzing the learner's learning patterns from past progress data. Furthermore, the monitoring unit can dynamically predict the learner's current progress by referring to past progress data. For example, the monitoring unit predicts current progress based on the learner's past progress data, enabling the learner to learn efficiently. By analyzing the learner's learning patterns from past progress data and predicting current progress, the unit enables the learner to learn effectively. By dynamically predicting the learner's current progress by referring to past progress data, the unit enables the learner to receive appropriate support. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past progress data into AI and have the AI ​​predict current progress. This allows for efficient monitoring by predicting current progress by referring to past progress data.

[0049] The monitoring unit can apply different monitoring methods to each learner category during monitoring. For example, the monitoring unit performs basic progress monitoring for beginner learners. It can also perform detailed progress monitoring for intermediate learners. Furthermore, it can perform specialized progress monitoring for advanced learners. For example, the monitoring unit performs basic progress monitoring for beginner learners to ensure they can learn smoothly. For intermediate learners, it performs detailed progress monitoring to ensure they can deeply understand the material. For advanced learners, it performs specialized progress monitoring to ensure they can advance to a higher level of learning. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input learner category data into AI and have the AI ​​select an appropriate monitoring method. This allows for more appropriate progress monitoring by applying a monitoring method according to the learner category.

[0050] The monitoring unit can analyze changes in learner progress based on the learner's submission timing during monitoring. For example, the monitoring unit can prioritize analyzing the progress of assignments recently submitted by the learner. The monitoring unit can also postpone the analysis of progress on older assignments. Furthermore, the monitoring unit can dynamically analyze changes in progress based on submission timing. For example, by prioritizing the analysis of progress on recently submitted assignments, the monitoring unit can enable learners to learn efficiently. By postponing the analysis of progress on older assignments, learners can prioritize obtaining the latest information. By dynamically analyzing changes in progress based on submission timing, learners can receive appropriate support. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input learner submission timing data into AI and have the AI ​​perform the analysis of changes in progress. This enables efficient monitoring by analyzing changes in progress based on the learner's submission timing.

[0051] The monitoring unit can analyze the learner's progress by referring to relevant data during monitoring. For example, the monitoring unit can analyze the current progress by referring to the learner's past performance data. The monitoring unit can also analyze progress by referring to the learner's learning history data. Furthermore, the monitoring unit can dynamically analyze progress based on the learner's relevant data. For example, the monitoring unit can analyze the current progress by referring to the learner's past performance data to enable the learner to learn efficiently. By analyzing progress by referring to learning history data, the learner can learn effectively. By dynamically analyzing progress based on relevant data, the learner can receive appropriate support. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the learner's relevant data into AI and have the AI ​​perform the progress analysis. This allows for efficient analysis of progress by referring to the learner's relevant data.

[0052] The notification unit can select the optimal notification method by referring to past notification data when sending a notification. For example, the notification unit can prioritize notification methods that the learner has preferred to use in the past. The notification unit can also select the notification method to which the learner responded most enthusiastically from past notification data. Furthermore, the notification unit can dynamically select the optimal notification method based on past notification data. For example, the notification unit can prioritize notification methods that the learner has preferred to use in the past, enabling the learner to receive notifications efficiently. By selecting the notification method to which the learner responded most enthusiastically from past notification data, the learner can receive notifications effectively. By dynamically selecting the optimal notification method based on past notification data, the learner can receive appropriate support. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification data into AI and have the AI ​​select the optimal notification method. This allows the notification unit to select the optimal notification method by referring to past notification data and provide efficient notifications.

[0053] The notification unit can apply different notification methods depending on the learner's category when sending notifications. For example, the notification unit can apply a basic notification method to beginner learners. It can also apply a detailed notification method to intermediate learners. Furthermore, it can apply a specialized notification method to advanced learners. For example, the notification unit can apply a basic notification method to beginner learners to ensure they receive notifications smoothly. For intermediate learners, it can apply a detailed notification method to ensure they deeply understand the notification content. For advanced learners, it can apply a specialized notification method to ensure they receive advanced notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input learner category data into AI and have the AI ​​select an appropriate notification method. This allows for the provision of more appropriate notifications by applying a notification method according to the learner's category.

[0054] The notification unit can analyze changes in notifications based on the learner's submission timing when sending notifications. For example, the notification unit can prioritize displaying notifications related to assignments recently submitted by the learner. It can also postpone notifications related to older assignments. Furthermore, the notification unit can dynamically analyze changes in notifications based on submission timing. For example, by prioritizing the display of notifications related to assignments recently submitted by the learner, the unit can ensure that learners receive notifications efficiently. By postponing notifications related to older assignments, the unit can ensure that learners receive the latest information first. By dynamically analyzing changes in notifications based on submission timing, the unit can ensure that learners receive appropriate support. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input learner submission timing data into AI and have the AI ​​perform the analysis of changes in notifications. This allows for the provision of efficient notifications by analyzing changes in notifications based on the learner's submission timing.

[0055] The notification unit can analyze notifications by referring to the learner's relevant data when a notification is sent. For example, the notification unit can refer to the learner's past performance data to display relevant notifications. It can also refer to the learner's learning history data to display relevant notifications. Furthermore, the notification unit can dynamically analyze notifications based on the learner's relevant data. For example, the notification unit can refer to the learner's past performance data to display relevant notifications, enabling the learner to receive notifications efficiently. By referring to learning history data to display relevant notifications, the learner can receive notifications effectively. By dynamically analyzing notifications based on relevant data, the learner can receive appropriate support. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the learner's relevant data into AI and have the AI ​​perform the notification analysis. This allows for the provision of efficient notifications by referring to the learner's relevant data.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can acquire learners' physiological data and adjust the way questions and inquiries are handled based on that data. For example, if a learner has a high heart rate, the reception desk can provide a simple interface and minimize the input steps. If a learner has a low heart rate, it can provide detailed input options and suggest a customizable input method. Furthermore, if a learner has a high body temperature, it can prioritize voice input to allow for quick input of questions and inquiries. This allows for more appropriate support by adjusting the way questions and inquiries are handled according to the learner's physiological data.

[0058] The system can analyze learners' learning histories and provide personalized advice and feedback based on their past learning. For example, it can provide detailed explanations for areas where learners have struggled in the past. It can also provide application problems for areas where learners excel. Furthermore, it can analyze specific learning patterns from learners' past learning histories and suggest optimal learning methods. By providing personalized advice and feedback based on learners' learning histories, the system can maximize learning effectiveness.

[0059] The reception desk can analyze a learner's past question history and select the most suitable reception method. For example, it can automatically display as suggestions the types of questions the learner has frequently asked in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the learner has used in the past. Furthermore, it can predict and suggest the types of questions a learner might ask during specific time periods based on their past question history. In this way, by analyzing a learner's past question history, the system can select the most suitable reception method and provide efficient support.

[0060] The reception desk can filter questions and inquiries based on the learner's current learning status and areas of interest. For example, it can prioritize receiving relevant questions based on the learner's current learning progress. It can also prioritize receiving highly relevant questions based on the learner's areas of interest. Furthermore, it can filter and receive appropriate questions considering the learner's current learning status. This allows for prioritizing the receipt of highly relevant questions by filtering based on the learner's current learning status and areas of interest.

[0061] The reception desk can prioritize receiving questions by considering the learner's geographical location. For example, if a learner is in a specific region, questions related to that region will be prioritized. Similarly, if a learner is on the move, questions related to their current location will be prioritized. Furthermore, if a learner is in a specific location, questions related to that location will be prioritized. This allows the reception desk to prioritize highly relevant questions by considering the learner's geographical location.

[0062] The reception desk can analyze learners' social media activity when receiving questions and inquiries, and prioritize relevant questions. For example, it can prioritize questions related to topics that learners frequently mention on social media. It can also analyze learners' current interests from their social media activity and prioritize relevant questions. Furthermore, it can prioritize questions related to accounts that learners follow on social media. In this way, by analyzing learners' social media activity, it is possible to prioritize the reception of relevant questions.

[0063] The analysis unit can adjust the level of detail in the analysis based on the importance of the questions or inquiries. For example, it can provide detailed analysis results for high-importance questions and concise results for low-importance questions. Furthermore, it can dynamically adjust the level of detail in the analysis according to the importance of the questions. This allows for efficient analysis by adjusting the level of detail in the analysis based on the importance of the questions or inquiries.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives learners' questions and inquiries. These questions and inquiries can be in text, audio, or image formats. For example, text questions can be entered via keyboard, audio questions via microphone, and image questions via camera. Step 2: The analysis unit analyzes the questions and inquiries received by the reception unit. The analysis is performed using natural language processing technology, machine learning algorithms, and image analysis technology. For example, text-based questions are analyzed using natural language processing technology, audio-based questions using machine learning algorithms, and image-based questions using image analysis technology. Step 3: The providing unit provides appropriate answers based on the results analyzed by the analysis unit. Appropriate answers are provided based on criteria such as accuracy, relevance, and ease of understanding. For example, individual advice and feedback may be provided, taking into account the learner's learning style and progress. Step 4: The monitoring unit monitors the learner's progress based on the responses provided by the delivery unit. Progress is monitored based on criteria such as learning achievement, time elapsed, and assignment completion status. Step 5: The notification unit notifies the teacher of the progress information monitored by the monitoring unit. Notifications are made via methods such as email, push notifications, and dashboard displays.

[0066] (Example of form 2) The smart learning assistant system according to an embodiment of the present invention is an innovative AI tool designed to support learners. This smart learning assistant system allows learners to input questions or doubts, which the AI ​​then analyzes and provides appropriate answers. The AI ​​provides optimal support, taking into account the learner's learning style and progress. For example, if a learner has difficulty understanding a mathematical problem, the AI ​​provides a detailed explanation to help them understand. The AI ​​also monitors the learner's progress and provides individualized advice and feedback as needed. This allows learners to learn at their own pace and maximize their learning effectiveness. Furthermore, the smart learning assistant system also includes a teacher collaboration function, enabling teachers to provide more effective instruction based on the learner's progress information provided by the AI. This reduces the burden on teachers while providing appropriate support to each individual learner. The smart learning assistant system provides an environment where learners can receive the necessary support 24 hours a day, allowing them to learn at their own pace. For example, learners input questions or doubts. These can be entered in various formats, such as text, audio, or image. The AI ​​then analyzes the question and provides an appropriate answer. For example, AI uses natural language processing technology to analyze questions and provide accurate and relevant answers. Furthermore, AI monitors learners' progress and provides personalized advice and feedback as needed. For instance, AI considers learners' learning styles and progress to provide optimal support. This allows learners to learn at their own pace and maximize learning effectiveness. As a result, smart learning assistant systems can efficiently receive, analyze, and provide appropriate answers to learners' questions and concerns, monitor progress, and notify teachers.

[0067] The smart learning assistant system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a monitoring unit, and a notification unit. The reception unit receives questions and inquiries from learners. These questions and inquiries may include, but are not limited to, text format, audio format, or image format. For example, the reception unit can receive questions in text format. The reception unit can also receive questions in audio format. Furthermore, the reception unit can also receive questions in image format. For example, the reception unit can receive questions entered by learners in text format. For questions in audio format, for example, audio can be entered using a microphone. For questions in image format, for example, images can be entered using a camera. The analysis unit analyzes the questions and inquiries received by the reception unit. The analysis may be performed using, but is not limited to, natural language processing techniques or machine learning algorithms. For example, the analysis unit can analyze questions in text format using natural language processing techniques. Furthermore, the analysis unit can also analyze questions in audio format using machine learning algorithms. Furthermore, the analysis unit can also analyze questions in image format using image analysis techniques. For example, the analysis unit analyzes text-based questions using natural language processing technology and provides appropriate answers. Machine learning algorithms analyze audio-based questions and provide appropriate answers. Image analysis technology analyzes image-based questions and provides appropriate answers. The delivery unit provides appropriate answers based on the results analyzed by the analysis unit. Appropriate answers are provided based on criteria such as accuracy, relevance, and ease of understanding, but are not limited to these examples. For example, the delivery unit provides accurate and relevant answers based on the results analyzed by the analysis unit. The delivery unit can also provide individualized advice and feedback, taking into account the learner's learning style and progress. For example, the delivery unit provides answers tailored to the learner's learning style, such as visual learning, auditory learning, or experiential learning. The monitoring unit monitors the learner's progress based on the answers provided by the delivery unit. Progress is monitored based on criteria such as learning achievement, time elapsed, and task completion, but is not limited to these examples.For example, the monitoring unit monitors the learner's learning achievement. The monitoring unit can also monitor the elapsed learning time of the learner. Furthermore, the monitoring unit can also monitor the learner's completion status of assignments. For example, the monitoring unit monitors the learner's learning achievement and provides progress information. The notification unit notifies the teacher of the progress information monitored by the monitoring unit. Notifications are made by methods such as email, push notifications, and dashboard displays, but are not limited to these examples. For example, the notification unit notifies the teacher of progress information using email. The notification unit can also notify the teacher of progress information using push notifications. Furthermore, the notification unit can also notify the teacher of progress information using a dashboard display. For example, the notification unit notifies the teacher of progress information using email so that the teacher can grasp the learner's progress. As a result, the smart learning assistant system according to the embodiment can efficiently receive, analyze, provide appropriate answers to learners' questions and inquiries, monitor progress, and notify the teacher.

[0068] The reception desk receives learners' questions and inquiries. These questions and inquiries may include, but are not limited to, text, audio, and image formats. For example, the reception desk can accept questions in text format. It can also accept questions in audio format. Furthermore, it can also accept questions in image format. For example, the reception desk can accept questions entered by learners in text format. Questions in audio format can be entered using a microphone, for example. Questions in image format can be entered using a camera, for example. The reception desk works in conjunction with various input devices to accept these diverse question formats. For example, questions in text format are entered using a keyboard or touchscreen, questions in audio format are received as audio data via a microphone, and questions in image format are received as image data captured using a camera. This allows learners to submit questions in the way that is most convenient for them, and the system can efficiently receive them. Furthermore, the reception desk also plays a role in converting the received questions into the appropriate format and sending them to the analysis unit. For example, questions in audio format are converted to text format using speech recognition technology, and questions in image format are converted to text information using image analysis technology. This allows the analysis unit to analyze questions in a consistent format. The reception unit takes learner convenience into maximum consideration and supports a variety of input formats, enabling it to quickly and accurately receive learners' questions and concerns.

[0069] The analysis unit analyzes questions and inquiries received by the reception unit. Analysis is performed using, for example, natural language processing techniques and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit can analyze text-based questions using natural language processing techniques. The analysis unit can also analyze voice-based questions using machine learning algorithms. Furthermore, the analysis unit can analyze image-based questions using image analysis techniques. For example, the analysis unit can analyze text-based questions using natural language processing techniques and provide appropriate answers. Machine learning algorithms can, for example, analyze voice-based questions and provide appropriate answers. Image analysis techniques can, for example, analyze image-based questions and provide appropriate answers. The analysis unit utilizes these techniques to accurately understand the intent of the learner's questions and derive the optimal answer. Specifically, when analyzing text-based questions using natural language processing techniques, the analysis unit extracts the context and keywords of the question and searches for appropriate information from relevant knowledge bases and databases. When analyzing voice-based questions, the analysis unit converts the voice data into text using speech recognition techniques, and then applies natural language processing techniques for analysis. When analyzing questions in image format, image recognition technology is used to identify text and objects within the image, and the intent of the question is understood based on this. The analysis unit combines these technologies to provide quick and accurate answers to learners' questions. Furthermore, the analysis unit can learn from past question and answer data and build a feedback loop to improve analysis accuracy. As a result, the analysis unit can respond to a wide range of learners' questions with high accuracy and maximize learning effectiveness.

[0070] The provider unit provides appropriate answers based on the results analyzed by the analysis unit. Appropriate answers are provided based on criteria such as accuracy, relevance, and ease of understanding, but are not limited to these examples. For example, the provider unit provides accurate and relevant answers based on the results analyzed by the analysis unit. The provider unit can also provide individual advice and feedback, taking into account the learner's learning style and progress. For example, the provider unit provides answers tailored to the learner's learning style, such as visual learning, auditory learning, or experiential learning. Based on the analysis results received from the analysis unit, the provider unit provides answers in the format best suited to the learner. For example, visual learners are provided with visual explanations using charts and graphs, and auditory learners are provided with explanations using audio and video. Experiential learners can deepen their understanding by showing actual operating procedures or experimental procedures. The provider unit also provides feedback at the appropriate time, taking into account the learner's progress. For example, when a learner completes a particular task, the provider unit evaluates the results and provides advice for moving on to the next step. Furthermore, the provider unit can provide additional supplementary information and reference materials depending on the learner's level of understanding. This allows learners to progress at their own pace, leading to a deeper understanding. Furthermore, the service provider collects learner feedback and accumulates data to continuously improve the quality of the answers provided. This enables the service provider to consistently offer learners the most optimal answers, maximizing learning effectiveness.

[0071] The monitoring unit monitors learners' progress based on responses provided by the provision unit. Progress is monitored based on criteria such as learning achievement, elapsed time, and assignment completion status, but is not limited to these examples. For example, the monitoring unit monitors learners' learning achievement. The monitoring unit can also monitor the elapsed time of learners' learning. Furthermore, the monitoring unit can also monitor the completion status of learners' assignments. For example, the monitoring unit monitors learners' learning achievement and provides progress information. The monitoring unit tracks learners' learning activities in real time and records learning progress in detail. Specifically, it monitors which assignments learners have completed, to what extent, how much time they have spent on them, and what level of understanding they have shown. This allows for the collection of data to understand learners' learning patterns and trends and to provide individualized learning support. Furthermore, the monitoring unit visualizes learners' progress, allowing learners to check their own progress. For example, it displays progress in graphs and charts, allowing learners to grasp their achievement level and assignment progress at a glance. Furthermore, the monitoring department provides timely feedback based on the learner's progress, supporting them in maintaining their motivation to learn. This allows learners to constantly understand their own learning status and proceed with their studies effectively.

[0072] The notification unit notifies teachers of progress information monitored by the monitoring unit. Notifications are made by methods such as email, push notifications, and dashboard displays, but are not limited to these. For example, the notification unit can notify teachers of progress information via email. The notification unit can also notify teachers of progress information via push notifications. Furthermore, the notification unit can notify teachers of progress information via dashboard displays. For example, the notification unit can notify teachers of progress information via email so that teachers can keep track of learners' progress. The notification unit plays a crucial role in quickly and accurately conveying learner progress information to teachers. Specifically, it organizes the progress data received from the monitoring unit and notifies teachers in a format that is easy for teachers to understand. For example, it displays a list of each learner's progress, clearly indicating which tasks a particular learner is struggling with and what level of progress they have made. The notification unit also provides a dashboard so that teachers can check progress information in real time. The dashboard visually displays learners' progress, achievement levels, and task completion status, enabling teachers to respond quickly. Furthermore, the notification unit can set up alerts for specific events and important progress, and notify teachers immediately. This allows teachers to constantly monitor learners' progress and provide necessary support quickly. The notification system effectively manages learners' progress information and facilitates smooth communication between teachers and learners, thereby maximizing learning effectiveness.

[0073] The service provider can offer individualized advice and feedback, taking into account the learner's learning style and progress. For example, the service provider can provide advice and feedback tailored to the learner's learning style, such as visual learning, auditory learning, or experiential learning. For instance, the service provider can provide visual advice using diagrams and graphs to visual learners. It can also provide auditory advice using audio to auditory learners. Furthermore, it can provide advice based on actual experiences to experiential learners. This allows for the maximization of learning effectiveness by providing individualized advice and feedback tailored to the learner's learning style and progress.

[0074] The reception system can estimate the learner's emotions and adjust how questions and inquiries are received based on the estimated emotions. For example, if the learner is stressed, the reception system can provide a simple interface and minimize the input steps. If the learner is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the learner is in a hurry, the reception system can prioritize voice input to allow for quick input of questions and inquiries. For example, if the learner is stressed, the reception system can provide a simple interface and minimize the input steps to allow the learner to input questions and inquiries smoothly. If the learner is relaxed, it can provide detailed input options and suggest customizable input methods to allow the learner to input questions and inquiries in a way that suits them. If the learner is in a hurry, it can prioritize voice input to allow for quick input of questions and inquiries, enabling the learner to receive support efficiently. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate support by adjusting how questions and inquiries are received according to the learner's emotions.

[0075] The reception desk can analyze the learner's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions the content of questions the learner has frequently asked in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the learner has used in the past. Furthermore, the reception desk can predict and suggest the content of questions that will be used during specific time periods based on the learner's past question history. For example, the reception desk can automatically display as suggestions the content of questions the learner has frequently asked in the past, allowing the learner to smoothly input questions and doubts. By prioritizing suggesting reception methods that have been used in the past, the learner can input questions and doubts in a familiar way. By predicting and suggesting the content of questions that will be used during specific time periods based on past question history, the learner can receive support efficiently. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the learner's past question history into AI and have the AI ​​select the optimal reception method. This allows the reception desk to select the optimal reception method by analyzing the learner's past question history and provide efficient support.

[0076] The reception desk can filter questions and inquiries based on the learner's current learning status and areas of interest. For example, the reception desk can prioritize receiving relevant questions based on the learner's current learning progress. It can also prioritize receiving highly relevant questions based on the learner's areas of interest. Furthermore, the reception desk can filter and receive appropriate questions considering the learner's current learning status. For example, by prioritizing the reception desk based on the learner's current learning progress, the learner can receive support efficiently. By prioritizing highly relevant questions based on areas of interest, the learner can proceed with their learning with interest. By filtering and receiving appropriate questions considering the current learning status, the learner can proceed with their learning effectively. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the learner's current learning status and areas of interest into the AI ​​and have the AI ​​perform the filtering. This allows for prioritizing the reception of highly relevant questions by filtering based on the learner's current learning status and areas of interest.

[0077] The reception desk can estimate the learner's emotions and prioritize the questions to be answered based on those emotions. For example, if the learner is stressed, the reception desk will prioritize urgent questions. If the learner is relaxed, the reception desk may also prioritize detailed questions. Furthermore, if the learner is in a hurry, the reception desk may prioritize concise questions. For example, if the learner is stressed, the reception desk will prioritize urgent questions to ensure the learner receives support quickly. If the learner is relaxed, prioritizing detailed questions will allow the learner to gain a deeper understanding. If the learner is in a hurry, prioritizing concise questions will allow the learner to receive support efficiently. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate support to be provided by prioritizing questions according to the learner's emotions.

[0078] The reception desk can prioritize receiving questions that are highly relevant, taking into account the learner's geographical location when receiving inquiries. For example, if the learner is in a specific region, the reception desk will prioritize receiving questions related to that region. Furthermore, if the learner is on the move, the reception desk can prioritize receiving questions related to their current location. Additionally, if the learner is in a specific location, the reception desk can prioritize receiving questions related to that location, enabling the learner to efficiently obtain region-related information. If the learner is on the move, prioritizing questions related to their current location ensures they receive appropriate support even while traveling. If the learner is in a specific location, prioritizing questions related to that location ensures they efficiently obtain location-related information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the learner's geographical location information into the AI ​​and have the AI ​​select highly relevant questions. This allows for prioritizing the reception of highly relevant questions by taking into account the learner's geographical location.

[0079] The reception desk can analyze learners' social media activity when receiving questions and inquiries, and accept relevant questions. For example, the reception desk can prioritize questions related to topics that learners frequently mention on social media. The reception desk can also analyze learners' current interests from their social media activity and accept relevant questions. Furthermore, the reception desk can prioritize questions related to accounts that learners follow on social media. For example, by prioritizing questions related to topics that learners frequently mention on social media, the reception desk can efficiently obtain information on topics that interest learners. By analyzing current interests from social media activity and accepting relevant questions, learners can proceed with their learning in an engaging manner. By prioritizing questions related to accounts that learners follow on social media, learners can efficiently obtain information related to those accounts. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input learners' social media activity data into AI and have the AI ​​select relevant questions. This allows the reception desk to prioritize the acceptance of relevant questions by analyzing learners' social media activity.

[0080] The analysis unit can estimate the learner's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the learner is stressed, the analysis unit provides simple and easy-to-understand analysis results. If the learner is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the learner is in a hurry, the analysis unit can provide concise analysis results. For example, if the learner is stressed, the analysis unit provides simple and easy-to-understand analysis results to allow the learner to understand smoothly. If the learner is relaxed, it provides detailed analysis results to allow the learner to understand deeply. If the learner is in a hurry, it provides concise analysis results to allow the learner to understand efficiently. 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. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the learner's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions and doubts during the analysis. For example, the analysis unit provides detailed analysis results for high-importance questions. It can also provide concise analysis results for low-importance questions. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the questions. For example, the analysis unit provides detailed analysis results for high-importance questions to enable learners to understand deeply. For low-importance questions, it provides concise analysis results to enable learners to understand efficiently. By dynamically adjusting the level of detail of the analysis according to the importance of the questions, learners can receive appropriate support. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the questions and doubts into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the questions and doubts.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the question or inquiry during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to mathematical questions. It can also apply a text analysis algorithm to historical questions. Furthermore, it can apply a data analysis algorithm to scientific questions. For example, the analysis unit applies a mathematical formula analysis algorithm to mathematical questions to provide accurate analysis results. It applies a text analysis algorithm to historical questions to provide highly relevant analysis results. It applies a data analysis algorithm to scientific questions to provide detailed analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question or inquiry category data into the AI ​​and have the AI ​​select an appropriate analysis algorithm. This allows for the application of different analysis algorithms depending on the category of the question or inquiry, thereby providing more appropriate analysis results.

[0083] The analysis unit can estimate the learner's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the learner is in a hurry, the analysis unit will provide a short, concise analysis. If the learner is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the learner is excited, the analysis unit can provide a visually stimulating analysis. For example, if the learner is in a hurry, the analysis unit will provide a short, concise analysis to allow the learner to understand quickly. If the learner is relaxed, it will provide a detailed analysis to allow the learner to understand deeply. If the learner is excited, it will provide a visually stimulating analysis to keep the learner interested and engaged in the learning process. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate analysis results by adjusting the length of the analysis according to the learner's emotions.

[0084] The analysis unit can determine the priority of analysis based on when questions and inquiries were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions. It can also postpone older questions. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. For example, by prioritizing the analysis of recently submitted questions, the analysis unit can ensure that learners receive support quickly. By postponing older questions, learners can receive the latest information first. By dynamically adjusting the analysis priority based on the submission date, learners can receive support efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the submission date data of questions and inquiries into the AI ​​and have the AI ​​determine the analysis priority. This allows for efficient analysis by determining the analysis priority based on the submission date of questions and inquiries.

[0085] The analysis unit can adjust the order of analysis based on the relevance of questions and inquiries during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions. It can also postpone the analysis of less relevant questions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of questions. For example, by prioritizing the analysis of highly relevant questions, the analysis unit can ensure that learners receive efficient support. By postponing less relevant questions, learners can prioritize obtaining important information. By dynamically adjusting the order of analysis based on the relevance of questions, learners can receive appropriate support. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of questions and inquiries into the AI ​​and have the AI ​​adjust the order of analysis. This allows for efficient analysis by adjusting the order of analysis based on the relevance of questions and inquiries.

[0086] The system can estimate the learner's emotions and adjust the way it presents its responses based on those emotions. For example, if the learner is stressed, the system will provide a simple and easily understandable response. If the learner is relaxed, the system can provide a more detailed response. Furthermore, if the learner is in a hurry, the system can provide a concise response. For instance, if the learner is stressed, the system will provide a simple and easily understandable response to ensure smooth comprehension. If the learner is relaxed, it will provide a detailed response to allow for deeper understanding. If the learner is in a hurry, it will provide a concise response to ensure efficient comprehension. 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. This allows for the provision of more appropriate responses by adjusting the way the responses are presented according to the learner's emotions.

[0087] The information provider can adjust the level of detail in its answers based on the importance of the questions. For example, it can provide detailed answers to high-importance questions and concise answers to low-importance questions. Furthermore, it can dynamically adjust the level of detail in its answers according to the importance of the questions. For example, it can provide detailed answers to high-importance questions to enable learners to understand deeply, and concise answers to low-importance questions to enable learners to understand efficiently. By dynamically adjusting the level of detail in its answers according to the importance of the questions, it can ensure that learners receive appropriate support. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can input data on the importance of questions into the AI ​​and have the AI ​​adjust the level of detail in its answers. This allows for the efficient provision of answers by adjusting the level of detail in the answers based on the importance of the questions.

[0088] The answering unit can apply different answering algorithms depending on the category of the question or inquiry when providing answers. For example, the answering unit can apply a mathematical formula analysis algorithm to mathematical questions. It can also apply a text analysis algorithm to historical questions. Furthermore, it can apply a data analysis algorithm to scientific questions. For example, the answering unit can apply a mathematical formula analysis algorithm to mathematical questions to provide accurate answers. It can apply a text analysis algorithm to historical questions to provide highly relevant answers. It can apply a data analysis algorithm to scientific questions to provide detailed answers. Some or all of the above processing in the answering unit may be performed using AI, for example, or not. For example, the answering unit can input question or inquiry category data into an AI and have the AI ​​select an appropriate answering algorithm. This allows for the provision of more appropriate answers by applying different answering algorithms depending on the category of the question or inquiry.

[0089] The provider can estimate the learner's emotions and adjust the length of the response based on the estimated emotions. For example, if the learner is in a hurry, the provider will provide a short, concise response. If the learner is relaxed, the provider can also provide a detailed response. Furthermore, if the learner is excited, the provider can provide a visually stimulating response. For example, if the learner is in a hurry, the provider will provide a short, concise response to allow the learner to understand quickly. If the learner is relaxed, it will provide a detailed response to allow the learner to understand deeply. If the learner is excited, it will provide a visually stimulating response to keep the learner interested and engaged in the learning process. Emotion estimation is achieved using emotion estimation functions, 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. This allows for the provision of more appropriate responses by adjusting the length of the response according to the learner's emotions.

[0090] The service provider can prioritize answers based on when the questions were submitted. For example, it might prioritize answers to recently submitted questions. It can also postpone older questions. Furthermore, the service provider can dynamically adjust the priority of answers based on the submission date. For example, by prioritizing answers to recently submitted questions, the service provider can ensure learners receive support quickly. By postponing older questions, learners can receive the latest information first. By dynamically adjusting the priority of answers based on the submission date, learners can receive support efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the submission date data of questions into an AI and have the AI ​​determine the priority of answers. This allows for efficient answers to be provided by prioritizing answers based on when the questions were submitted.

[0091] The information provider can adjust the order of answers based on the relevance of the questions when providing responses. For example, the information provider can prioritize answering highly relevant questions. It can also postpone less relevant questions. Furthermore, the information provider can dynamically adjust the order of answers based on the relevance of the questions. For example, by prioritizing highly relevant questions, the information provider can ensure that learners receive efficient support. By postponing less relevant questions, learners can receive important information preferentially. By dynamically adjusting the order of answers based on the relevance of the questions, learners can receive appropriate support. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can input the relevance data of questions into AI and have the AI ​​adjust the order of answers. This allows for efficient response provision by adjusting the order of answers based on the relevance of the questions.

[0092] The monitoring unit can estimate the learner's emotions and adjust the progress monitoring method based on the estimated learner's emotions. For example, if the learner is stressed, the monitoring unit will perform simple and highly visible progress monitoring. If the learner is relaxed, the monitoring unit can also perform detailed progress monitoring. Furthermore, if the learner is in a hurry, the monitoring unit can perform concise progress monitoring. For example, if the learner is stressed, the monitoring unit will perform simple and highly visible progress monitoring to allow the learner to grasp the progress smoothly. If the learner is relaxed, it will perform detailed progress monitoring to allow the learner to understand the progress more deeply. If the learner is in a hurry, it will perform concise progress monitoring to allow the learner to grasp the progress efficiently. 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. This allows for more appropriate progress monitoring by adjusting the progress monitoring method according to the learner's emotions.

[0093] The monitoring unit can predict current progress by referring to past progress data during monitoring. For example, the monitoring unit predicts current progress based on the learner's past progress data. The monitoring unit can also predict current progress by analyzing the learner's learning patterns from past progress data. Furthermore, the monitoring unit can dynamically predict the learner's current progress by referring to past progress data. For example, the monitoring unit predicts current progress based on the learner's past progress data, enabling the learner to learn efficiently. By analyzing the learner's learning patterns from past progress data and predicting current progress, the unit enables the learner to learn effectively. By dynamically predicting the learner's current progress by referring to past progress data, the unit enables the learner to receive appropriate support. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past progress data into AI and have the AI ​​predict current progress. This allows for efficient monitoring by predicting current progress by referring to past progress data.

[0094] The monitoring unit can apply different monitoring methods to each learner category during monitoring. For example, the monitoring unit performs basic progress monitoring for beginner learners. It can also perform detailed progress monitoring for intermediate learners. Furthermore, it can perform specialized progress monitoring for advanced learners. For example, the monitoring unit performs basic progress monitoring for beginner learners to ensure they can learn smoothly. For intermediate learners, it performs detailed progress monitoring to ensure they can deeply understand the material. For advanced learners, it performs specialized progress monitoring to ensure they can advance to a higher level of learning. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input learner category data into AI and have the AI ​​select an appropriate monitoring method. This allows for more appropriate progress monitoring by applying a monitoring method according to the learner category.

[0095] The monitoring unit can estimate the learner's emotions and adjust the importance of progress based on the estimated emotions. For example, if the learner is stressed, the monitoring unit will prioritize monitoring high-importance progress. The monitoring unit can also monitor detailed progress if the learner is relaxed. Furthermore, if the learner is in a hurry, the monitoring unit can monitor concise progress. For example, if the learner is stressed, the monitoring unit prioritizes monitoring high-importance progress to allow the learner to learn efficiently. If the learner is relaxed, detailed progress monitoring allows the learner to understand the material more deeply. If the learner is in a hurry, concise progress monitoring allows the learner to learn efficiently. 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. This allows for more appropriate progress monitoring by adjusting the importance of progress according to the learner's emotions.

[0096] The monitoring unit can analyze changes in learner progress based on the learner's submission timing during monitoring. For example, the monitoring unit can prioritize analyzing the progress of assignments recently submitted by the learner. The monitoring unit can also postpone the analysis of progress on older assignments. Furthermore, the monitoring unit can dynamically analyze changes in progress based on submission timing. For example, by prioritizing the analysis of progress on recently submitted assignments, the monitoring unit can enable learners to learn efficiently. By postponing the analysis of progress on older assignments, learners can prioritize obtaining the latest information. By dynamically analyzing changes in progress based on submission timing, learners can receive appropriate support. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input learner submission timing data into AI and have the AI ​​perform the analysis of changes in progress. This enables efficient monitoring by analyzing changes in progress based on the learner's submission timing.

[0097] The monitoring unit can analyze the learner's progress by referring to relevant data during monitoring. For example, the monitoring unit can analyze the current progress by referring to the learner's past performance data. The monitoring unit can also analyze progress by referring to the learner's learning history data. Furthermore, the monitoring unit can dynamically analyze progress based on the learner's relevant data. For example, the monitoring unit can analyze the current progress by referring to the learner's past performance data to enable the learner to learn efficiently. By analyzing progress by referring to learning history data, the learner can learn effectively. By dynamically analyzing progress based on relevant data, the learner can receive appropriate support. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the learner's relevant data into AI and have the AI ​​perform the progress analysis. This allows for efficient analysis of progress by referring to the learner's relevant data.

[0098] The notification unit can estimate the learner's emotions and adjust how notifications are displayed based on the estimated emotions. For example, if the learner is stressed, the notification unit will display a simple and highly visible notification. It can also display a detailed notification if the learner is relaxed. Furthermore, if the learner is in a hurry, the notification unit can display a concise notification. For instance, if the learner is stressed, the notification unit will display a simple and highly visible notification to allow the learner to easily understand the content. If the learner is relaxed, it will display a detailed notification to allow the learner to deeply understand the content. If the learner is in a hurry, it will display a concise notification to allow the learner to efficiently understand the content. 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. This allows for the provision of more appropriate notifications by adjusting how notifications are displayed according to the learner's emotions.

[0099] The notification unit can select the optimal notification method by referring to past notification data when sending a notification. For example, the notification unit can prioritize notification methods that the learner has preferred to use in the past. The notification unit can also select the notification method to which the learner responded most enthusiastically from past notification data. Furthermore, the notification unit can dynamically select the optimal notification method based on past notification data. For example, the notification unit can prioritize notification methods that the learner has preferred to use in the past, enabling the learner to receive notifications efficiently. By selecting the notification method to which the learner responded most enthusiastically from past notification data, the learner can receive notifications effectively. By dynamically selecting the optimal notification method based on past notification data, the learner can receive appropriate support. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification data into AI and have the AI ​​select the optimal notification method. This allows the notification unit to select the optimal notification method by referring to past notification data and provide efficient notifications.

[0100] The notification unit can apply different notification methods depending on the learner's category when sending notifications. For example, the notification unit can apply a basic notification method to beginner learners. It can also apply a detailed notification method to intermediate learners. Furthermore, it can apply a specialized notification method to advanced learners. For example, the notification unit can apply a basic notification method to beginner learners to ensure they receive notifications smoothly. For intermediate learners, it can apply a detailed notification method to ensure they deeply understand the notification content. For advanced learners, it can apply a specialized notification method to ensure they receive advanced notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input learner category data into AI and have the AI ​​select an appropriate notification method. This allows for the provision of more appropriate notifications by applying a notification method according to the learner's category.

[0101] The notification unit can estimate the learner's emotions and prioritize notifications based on those emotions. For example, if the learner is stressed, the notification unit will prioritize displaying urgent notifications. It can also prioritize displaying detailed notifications if the learner is relaxed. Furthermore, if the learner is in a hurry, it can prioritize displaying concise notifications. For instance, if the learner is stressed, the notification unit prioritizes urgent notifications to allow for a quick response. If the learner is relaxed, prioritizing detailed notifications allows for a deeper understanding of the notification content. If the learner is in a hurry, prioritizing concise notifications allows for efficient understanding of the notification content. 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. This allows for the provision of more appropriate notifications by prioritizing notifications according to the learner's emotions.

[0102] The notification unit can analyze changes in notifications based on the learner's submission timing when sending notifications. For example, the notification unit can prioritize displaying notifications related to assignments recently submitted by the learner. It can also postpone notifications related to older assignments. Furthermore, the notification unit can dynamically analyze changes in notifications based on submission timing. For example, by prioritizing the display of notifications related to assignments recently submitted by the learner, the unit can ensure that learners receive notifications efficiently. By postponing notifications related to older assignments, the unit can ensure that learners receive the latest information first. By dynamically analyzing changes in notifications based on submission timing, the unit can ensure that learners receive appropriate support. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input learner submission timing data into AI and have the AI ​​perform the analysis of changes in notifications. This allows for the provision of efficient notifications by analyzing changes in notifications based on the learner's submission timing.

[0103] The notification unit can analyze notifications by referring to the learner's relevant data when a notification is sent. For example, the notification unit can refer to the learner's past performance data to display relevant notifications. It can also refer to the learner's learning history data to display relevant notifications. Furthermore, the notification unit can dynamically analyze notifications based on the learner's relevant data. For example, the notification unit can refer to the learner's past performance data to display relevant notifications, enabling the learner to receive notifications efficiently. By referring to learning history data to display relevant notifications, the learner can receive notifications effectively. By dynamically analyzing notifications based on relevant data, the learner can receive appropriate support. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the learner's relevant data into AI and have the AI ​​perform the notification analysis. This allows for the provision of efficient notifications by referring to the learner's relevant data.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can acquire learners' physiological data and adjust the way questions and inquiries are handled based on that data. For example, if a learner has a high heart rate, the reception desk can provide a simple interface and minimize the input steps. If a learner has a low heart rate, it can provide detailed input options and suggest a customizable input method. Furthermore, if a learner has a high body temperature, it can prioritize voice input to allow for quick input of questions and inquiries. This allows for more appropriate support by adjusting the way questions and inquiries are handled according to the learner's physiological data.

[0106] The system can analyze learners' learning histories and provide personalized advice and feedback based on their past learning. For example, it can provide detailed explanations for areas where learners have struggled in the past. It can also provide application problems for areas where learners excel. Furthermore, it can analyze specific learning patterns from learners' past learning histories and suggest optimal learning methods. By providing personalized advice and feedback based on learners' learning histories, the system can maximize learning effectiveness.

[0107] The reception system can estimate the learner's emotions and adjust how questions and inquiries are handled based on that estimation. For example, if a learner is stressed, it can provide a simple interface and minimize the input steps. If the learner is relaxed, it can offer detailed input options and suggest customizable input methods. Furthermore, if the learner is in a hurry, it can prioritize voice input to allow for quick entry of questions and inquiries. This allows for more appropriate support by adjusting how questions and inquiries are handled according to the learner's emotions.

[0108] The reception desk can analyze a learner's past question history and select the most suitable reception method. For example, it can automatically display as suggestions the types of questions the learner has frequently asked in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the learner has used in the past. Furthermore, it can predict and suggest the types of questions a learner might ask during specific time periods based on their past question history. In this way, by analyzing a learner's past question history, the system can select the most suitable reception method and provide efficient support.

[0109] The reception desk can filter questions and inquiries based on the learner's current learning status and areas of interest. For example, it can prioritize receiving relevant questions based on the learner's current learning progress. It can also prioritize receiving highly relevant questions based on the learner's areas of interest. Furthermore, it can filter and receive appropriate questions considering the learner's current learning status. This allows for prioritizing the receipt of highly relevant questions by filtering based on the learner's current learning status and areas of interest.

[0110] The reception desk can estimate the learner's emotions and prioritize questions based on those estimates. For example, if a learner is stressed, urgent questions will be prioritized. If the learner is relaxed, detailed questions may be prioritized. Furthermore, if the learner is in a hurry, concise questions may be prioritized. This allows for more appropriate support to be provided by prioritizing questions according to the learner's emotions.

[0111] The reception desk can prioritize receiving questions by considering the learner's geographical location. For example, if a learner is in a specific region, questions related to that region will be prioritized. Similarly, if a learner is on the move, questions related to their current location will be prioritized. Furthermore, if a learner is in a specific location, questions related to that location will be prioritized. This allows the reception desk to prioritize highly relevant questions by considering the learner's geographical location.

[0112] The reception desk can analyze learners' social media activity when receiving questions and inquiries, and prioritize relevant questions. For example, it can prioritize questions related to topics that learners frequently mention on social media. It can also analyze learners' current interests from their social media activity and prioritize relevant questions. Furthermore, it can prioritize questions related to accounts that learners follow on social media. In this way, by analyzing learners' social media activity, it is possible to prioritize the reception of relevant questions.

[0113] The analysis unit can estimate the learner's emotions and adjust the presentation of the analysis based on those emotions. For example, if the learner is stressed, it can provide a simple and easy-to-understand analysis result. If the learner is relaxed, it can provide a detailed analysis result. Furthermore, if the learner is in a hurry, it can provide a concise analysis result. By adjusting the presentation of the analysis according to the learner's emotions, it is possible to provide more appropriate analysis results.

[0114] The analysis unit can adjust the level of detail in the analysis based on the importance of the questions or inquiries. For example, it can provide detailed analysis results for high-importance questions and concise results for low-importance questions. Furthermore, it can dynamically adjust the level of detail in the analysis according to the importance of the questions. This allows for efficient analysis by adjusting the level of detail in the analysis based on the importance of the questions or inquiries.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives learners' questions and inquiries. These questions and inquiries can be in text, audio, or image formats. For example, text questions can be entered via keyboard, audio questions via microphone, and image questions via camera. Step 2: The analysis unit analyzes the questions and inquiries received by the reception unit. The analysis is performed using natural language processing technology, machine learning algorithms, and image analysis technology. For example, text-based questions are analyzed using natural language processing technology, audio-based questions using machine learning algorithms, and image-based questions using image analysis technology. Step 3: The providing unit provides appropriate answers based on the results analyzed by the analysis unit. Appropriate answers are provided based on criteria such as accuracy, relevance, and ease of understanding. For example, individual advice and feedback may be provided, taking into account the learner's learning style and progress. Step 4: The monitoring unit monitors the learner's progress based on the responses provided by the delivery unit. Progress is monitored based on criteria such as learning achievement, time elapsed, and assignment completion status. Step 5: The notification unit notifies the teacher of the progress information monitored by the monitoring unit. Notifications are made via methods such as email, push notifications, and dashboard displays.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives learner questions and inquiries in text, voice, and image formats. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes questions and inquiries using natural language processing technology and machine learning algorithms. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate answers based on the analysis results. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the learner's progress. The notification unit is implemented by the output device 40 of the smart device 14 and notifies the teacher of progress information. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and camera 42 of the smart glasses 214, and receives learners' questions and inquiries in voice or image format. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes questions and inquiries using natural language processing technology and machine learning algorithms. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides appropriate answers based on the analysis results. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and monitors the learner's progress. The notification unit is implemented, for example, by the speaker 240 of the smart glasses 214, and notifies the teacher of progress information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and camera 42 of the headset terminal 314, and receives learners' questions and inquiries in voice or image format. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes questions and inquiries using natural language processing technology and machine learning algorithms. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides appropriate answers based on the analysis results. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and monitors the learner's progress. The notification unit is implemented by, for example, the speaker 240 of the headset terminal 314, and notifies the teacher of progress information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, monitoring unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and camera 42 of the robot 414, and receives learners' questions and inquiries in voice or image format. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes questions and inquiries using natural language processing technology and machine learning algorithms. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides appropriate answers based on the analysis results. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and monitors the learner's progress. The notification unit is implemented by, for example, the speaker 240 of the robot 414, and notifies the teacher of progress information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception desk to receive learners' questions and inquiries, An analysis unit analyzes the questions and inquiries received by the reception unit, A providing unit that provides an appropriate answer based on the results of the analysis performed by the aforementioned analysis unit, A monitoring unit monitors the learner's progress based on the answers provided by the aforementioned provisioning unit, The system includes a notification unit that notifies the teacher of the progress information monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We provide individualized advice and feedback, taking into account the learner's learning style and progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system estimates the learner's emotions and adjusts how questions and inquiries are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Analyze the learner's past question history and select the most suitable method of receiving questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving questions or inquiries, filtering is performed based on the learner's current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the learner's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving questions or inquiries, the system prioritizes accepting questions that are highly relevant to the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving questions or inquiries, the system analyzes the learner's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the learner's emotions and adjusts the representation of the analysis based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the questions or concerns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the question or inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the learner's emotions and adjusts the length of the analysis based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when questions and inquiries were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the questions and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, The system estimates the learner's emotions and adjusts the way the response is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing answers, adjust the level of detail in the response based on the importance of the question or inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing answers, different answer algorithms are applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the learner's emotions and adjusts the length of the response based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing answers, we prioritize responses based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing answers, we adjust the order of the answers based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, The system estimates learners' emotions and adjusts progress monitoring methods based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, During monitoring, past progress data is used to predict current progress. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, During monitoring, different monitoring methods are applied for each learner category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, 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 25) The monitoring unit, During monitoring, analyze changes in progress based on when learners submit their work. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, analyze progress by referring to relevant data on the learner. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, The system estimates the learner's emotions and adjusts how notifications are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending a notification, the system will refer to past notification data to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, different notification methods are applied depending on the learner's category. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, The system estimates the learner's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, analyze how the notification changes based on when learners submit their work. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, the system analyzes the notifications by referring to the learner's relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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. A reception desk to receive learners' questions and inquiries, An analysis unit analyzes the questions and inquiries received by the reception unit, A providing unit that provides an appropriate answer based on the results of the analysis performed by the aforementioned analysis unit, A monitoring unit monitors the learner's progress based on the answers provided by the aforementioned provisioning unit, The system includes a notification unit that notifies the teacher of the progress information monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned supply unit is, We provide individualized advice and feedback, taking into account the learner's learning style and progress. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the learner's emotions and adjusts how questions and inquiries are received based on those estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is Analyze the learner's past question history and select the most suitable method of receiving questions. The system according to feature 1.

5. The aforementioned reception unit is When receiving questions or inquiries, filtering is performed based on the learner's current learning situation and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the learner's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving questions or inquiries, the system prioritizes accepting questions that are highly relevant to the learner's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When receiving questions or inquiries, the system analyzes the learner's social media activity and selects relevant questions. The system according to feature 1.

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