system

Abstract

The system according to this embodiment aims to monitor a child's learning progress and provide a personalized learning plan. [Solution] The system according to the embodiment comprises a monitoring unit, a plan provision unit, and a communication unit. The monitoring unit monitors learning progress. The plan provision unit provides a personalized learning plan based on the data collected by the monitoring unit. The communication unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to monitor the learning progress of children in real time and provide a personalized learning plan, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the learning progress of children and provide a personalized learning plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a plan provision unit, and a communication unit. The monitoring unit monitors learning progress. The plan provision unit provides a personalized learning plan based on the data collected by the monitoring unit. The communication unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor a child's learning progress and provide a personalized learning plan. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​virtual tutoring system according to an embodiment of the present invention is a system that targets dual-income families and utilizes natural language processing (NLP) and machine learning (ML) to monitor a child's learning progress in real time and provide a personalized learning plan. This system also aims to improve the quality of education by supporting communication between parents and teachers. First, the AI ​​virtual tutoring system monitors the child's learning progress in real time. Specifically, it collects data on the child's learning and builds a question-answering system using NLP. As a result, if the child has a question while learning, the AI ​​provides an immediate answer. Next, based on the collected data, it uses ML to analyze and optimize individual learning progress. This makes it possible to provide a personalized learning plan tailored to each child. For example, if a child has difficulty in a particular subject, a plan is created that focuses on studying that area. Furthermore, the AI ​​virtual tutoring system is equipped with a feedback system that supports communication between parents and teachers. The AI ​​analyzes the child's learning situation and provides feedback on the results to parents and teachers. This makes it easier for parents and teachers to understand the child's learning situation and provide appropriate support. For example, the AI ​​virtual tutoring system collects data on the child's learning situation and builds a question-answering system using NLP. This allows the AI ​​to instantly provide answers to any questions a child may have while learning. Next, based on the collected data, machine learning (ML) is used to analyze and optimize individual learning progress. This enables the provision of personalized learning plans tailored to each child. For example, if a child struggles with a particular subject, a plan focusing on that area can be created. Furthermore, the AI ​​virtual tutoring system includes a feedback system to support communication between parents and teachers. The AI ​​analyzes the child's learning progress and provides feedback to parents and teachers. This makes it easier for parents and teachers to understand the child's learning situation and provide appropriate support. As a result, the AI ​​virtual tutoring system is expected to improve learning efficiency and reduce learning time by an average of 20%.Furthermore, concrete quantifiable effects were observed, such as a 15% improvement in students' average grades and 90% of parents being satisfied with the service.

[0029] The AI ​​virtual tutoring system according to this embodiment comprises a monitoring unit, a plan provision unit, and a communication unit. The monitoring unit monitors learning progress. For example, the monitoring unit collects data on when a child is learning and monitors learning progress in real time. For example, the monitoring unit builds a question-answering system in which the AI ​​provides immediate answers if a child has questions while learning. For example, the monitoring unit analyzes the child's learning progress and optimizes individual learning progress. The plan provision unit provides a personalized learning plan based on the data collected by the monitoring unit. For example, if a child has difficulty in a particular subject, the plan provision unit creates a plan that focuses on studying that area. For example, the plan provision unit provides a personalized learning plan tailored to each child. For example, the plan provision unit analyzes and optimizes individual learning progress using ML based on the collected data. The communication unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. For example, the communication unit uses AI to analyze the child's learning situation and provides feedback to parents and teachers. The communication section, for example, makes it easier for parents and teachers to understand a child's learning progress and provide appropriate support. The communication section includes, for example, a feedback system that supports communication between parents and teachers. As a result, the AI ​​virtual tutoring system according to the embodiment can improve the quality of education by monitoring learning progress in real time, providing personalized learning plans, and supporting communication between parents and teachers.

[0030] The monitoring unit monitors learning progress. For example, the monitoring unit collects data on children's learning and monitors their learning progress in real time. Specifically, it collects data such as learning time, accuracy of answers, speed of answers, and level of understanding of learning content through applications and software installed on the devices used by the children. This data is sent to a cloud server, where AI analyzes it in real time. For example, the monitoring unit builds a question-answering system in which AI provides immediate answers if a child has a question while learning. The AI ​​uses natural language processing technology to understand the child's question and generates an appropriate answer. For example, if a child gets stuck on a math problem, the AI ​​explains the solution to the problem step by step, supporting the child so that they can understand it. For example, the monitoring unit analyzes the child's learning progress and optimizes individual learning progress. Based on the collected data, the AI ​​identifies the child's strengths and weaknesses and visualizes their learning progress. This allows for an understanding of the child's learning trends and patterns, and the suggestion of the optimal learning method. Furthermore, the monitoring unit regularly compiles learning progress into reports and provides them to parents and teachers. This makes it easier for parents and teachers to understand their children's learning progress and provide appropriate support. The monitoring unit uses AI machine learning algorithms to continuously learn from children's learning data, achieving more accurate progress monitoring. As a result, the monitoring unit can grasp children's learning progress in real time and provide support tailored to their individual needs.

[0031] The plan provision department provides personalized learning plans based on data collected by the monitoring department. Specifically, AI analyzes children's learning data and creates optimal learning plans tailored to individual learning needs. For example, if a child has difficulty in a particular subject, the plan will focus on that area. The AI ​​determines which topics and skills should be emphasized based on the child's past learning data and current level of understanding. The plan provision department provides personalized learning plans tailored to each child. The AI ​​proposes an optimal learning schedule that matches the child's learning style and pace. For example, for a child who learns intensely in short bursts, the plan will provide a plan with multiple short sessions, while for a child who can concentrate for longer periods, longer sessions will be suggested. The plan provision department uses machine learning to analyze and optimize individual learning progress based on collected data. The AI ​​continuously learns from children's learning data to improve the accuracy of learning plans. For example, if a child repeatedly struggles with a particular problem, the plan will provide a plan to review the fundamental concepts related to that problem. This allows the plan provision department to optimize children's learning progress and support effective learning. Furthermore, the plan provision department regularly evaluates the progress of the learning plan and modifies it as needed. This ensures that the optimal plan is always provided, tailored to the student's current learning situation.

[0032] The Communications Department supports communication between parents and teachers based on the learning plans provided by the Plan Provision Department. Specifically, AI analyzes the child's learning progress and provides feedback to parents and teachers. The AI ​​analyzes learning data and visualizes the child's learning progress, understanding, strengths, and weaknesses. This allows parents and teachers to grasp the child's learning situation at a glance. The Communications Department, for example, makes it easier for parents and teachers to understand the child's learning situation and provide appropriate support. The AI ​​visually displays learning progress and assignment completion in graphs and charts and provides them to parents and teachers. This makes it easier for parents and teachers to intuitively understand the child's learning situation. The Communications Department, for example, has a feedback system that supports communication between parents and teachers. The AI ​​periodically generates reports on the child's learning situation and sends them to parents and teachers. Based on these reports, parents and teachers can discuss the child's learning situation and consider appropriate support methods. Furthermore, the Communications Department collects feedback from parents and teachers and uses it for the AI ​​to improve learning plans and support methods. This allows the communications department to support smooth communication between parents and teachers and effectively assist children's learning.

[0033] The question-answering unit constructs a question-answering system. The question-answering unit provides immediate answers, for example, when a child has a question while learning. The question-answering unit constructs an automated response system using, for example, natural language processing technology. The question-answering unit provides answers to children's questions, for example, by utilizing an FAQ database. The question-answering unit provides optimal answers to children's questions, for example, by using AI. This allows the question-answering unit to provide immediate answers when a child has a question while learning. Some or all of the above-described processes in the question-answering unit may be performed using, for example, AI, or without using AI. For example, the question-answering unit can perform question-answering using an AI model that analyzes children's questions using natural language processing technology and generates optimal answers.

[0034] The analysis unit analyzes learning progress. For example, the analysis unit collects and analyzes data on children's learning progress. For example, the analysis unit optimizes individual learning progress based on the collected data. For example, the analysis unit uses AI to analyze children's learning progress. For example, the analysis unit uses data collection methods and analysis algorithms to analyze learning progress. This allows the analysis unit to optimize individual learning progress and provide personalized learning plans. 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 collected data into an AI model and have the AI ​​perform the analysis of learning progress.

[0035] The feedback unit provides feedback. The feedback unit, for example, analyzes a child's learning progress and provides feedback to parents or teachers based on the results. The feedback unit provides feedback in methods such as text messages or graph displays. The feedback unit, for example, uses AI to analyze a child's learning progress and provides feedback based on the results. The feedback unit makes it easier for parents and teachers to understand a child's learning progress and provide appropriate support. This allows the feedback unit to make it easier for parents and teachers to understand a child's learning progress and provide appropriate support. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input a child's learning progress into an AI model and have the AI ​​generate the content of the feedback.

[0036] The monitoring unit can collect data while considering the child's learning environment when monitoring learning progress. For example, the monitoring unit can measure the noise level of the learning environment and recommend learning in a quiet environment. For example, the monitoring unit can measure the brightness of the lighting and recommend learning at an appropriate brightness. For example, the monitoring unit can measure the temperature of the learning environment and recommend learning at a comfortable temperature. By collecting data while considering the learning environment, it becomes possible to monitor learning progress more accurately. 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 learning environment data into a generating AI and have the generating AI perform an evaluation of the learning environment.

[0037] The monitoring unit can improve the accuracy of monitoring by referring to the child's past learning history when monitoring learning progress. For example, the monitoring unit can analyze past learning history to understand the child's level of understanding of specific subjects or topics. For example, the monitoring unit can predict learning progress based on past learning history to improve the accuracy of monitoring. For example, the monitoring unit can refer to past learning history and provide appropriate feedback according to the child's learning progress. In this way, the accuracy of monitoring can be improved by referring to past learning history. 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 learning history data into a generating AI and have the generating AI perform a prediction of learning progress.

[0038] The monitoring unit can collect data while considering the child's health condition when monitoring learning progress. For example, the monitoring unit can record the child's sleep time and recommend that the child get enough sleep. For example, the monitoring unit can record the child's meals and recommend a balanced diet. For example, the monitoring unit can record the child's exercise level and recommend moderate exercise. By collecting data while considering the child's health condition, more accurate monitoring of learning progress becomes possible. 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 child's health data into a generating AI and have the generating AI perform an assessment of the child's health condition.

[0039] The monitoring unit can analyze a child's social media activity and evaluate its impact on learning when monitoring learning progress. For example, the monitoring unit can record the child's social media usage time and recommend balancing it with study time. For example, the monitoring unit can analyze the content of the child's social media activities and evaluate their impact on learning. For example, the monitoring unit can analyze the child's social media usage patterns and adjust them so as not to interfere with learning. In this way, by analyzing social media activity, the impact on learning can be evaluated and appropriate feedback can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the child's social media data into a generating AI and have the generating AI perform an evaluation of social media activity.

[0040] The plan provider can customize learning plans according to the child's learning style when providing them. For example, the plan provider can provide a learning plan that makes extensive use of diagrams and graphs for visually-oriented children. For example, the plan provider can provide a learning plan that makes extensive use of audio and video for auditory-oriented children. For example, the plan provider can provide a learning plan that includes activities that involve hands-on learning for tactile-oriented children. By customizing the plan according to the learning style, the efficiency of learning can be improved. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input the child's learning style data into a generating AI and have the generating AI perform the customization of the learning plan.

[0041] The plan provider can determine the priority of a learning plan based on the child's learning goals when providing a learning plan. For example, the plan provider can determine the content to be prioritized based on the child's short-term learning goals. For example, the plan provider can provide a plan that allows the child to progress through learning in stages based on the child's medium-term learning goals. For example, the plan provider can create an overall learning plan based on the child's long-term learning goals. This enables efficient learning by determining the priority of the plan based on learning goals. Some or all of the above processes in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input the child's learning goal data into a generating AI and have the generating AI perform the determination of plan priorities.

[0042] The plan provider can customize learning plans by taking into account the child's home environment when providing them. For example, the plan provider can provide learning plans that include content requiring home support, depending on the parents' educational level. For example, the plan provider can provide cost-effective learning resources, depending on the family's economic situation. For example, the plan provider can provide appropriate learning content, depending on the family's cultural background. By customizing the plan to take the home environment into account, a more appropriate learning plan can be provided. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input data on the child's home environment into a generating AI and have the generating AI perform the customization of the learning plan.

[0043] The plan provider can adjust the learning plan to take into account the child's extracurricular activities when providing the plan. For example, the plan provider can adjust the learning plan to match the child's sports activity schedule. For example, the plan provider can adjust the learning plan to match the child's music activity schedule. For example, the plan provider can adjust the learning plan to match the child's other extracurricular activity schedule. By adjusting the plan to take extracurricular activities into consideration, a balance can be achieved between learning and extracurricular activities. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input the child's extracurricular activity data into a generating AI and have the generating AI perform the adjustment of the learning plan.

[0044] The communication department can provide optimal support methods by referring to the past communication history of parents and teachers during communication support. For example, the communication department can analyze past communication history and provide support tailored to the communication styles of parents and teachers. For example, the communication department can provide information tailored to the interests of parents and teachers based on past communication history. For example, the communication department can refer to past communication history and adjust the frequency of communication between parents and teachers. In this way, by referring to past communication history, the communication department can provide optimal support methods. Some or all of the above processes in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input parent-teacher communication history data into a generating AI and have the generating AI determine the support method.

[0045] The communication department can provide support at the optimal time, taking into account the schedules of parents and teachers. For example, the communication department can adjust the schedules of parents and teachers to support communication at the optimal time. For example, the communication department can support communication while avoiding busy times for parents and teachers. For example, the communication department can provide communication reminders according to the schedules of parents and teachers. This makes it possible to provide communication support at the optimal time by taking into account the schedules of parents and teachers. Some or all of the above processes in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input parent and teacher schedule data into a generating AI and have the generating AI determine the optimal timing.

[0046] The communication department can provide the most suitable communication method when supporting communication, taking into account the geographical distance between parents and teachers. For example, if parents and teachers are in distant locations, the communication department may recommend an online meeting. For example, if parents and teachers are in close proximity, the communication department may recommend face-to-face communication. The communication department provides the most suitable communication method according to the geographical distance between parents and teachers. This allows the communication department to provide the most suitable communication method by considering the geographical distance between parents and teachers. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input geographical distance data between parents and teachers into a generating AI and have the generating AI determine the communication method.

[0047] The communication department can adjust the content of communication when providing communication support, taking into account the cultural backgrounds of parents and teachers. For example, the communication department can provide an appropriate communication style according to the cultural backgrounds of parents and teachers. For example, the communication department can adjust the content of communication, taking into account the cultural backgrounds of parents and teachers. For example, the communication department can provide appropriate feedback based on the cultural backgrounds of parents and teachers. This makes more appropriate communication possible by taking into account the cultural backgrounds of parents and teachers. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input cultural background data of parents and teachers into a generating AI and have the generating AI perform the adjustment of the communication content.

[0048] The question-answering unit can provide the best answer by referring to the child's past question history when answering a question. For example, the question-answering unit can analyze past question history and provide the best answer to similar questions. For example, the question-answering unit can provide an answer that is appropriate to the child's level of understanding based on past question history. For example, the question-answering unit can refer to past question history and provide relevant additional information. In this way, the best answer can be provided by referring to past question history. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input past question history data into a generating AI and have the generating AI perform the generation of the best answer.

[0049] The question-answering unit can adjust the difficulty level of questions based on the child's learning progress when answering questions. For example, the question-answering unit provides questions of appropriate difficulty level according to the child's learning progress. For example, the question-answering unit adjusts the difficulty level of questions based on the child's level of understanding. For example, the question-answering unit refers to the child's learning progress and provides questions that gradually increase in difficulty. This makes it possible to provide appropriate learning support by adjusting the difficulty level of questions based on learning progress. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the child's learning progress data into a generating AI and have the generating AI perform the adjustment of the difficulty level of the questions.

[0050] The analysis unit can improve the accuracy of its analysis by referring to the child's past learning data when analyzing learning progress. For example, the analysis unit can analyze past learning data to understand the level of comprehension of a particular subject or topic. For example, the analysis unit can predict learning progress based on past learning data and improve the accuracy of its analysis. For example, the analysis unit can refer to past learning data and provide appropriate feedback according to learning progress. In this way, the accuracy of the analysis can be improved by referring to past learning data. 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 past learning data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0051] The analysis unit can take into account the child's learning environment when analyzing learning progress. For example, the analysis unit can consider the noise level of the learning environment when analyzing learning progress. For example, the analysis unit can consider the brightness of the lighting in the learning environment when analyzing learning progress. For example, the analysis unit can consider the temperature of the learning environment when analyzing learning progress. By taking the learning environment into account, a more accurate analysis of learning progress becomes possible. 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 learning environment data into a generating AI and have the generating AI perform the analysis of learning progress.

[0052] The feedback unit can determine the priority of feedback based on the child's learning progress when providing feedback. For example, the feedback unit may determine what should be prioritized in feedback based on the child's short-term learning progress. For example, the feedback unit may provide feedback in stages based on the child's medium-term learning progress. For example, the feedback unit may create an overall feedback plan based on the child's long-term learning progress. This enables efficient feedback by determining the priority of feedback based on learning progress. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may input the child's learning progress data into a generating AI and have the generating AI perform the determination of feedback priorities.

[0053] The feedback unit can provide optimal feedback by referring to the communication history between parents and teachers when providing feedback. For example, the feedback unit can analyze past communication history and provide feedback tailored to the communication style of parents and teachers. For example, the feedback unit can provide feedback tailored to the concerns of parents and teachers based on past communication history. For example, the feedback unit can refer to past communication history and adjust the frequency of communication between parents and teachers. In this way, optimal feedback can be provided by referring to the communication history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input parent-teacher communication history data into a generating AI and have the generating AI perform the generation of optimal feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The monitoring unit can adjust the monitoring method according to the child's learning style when monitoring the child's learning progress. For example, visually-oriented children can be monitored using graphs and charts. Auditory-oriented children can be monitored using audio feedback. Tactile-oriented children can be monitored through interactive activities. By monitoring according to the child's learning style, the efficiency of learning can be improved.

[0056] The question-answering section can adjust the content of questions based on the child's learning progress. For example, if a child has difficulty with a particular subject, the system can focus on providing questions related to that area. For subjects the child excels in, more advanced questions can be provided. Furthermore, the difficulty level of the questions can be gradually increased according to the child's learning progress. This allows for question-and-answer sessions tailored to the child's learning progress, thereby improving the efficiency of learning.

[0057] The analysis department can prioritize analysis based on the child's learning goals when analyzing a child's learning progress. For example, it can determine what should be prioritized for analysis based on the child's short-term learning goals. It can proceed with analysis in stages based on medium-term learning goals. It can create an overall learning plan based on long-term learning goals. By prioritizing analysis based on learning goals, efficient learning support becomes possible.

[0058] The feedback system can provide optimal feedback by referencing the communication history between parents and teachers. For example, it can analyze past communication history and provide feedback tailored to the communication styles of parents and teachers. It can also provide feedback aligned with the concerns of parents and teachers. Furthermore, it can adjust the frequency of communication between parents and teachers. This allows for the provision of optimal feedback by referencing the communication history.

[0059] The monitoring unit can collect data while monitoring learning progress, taking into account the child's health condition. For example, it can record the child's sleep time and recommend getting enough sleep. It can record the child's meals and recommend a balanced diet. It can record the child's exercise level and recommend moderate exercise. By collecting data while considering the child's health condition, it becomes possible to monitor learning progress more accurately.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The monitoring unit monitors learning progress. For example, the monitoring unit collects data on how children learn and monitors their learning progress in real time. For example, the monitoring unit builds a question-answering system in which AI provides immediate answers if a child has questions while learning. For example, the monitoring unit analyzes the child's learning progress and optimizes individual learning progress. Step 2: The plan provision department provides personalized learning plans based on data collected by the monitoring department. For example, if a student has difficulty in a particular subject, the plan provision department will create a plan that focuses on that area. For example, the plan provision department will provide personalized learning plans tailored to each individual child. For example, the plan provision department will use machine learning to analyze and optimize individual learning progress based on the collected data. Step 3: The Communication Department supports communication between parents and teachers based on the learning plan provided by the Plan Provision Department. For example, the Communication Department uses AI to analyze the child's learning progress and provides feedback to parents and teachers. For example, the Communication Department makes it easier for parents and teachers to understand the child's learning progress and provide appropriate support. For example, the Communication Department has a feedback system that supports communication between parents and teachers.

[0062] (Example of form 2) The AI ​​virtual tutoring system according to an embodiment of the present invention is a system that targets dual-income families and utilizes natural language processing (NLP) and machine learning (ML) to monitor a child's learning progress in real time and provide a personalized learning plan. This system also aims to improve the quality of education by supporting communication between parents and teachers. First, the AI ​​virtual tutoring system monitors the child's learning progress in real time. Specifically, it collects data on the child's learning and builds a question-answering system using NLP. As a result, if the child has a question while learning, the AI ​​provides an immediate answer. Next, based on the collected data, it uses ML to analyze and optimize individual learning progress. This makes it possible to provide a personalized learning plan tailored to each child. For example, if a child has difficulty in a particular subject, a plan is created that focuses on studying that area. Furthermore, the AI ​​virtual tutoring system is equipped with a feedback system that supports communication between parents and teachers. The AI ​​analyzes the child's learning situation and provides feedback on the results to parents and teachers. This makes it easier for parents and teachers to understand the child's learning situation and provide appropriate support. For example, the AI ​​virtual tutoring system collects data on the child's learning situation and builds a question-answering system using NLP. This allows the AI ​​to instantly provide answers to any questions a child may have while learning. Next, based on the collected data, machine learning (ML) is used to analyze and optimize individual learning progress. This enables the provision of personalized learning plans tailored to each child. For example, if a child struggles with a particular subject, a plan focusing on that area can be created. Furthermore, the AI ​​virtual tutoring system includes a feedback system to support communication between parents and teachers. The AI ​​analyzes the child's learning progress and provides feedback to parents and teachers. This makes it easier for parents and teachers to understand the child's learning situation and provide appropriate support. As a result, the AI ​​virtual tutoring system is expected to improve learning efficiency and reduce learning time by an average of 20%.Furthermore, concrete quantifiable effects were observed, such as a 15% improvement in students' average grades and 90% of parents being satisfied with the service.

[0063] The AI ​​virtual tutoring system according to this embodiment comprises a monitoring unit, a plan provision unit, and a communication unit. The monitoring unit monitors learning progress. For example, the monitoring unit collects data on when a child is learning and monitors learning progress in real time. For example, the monitoring unit builds a question-answering system in which the AI ​​provides immediate answers if a child has questions while learning. For example, the monitoring unit analyzes the child's learning progress and optimizes individual learning progress. The plan provision unit provides a personalized learning plan based on the data collected by the monitoring unit. For example, if a child has difficulty in a particular subject, the plan provision unit creates a plan that focuses on studying that area. For example, the plan provision unit provides a personalized learning plan tailored to each child. For example, the plan provision unit analyzes and optimizes individual learning progress using ML based on the collected data. The communication unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. For example, the communication unit uses AI to analyze the child's learning situation and provides feedback to parents and teachers. The communication section, for example, makes it easier for parents and teachers to understand a child's learning progress and provide appropriate support. The communication section includes, for example, a feedback system that supports communication between parents and teachers. As a result, the AI ​​virtual tutoring system according to the embodiment can improve the quality of education by monitoring learning progress in real time, providing personalized learning plans, and supporting communication between parents and teachers.

[0064] The monitoring unit monitors learning progress. For example, the monitoring unit collects data on children's learning and monitors their learning progress in real time. Specifically, it collects data such as learning time, accuracy of answers, speed of answers, and level of understanding of learning content through applications and software installed on the devices used by the children. This data is sent to a cloud server, where AI analyzes it in real time. For example, the monitoring unit builds a question-answering system in which AI provides immediate answers if a child has a question while learning. The AI ​​uses natural language processing technology to understand the child's question and generates an appropriate answer. For example, if a child gets stuck on a math problem, the AI ​​explains the solution to the problem step by step, supporting the child so that they can understand it. For example, the monitoring unit analyzes the child's learning progress and optimizes individual learning progress. Based on the collected data, the AI ​​identifies the child's strengths and weaknesses and visualizes their learning progress. This allows for an understanding of the child's learning trends and patterns, and the suggestion of the optimal learning method. Furthermore, the monitoring unit regularly compiles learning progress into reports and provides them to parents and teachers. This makes it easier for parents and teachers to understand their children's learning progress and provide appropriate support. The monitoring unit uses AI machine learning algorithms to continuously learn from children's learning data, achieving more accurate progress monitoring. As a result, the monitoring unit can grasp children's learning progress in real time and provide support tailored to their individual needs.

[0065] The plan provision department provides personalized learning plans based on data collected by the monitoring department. Specifically, AI analyzes children's learning data and creates optimal learning plans tailored to individual learning needs. For example, if a child has difficulty in a particular subject, the plan will focus on that area. The AI ​​determines which topics and skills should be emphasized based on the child's past learning data and current level of understanding. The plan provision department provides personalized learning plans tailored to each child. The AI ​​proposes an optimal learning schedule that matches the child's learning style and pace. For example, for a child who learns intensely in short bursts, the plan will provide a plan with multiple short sessions, while for a child who can concentrate for longer periods, longer sessions will be suggested. The plan provision department uses machine learning to analyze and optimize individual learning progress based on collected data. The AI ​​continuously learns from children's learning data to improve the accuracy of learning plans. For example, if a child repeatedly struggles with a particular problem, the plan will provide a plan to review the fundamental concepts related to that problem. This allows the plan provision department to optimize children's learning progress and support effective learning. Furthermore, the plan provision department regularly evaluates the progress of the learning plan and modifies it as needed. This ensures that the optimal plan is always provided, tailored to the student's current learning situation.

[0066] The Communications Department supports communication between parents and teachers based on the learning plans provided by the Plan Provision Department. Specifically, AI analyzes the child's learning progress and provides feedback to parents and teachers. The AI ​​analyzes learning data and visualizes the child's learning progress, understanding, strengths, and weaknesses. This allows parents and teachers to grasp the child's learning situation at a glance. The Communications Department, for example, makes it easier for parents and teachers to understand the child's learning situation and provide appropriate support. The AI ​​visually displays learning progress and assignment completion in graphs and charts and provides them to parents and teachers. This makes it easier for parents and teachers to intuitively understand the child's learning situation. The Communications Department, for example, has a feedback system that supports communication between parents and teachers. The AI ​​periodically generates reports on the child's learning situation and sends them to parents and teachers. Based on these reports, parents and teachers can discuss the child's learning situation and consider appropriate support methods. Furthermore, the Communications Department collects feedback from parents and teachers and uses it for the AI ​​to improve learning plans and support methods. This allows the communications department to support smooth communication between parents and teachers and effectively assist children's learning.

[0067] The question-answering unit constructs a question-answering system. The question-answering unit provides immediate answers, for example, when a child has a question while learning. The question-answering unit constructs an automated response system using, for example, natural language processing technology. The question-answering unit provides answers to children's questions, for example, by utilizing an FAQ database. The question-answering unit provides optimal answers to children's questions, for example, by using AI. This allows the question-answering unit to provide immediate answers when a child has a question while learning. Some or all of the above-described processes in the question-answering unit may be performed using, for example, AI, or without using AI. For example, the question-answering unit can perform question-answering using an AI model that analyzes children's questions using natural language processing technology and generates optimal answers.

[0068] The analysis unit analyzes learning progress. For example, the analysis unit collects and analyzes data on children's learning progress. For example, the analysis unit optimizes individual learning progress based on the collected data. For example, the analysis unit uses AI to analyze children's learning progress. For example, the analysis unit uses data collection methods and analysis algorithms to analyze learning progress. This allows the analysis unit to optimize individual learning progress and provide personalized learning plans. 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 collected data into an AI model and have the AI ​​perform the analysis of learning progress.

[0069] The feedback unit provides feedback. The feedback unit, for example, analyzes a child's learning progress and provides feedback to parents or teachers based on the results. The feedback unit provides feedback in methods such as text messages or graph displays. The feedback unit, for example, uses AI to analyze a child's learning progress and provides feedback based on the results. The feedback unit makes it easier for parents and teachers to understand a child's learning progress and provide appropriate support. This allows the feedback unit to make it easier for parents and teachers to understand a child's learning progress and provide appropriate support. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input a child's learning progress into an AI model and have the AI ​​generate the content of the feedback.

[0070] The monitoring unit can estimate a child's emotions and adjust the frequency of monitoring learning progress based on the estimated emotions. For example, if a child is stressed, the monitoring unit can reduce the monitoring frequency and increase the time the child can relax. For example, if a child is concentrating, the monitoring unit can increase the monitoring frequency and gain a more detailed understanding of their learning progress. For example, if a child is tired, the monitoring unit can adjust the monitoring frequency and encourage them to take a break. By adjusting the monitoring frequency according to the child's emotions, the efficiency of learning can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The monitoring unit can collect data while considering the child's learning environment when monitoring learning progress. For example, the monitoring unit can measure the noise level of the learning environment and recommend learning in a quiet environment. For example, the monitoring unit can measure the brightness of the lighting and recommend learning at an appropriate brightness. For example, the monitoring unit can measure the temperature of the learning environment and recommend learning at a comfortable temperature. By collecting data while considering the learning environment, it becomes possible to monitor learning progress more accurately. 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 learning environment data into a generating AI and have the generating AI perform an evaluation of the learning environment.

[0072] The monitoring unit can improve the accuracy of monitoring by referring to the child's past learning history when monitoring learning progress. For example, the monitoring unit can analyze past learning history to understand the child's level of understanding of specific subjects or topics. For example, the monitoring unit can predict learning progress based on past learning history to improve the accuracy of monitoring. For example, the monitoring unit can refer to past learning history and provide appropriate feedback according to the child's learning progress. In this way, the accuracy of monitoring can be improved by referring to past learning history. 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 learning history data into a generating AI and have the generating AI perform a prediction of learning progress.

[0073] The monitoring unit can estimate a child's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if a child is stressed, the monitoring unit provides a simple and visually user-friendly display method. For example, if a child is relaxed, the monitoring unit provides a display method that includes detailed information. For example, if a child is focused, the monitoring unit provides a concise display method. By adjusting the display method of the monitoring results according to the child's emotions, the understanding of learning can be deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 child's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0074] The monitoring unit can collect data while considering the child's health condition when monitoring learning progress. For example, the monitoring unit can record the child's sleep time and recommend that the child get enough sleep. For example, the monitoring unit can record the child's meals and recommend a balanced diet. For example, the monitoring unit can record the child's exercise level and recommend moderate exercise. By collecting data while considering the child's health condition, more accurate monitoring of learning progress becomes possible. 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 child's health data into a generating AI and have the generating AI perform an assessment of the child's health condition.

[0075] The monitoring unit can analyze a child's social media activity and evaluate its impact on learning when monitoring learning progress. For example, the monitoring unit can record the child's social media usage time and recommend balancing it with study time. For example, the monitoring unit can analyze the content of the child's social media activities and evaluate their impact on learning. For example, the monitoring unit can analyze the child's social media usage patterns and adjust them so as not to interfere with learning. In this way, by analyzing social media activity, the impact on learning can be evaluated and appropriate feedback can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the child's social media data into a generating AI and have the generating AI perform an evaluation of social media activity.

[0076] The plan provider can estimate a child's emotions and adjust the content of the learning plan based on those emotions. For example, if a child is stressed, the plan provider can provide a learning plan with relaxing content. For example, if a child is focused, the plan provider can provide a learning plan with challenging content. For example, if a child is tired, the plan provider can provide a learning plan that includes breaks. By adjusting the content of the learning plan according to the child's emotions, the efficiency of learning can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan provider may be performed using AI, for example, or not using AI. For example, the plan provider can input child emotion data into the generative AI and have the generative AI perform the adjustment of the learning plan.

[0077] The plan provider can customize learning plans according to the child's learning style when providing them. For example, the plan provider can provide a learning plan that makes extensive use of diagrams and graphs for visually-oriented children. For example, the plan provider can provide a learning plan that makes extensive use of audio and video for auditory-oriented children. For example, the plan provider can provide a learning plan that includes activities that involve hands-on learning for tactile-oriented children. By customizing the plan according to the learning style, the efficiency of learning can be improved. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input the child's learning style data into a generating AI and have the generating AI perform the customization of the learning plan.

[0078] The plan provider can determine the priority of a learning plan based on the child's learning goals when providing a learning plan. For example, the plan provider can determine the content to be prioritized based on the child's short-term learning goals. For example, the plan provider can provide a plan that allows the child to progress through learning in stages based on the child's medium-term learning goals. For example, the plan provider can create an overall learning plan based on the child's long-term learning goals. This enables efficient learning by determining the priority of the plan based on learning goals. Some or all of the above processes in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input the child's learning goal data into a generating AI and have the generating AI perform the determination of plan priorities.

[0079] The plan provider can estimate a child's emotions and adjust the progress of the learning plan based on those emotions. For example, if a child is stressed, the plan provider can slow down the progress of the learning plan. For example, if a child is concentrating, the plan provider can speed up the progress of the learning plan. For example, if a child is tired, the plan provider can adjust the progress of the learning plan and include breaks. By adjusting the progress of the learning plan according to the child's emotions, the efficiency of learning can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input child emotion data into the generative AI and have the generative AI adjust the progress of the learning plan.

[0080] The plan provider can customize learning plans by taking into account the child's home environment when providing them. For example, the plan provider can provide learning plans that include content requiring home support, depending on the parents' educational level. For example, the plan provider can provide cost-effective learning resources, depending on the family's economic situation. For example, the plan provider can provide appropriate learning content, depending on the family's cultural background. By customizing the plan to take the home environment into account, a more appropriate learning plan can be provided. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input data on the child's home environment into a generating AI and have the generating AI perform the customization of the learning plan.

[0081] The plan provider can adjust the learning plan to take into account the child's extracurricular activities when providing the plan. For example, the plan provider can adjust the learning plan to match the child's sports activity schedule. For example, the plan provider can adjust the learning plan to match the child's music activity schedule. For example, the plan provider can adjust the learning plan to match the child's other extracurricular activity schedule. By adjusting the plan to take extracurricular activities into consideration, a balance can be achieved between learning and extracurricular activities. Some or all of the above processing in the plan provider may be performed using AI, for example, or without AI. For example, the plan provider can input the child's extracurricular activity data into a generating AI and have the generating AI perform the adjustment of the learning plan.

[0082] The communication unit can estimate the emotions of parents and teachers and adjust the content of communication based on the estimated emotions. For example, if a parent is stressed, the communication unit will provide simple, to-the-point communication. For example, if a teacher is relaxed, the communication unit will provide communication that includes detailed information. For example, if both parents and teachers are tense, the communication unit will provide communication in a calm tone. This allows for more effective communication by adjusting the content of communication according to the emotions of parents and teachers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input parent and teacher emotion data into a generative AI and have the generative AI adjust the content of communication.

[0083] The communication department can provide optimal support methods by referring to the past communication history of parents and teachers during communication support. For example, the communication department can analyze past communication history and provide support tailored to the communication styles of parents and teachers. For example, the communication department can provide information tailored to the interests of parents and teachers based on past communication history. For example, the communication department can refer to past communication history and adjust the frequency of communication between parents and teachers. In this way, by referring to past communication history, the communication department can provide optimal support methods. Some or all of the above processes in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input parent-teacher communication history data into a generating AI and have the generating AI determine the support method.

[0084] The communication department can provide support at the optimal time, taking into account the schedules of parents and teachers. For example, the communication department can adjust the schedules of parents and teachers to support communication at the optimal time. For example, the communication department can support communication while avoiding busy times for parents and teachers. For example, the communication department can provide communication reminders according to the schedules of parents and teachers. This makes it possible to provide communication support at the optimal time by taking into account the schedules of parents and teachers. Some or all of the above processes in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input parent and teacher schedule data into a generating AI and have the generating AI determine the optimal timing.

[0085] The communication unit can estimate the emotions of parents and teachers and adjust the frequency of communication based on the estimated emotions. For example, if a parent is stressed, the communication unit will reduce the frequency of communication. For example, if a teacher is relaxed, the communication unit will increase the frequency of communication. For example, if both parents and teachers are tense, the communication unit will adjust the frequency of communication. This allows for more effective communication by adjusting the frequency of communication according to the emotions of parents and teachers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input parent and teacher emotion data into the generative AI and have the generative AI adjust the frequency of communication.

[0086] The communication department can provide the most suitable communication method when supporting communication, taking into account the geographical distance between parents and teachers. For example, if parents and teachers are in distant locations, the communication department may recommend an online meeting. For example, if parents and teachers are in close proximity, the communication department may recommend face-to-face communication. The communication department provides the most suitable communication method according to the geographical distance between parents and teachers. This allows the communication department to provide the most suitable communication method by considering the geographical distance between parents and teachers. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input geographical distance data between parents and teachers into a generating AI and have the generating AI determine the communication method.

[0087] The communication department can adjust the content of communication when providing communication support, taking into account the cultural backgrounds of parents and teachers. For example, the communication department can provide an appropriate communication style according to the cultural backgrounds of parents and teachers. For example, the communication department can adjust the content of communication, taking into account the cultural backgrounds of parents and teachers. For example, the communication department can provide appropriate feedback based on the cultural backgrounds of parents and teachers. This makes more appropriate communication possible by taking into account the cultural backgrounds of parents and teachers. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input cultural background data of parents and teachers into a generating AI and have the generating AI perform the adjustment of the communication content.

[0088] The question-answering unit can estimate a child's emotions and adjust the timing of its responses based on those emotions. For example, if a child is stressed, the unit can delay its responses to provide time for relaxation. If a child is focused, the unit can respond immediately to questions without interrupting the learning process. If a child is tired, the unit can adjust its response time to encourage a break. By adjusting the timing of responses according to the child's emotions, the learning efficiency can be improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the question-answering unit may be performed using AI or not. For example, the question-answering unit can input child emotion data into the generative AI and have the generative AI adjust the timing of its responses.

[0089] The question-answering unit can provide the best answer by referring to the child's past question history when answering a question. For example, the question-answering unit can analyze past question history and provide the best answer to similar questions. For example, the question-answering unit can provide an answer that is appropriate to the child's level of understanding based on past question history. For example, the question-answering unit can refer to past question history and provide relevant additional information. In this way, the best answer can be provided by referring to past question history. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input past question history data into a generating AI and have the generating AI perform the generation of the best answer.

[0090] The question-answering unit can estimate a child's emotions and adjust the content of the questions and answers based on the estimated emotions. For example, if the child is stressed, the question-answering unit will provide a simple and easy-to-understand answer. For example, if the child is relaxed, the question-answering unit will provide an answer that includes detailed explanations. For example, if the child is focused, the question-answering unit will provide a concise answer. By adjusting the content of the questions and answers according to the child's emotions, the efficiency of learning can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the child's emotion data into the generative AI and have the generative AI perform the adjustment of the question-answer content.

[0091] The question-answering unit can adjust the difficulty level of questions based on the child's learning progress when answering questions. For example, the question-answering unit provides questions of appropriate difficulty level according to the child's learning progress. For example, the question-answering unit adjusts the difficulty level of questions based on the child's level of understanding. For example, the question-answering unit refers to the child's learning progress and provides questions that gradually increase in difficulty. This makes it possible to provide appropriate learning support by adjusting the difficulty level of questions based on learning progress. Some or all of the above processing in the question-answering unit may be performed using AI, for example, or without AI. For example, the question-answering unit can input the child's learning progress data into a generating AI and have the generating AI perform the adjustment of the difficulty level of the questions.

[0092] The analysis unit can estimate a child's emotions and adjust the method of analyzing learning progress based on the estimated emotions. For example, if the child is stressed, the analysis unit uses a simple analysis method. For example, if the child is relaxed, the analysis unit uses a detailed analysis method. For example, if the child is focused, the analysis unit uses a concise analysis method. By adjusting the analysis method according to the child's emotions, the accuracy of the learning progress analysis can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the child's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0093] The analysis unit can improve the accuracy of its analysis by referring to the child's past learning data when analyzing learning progress. For example, the analysis unit can analyze past learning data to understand the level of comprehension of a particular subject or topic. For example, the analysis unit can predict learning progress based on past learning data and improve the accuracy of its analysis. For example, the analysis unit can refer to past learning data and provide appropriate feedback according to learning progress. In this way, the accuracy of the analysis can be improved by referring to past learning data. 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 past learning data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0094] The analysis unit can estimate a child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if a child is stressed, the analysis unit provides a simple and visually user-friendly display method. For example, if a child is relaxed, the analysis unit provides a display method that includes detailed information. For example, if a child is focused, the analysis unit provides a display method that focuses on the key points. By adjusting the display method of the analysis results according to the child's emotions, the understanding of learning can be deepened. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 child's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0095] The analysis unit can take into account the child's learning environment when analyzing learning progress. For example, the analysis unit can consider the noise level of the learning environment when analyzing learning progress. For example, the analysis unit can consider the brightness of the lighting in the learning environment when analyzing learning progress. For example, the analysis unit can consider the temperature of the learning environment when analyzing learning progress. By taking the learning environment into account, a more accurate analysis of learning progress becomes possible. 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 learning environment data into a generating AI and have the generating AI perform the analysis of learning progress.

[0096] The feedback unit can estimate the emotions of parents and teachers and adjust the content of the feedback based on the estimated emotions. For example, if a parent is stressed, the feedback unit will provide simple, to-the-point feedback. For example, if a teacher is relaxed, the feedback unit will provide feedback with more detailed information. For example, if both parents and teachers are tense, the feedback unit will provide feedback in a calm tone. This allows for more effective feedback by adjusting the content of the feedback according to the emotions of parents and teachers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input parent and teacher emotion data into the generative AI and have the generative AI adjust the content of the feedback.

[0097] The feedback unit can determine the priority of feedback based on the child's learning progress when providing feedback. For example, the feedback unit may determine what should be prioritized in feedback based on the child's short-term learning progress. For example, the feedback unit may provide feedback in stages based on the child's medium-term learning progress. For example, the feedback unit may create an overall feedback plan based on the child's long-term learning progress. This enables efficient feedback by determining the priority of feedback based on learning progress. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may input the child's learning progress data into a generating AI and have the generating AI perform the determination of feedback priorities.

[0098] The feedback unit can estimate the emotions of parents and teachers and adjust the frequency of feedback based on the estimated emotions. For example, if a parent is stressed, the feedback unit will reduce the frequency of feedback. For example, if a teacher is relaxed, the feedback unit will increase the frequency of feedback. For example, if both parents and teachers are tense, the feedback unit will adjust the frequency of feedback. This allows for more effective feedback by adjusting the frequency of feedback according to the emotions of parents and teachers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input parent and teacher emotion data into a generative AI and have the generative AI adjust the feedback frequency.

[0099] The feedback unit can provide optimal feedback by referring to the communication history between parents and teachers when providing feedback. For example, the feedback unit can analyze past communication history and provide feedback tailored to the communication style of parents and teachers. For example, the feedback unit can provide feedback tailored to the concerns of parents and teachers based on past communication history. For example, the feedback unit can refer to past communication history and adjust the frequency of communication between parents and teachers. In this way, optimal feedback can be provided by referring to the communication history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input parent-teacher communication history data into a generating AI and have the generating AI perform the generation of optimal feedback.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The monitoring unit can adjust the monitoring method according to the child's learning style when monitoring the child's learning progress. For example, visually-oriented children can be monitored using graphs and charts. Auditory-oriented children can be monitored using audio feedback. Tactile-oriented children can be monitored through interactive activities. By monitoring according to the child's learning style, the efficiency of learning can be improved.

[0102] The question-answering section can adjust the content of questions based on the child's learning progress. For example, if a child has difficulty with a particular subject, the system can focus on providing questions related to that area. For subjects the child excels in, more advanced questions can be provided. Furthermore, the difficulty level of the questions can be gradually increased according to the child's learning progress. This allows for question-and-answer sessions tailored to the child's learning progress, thereby improving the efficiency of learning.

[0103] The analysis department can prioritize analysis based on the child's learning goals when analyzing a child's learning progress. For example, it can determine what should be prioritized for analysis based on the child's short-term learning goals. It can proceed with analysis in stages based on medium-term learning goals. It can create an overall learning plan based on long-term learning goals. By prioritizing analysis based on learning goals, efficient learning support becomes possible.

[0104] The feedback system can provide optimal feedback by referencing the communication history between parents and teachers. For example, it can analyze past communication history and provide feedback tailored to the communication styles of parents and teachers. It can also provide feedback aligned with the concerns of parents and teachers. Furthermore, it can adjust the frequency of communication between parents and teachers. This allows for the provision of optimal feedback by referencing the communication history.

[0105] The monitoring unit can estimate a child's emotions and adjust the frequency of monitoring learning progress based on those emotions. For example, if a child is stressed, the monitoring frequency can be reduced to allow for more relaxation time. If a child is focused, the monitoring frequency can be increased to gain a more detailed understanding of their learning progress. If a child is tired, the monitoring frequency can be adjusted to encourage breaks. By adjusting the monitoring frequency according to the child's emotions, learning efficiency can be improved.

[0106] The learning plan provider can estimate a child's emotions and adjust the content of the learning plan based on those estimates. For example, if a child is stressed, a learning plan with relaxing content can be provided. If a child is focused, a learning plan with more challenging content can be provided. If a child is tired, a learning plan that includes breaks can be provided. By adjusting the content of the learning plan according to the child's emotions, the efficiency of learning can be improved.

[0107] The communication department can estimate the emotions of parents and teachers and adjust the content of communication based on those estimates. For example, if a parent is stressed, it can provide simple, to-the-point communication. If a teacher is relaxed, it can provide communication that includes detailed information. If both parents and teachers are tense, it can provide communication in a calm tone. By adjusting the content of communication according to the emotions of parents and teachers, more effective communication becomes possible.

[0108] The question-answering unit can estimate a child's emotions and adjust the timing of question-answering based on those emotions. For example, if a child is stressed, the timing of question-answering can be delayed to provide time for relaxation. If a child is focused, questions can be answered immediately to avoid interrupting the learning flow. If a child is tired, the timing of question-answering can be adjusted to encourage a break. In this way, by adjusting the timing of question-answering according to the child's emotions, the efficiency of learning can be improved.

[0109] The analysis unit can estimate a child's emotions and adjust the analysis method of learning progress based on those estimated emotions. For example, if a child is stressed, a simple analysis method can be used. If a child is relaxed, a detailed analysis method can be used. If a child is focused, a concise analysis method can be used. By adjusting the analysis method according to the child's emotions, the accuracy of the learning progress analysis can be improved.

[0110] The monitoring unit can collect data while monitoring learning progress, taking into account the child's health condition. For example, it can record the child's sleep time and recommend getting enough sleep. It can record the child's meals and recommend a balanced diet. It can record the child's exercise level and recommend moderate exercise. By collecting data while considering the child's health condition, it becomes possible to monitor learning progress more accurately.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The monitoring unit monitors learning progress. For example, the monitoring unit collects data on how children learn and monitors their learning progress in real time. For example, the monitoring unit builds a question-answering system in which AI provides immediate answers if a child has questions while learning. For example, the monitoring unit analyzes the child's learning progress and optimizes individual learning progress. Step 2: The plan provision department provides personalized learning plans based on data collected by the monitoring department. For example, if a student has difficulty in a particular subject, the plan provision department will create a plan that focuses on that area. For example, the plan provision department will provide personalized learning plans tailored to each individual child. For example, the plan provision department will use machine learning to analyze and optimize individual learning progress based on the collected data. Step 3: The Communication Department supports communication between parents and teachers based on the learning plan provided by the Plan Provision Department. For example, the Communication Department uses AI to analyze the child's learning progress and provides feedback to parents and teachers. For example, the Communication Department makes it easier for parents and teachers to understand the child's learning progress and provide appropriate support. For example, the Communication Department has a feedback system that supports communication between parents and teachers.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication unit, question answering unit, analysis unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the child's learning progress in real time using the camera 42 and microphone 38B of the smart device 14 and collects data using the control unit 46A. The plan provision unit provides a personalized learning plan based on the data collected by the specific processing unit 290 of the data processing unit 12. The communication unit supports communication between parents and teachers using the control unit 46A of the smart device 14. The question answering unit builds a question answering system using the control unit 46A of the smart device 14 and answers the child's questions immediately. The analysis unit analyzes the child's learning progress using the specific processing unit 290 of the data processing unit 12. The feedback unit analyzes the child's learning situation using the specific processing unit 290 of the data processing unit 12 and provides feedback on the results to parents and teachers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication unit, question answering unit, analysis unit, and feedback unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the child's learning progress in real time using the camera 42 and microphone 238 of the smart glasses 214 and collects data by the control unit 46A. The plan provision unit provides a personalized learning plan based on the data collected by the specific processing unit 290 of the data processing unit 12. The communication unit supports communication between parents and teachers by the control unit 46A of the smart glasses 214. The question answering unit builds a question answering system by the control unit 46A of the smart glasses 214 and answers the child's questions immediately. The analysis unit analyzes the child's learning progress by the specific processing unit 290 of the data processing unit 12. The feedback unit analyzes the child's learning situation by the specific processing unit 290 of the data processing unit 12 and provides feedback on the results to parents and teachers. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication unit, question answering unit, analysis unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the child's learning progress in real time using the camera 42 and microphone 238 of the headset terminal 314 and collects data by the control unit 46A. The plan provision unit provides a personalized learning plan based on the data collected by, for example, the specific processing unit 290 of the data processing unit 12. The communication unit supports communication between parents and teachers by, for example, the control unit 46A of the headset terminal 314. The question answering unit constructs a question answering system by, for example, the control unit 46A of the headset terminal 314 and answers the child's questions immediately. The analysis unit analyzes the child's learning progress by, for example, the specific processing unit 290 of the data processing unit 12. The feedback unit analyzes the child's learning situation by, for example, the specific processing unit 290 of the data processing unit 12 and provides feedback on the results to parents and teachers. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication unit, question answering unit, analysis unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the child's learning progress in real time using the camera 42 and microphone 238 of the robot 414 and collects data by the control unit 46A. The plan provision unit provides a personalized learning plan based on the data collected by the specific processing unit 290 of the data processing unit 12. The communication unit supports communication between parents and teachers, for example, by the control unit 46A of the robot 414. The question answering unit constructs a question answering system by the control unit 46A of the robot 414 and answers the child's questions immediately. The analysis unit analyzes the child's learning progress, for example, by the specific processing unit 290 of the data processing unit 12. The feedback unit analyzes the child's learning situation by the specific processing unit 290 of the data processing unit 12 and provides feedback on the results to parents and teachers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The monitoring unit monitors learning progress, A plan provision unit provides a personalized learning plan based on the data collected by the monitoring unit, The system includes a communication unit that supports communication between parents and teachers based on the learning plan provided by the aforementioned plan provision unit. A system characterized by the following features. (Note 2) It includes a question answering unit that constructs a question answering system. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with an analysis unit to analyze learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a feedback unit that provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, The system estimates the child's emotions and adjusts the frequency of monitoring their learning progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, When monitoring learning progress, data should be collected while taking into account the child's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, When monitoring learning progress, referencing the child's past learning history improves the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, The system estimates the child's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, When monitoring learning progress, data should be collected while taking into account the child's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, When monitoring learning progress, analyze children's social media activity to assess its impact on their learning. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned plan provision department, The system estimates the child's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned plan provision department, When providing a learning plan, customize the plan according to the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan provision department, When providing a learning plan, prioritize the plan based on the child's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned plan provision department, The system estimates the child's emotions and adjusts the progress of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned plan provision department, When providing a learning plan, we customize it to take into account the child's home environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned plan provision department, When providing a learning plan, we adjust it to take into account the child's extracurricular activities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned communications department, The system estimates the emotions of parents and teachers and adjusts the content of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned communications department, When providing communication support, we refer to the past communication history between parents and teachers to provide the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned communications department, When providing communication support, we take into account the schedules of parents and teachers to provide support at the most optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned communications department, The system estimates the emotions of parents and teachers and adjusts the frequency of communication based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned communications department, When providing communication support, we consider the geographical distance between parents and teachers and provide the most appropriate communication method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned communications department, When providing communication support, adjust the content of the communication to take into account the cultural backgrounds of parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question answering unit is Estimate the child's emotions and adjust the timing of question-answering based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned question answering unit is When answering questions, refer to the child's past question history to provide the most appropriate answer. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned question answering unit is The system estimates the child's emotions and adjusts the content of the questions and answers based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned question answering unit is When answering questions, adjust the difficulty level of the questions based on the child's learning progress. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned analysis unit is We estimate children's emotions and adjust the analysis method of learning progress based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned analysis unit is When analyzing learning progress, referencing the child's past learning data improves the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned analysis unit is It estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned analysis unit is When analyzing learning progress, the child's learning environment should be taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned feedback unit is The system estimates the emotions of parents and teachers and adjusts the content of feedback based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on the child's learning progress. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned feedback unit is The system estimates the emotions of parents and teachers and adjusts the frequency of feedback based on these estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When providing feedback, refer to the communication history between parents and teachers to provide the most appropriate feedback. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring unit monitors learning progress, A plan provision unit provides a personalized learning plan based on the data collected by the monitoring unit, The system includes a communication unit that supports communication between parents and teachers based on the learning plan provided by the aforementioned plan provision unit. A system characterized by the following features.

2. It includes a question answering unit that constructs a question answering system. The system according to feature 1.

3. It is equipped with an analysis unit to analyze learning progress. The system according to feature 1.

4. Equipped with a feedback unit that provides feedback. The system according to feature 1.

5. The monitoring unit, The system estimates the child's emotions and adjusts the frequency of monitoring their learning progress based on those estimated emotions. The system according to feature 1.

6. The monitoring unit, When monitoring learning progress, data should be collected while taking into account the child's learning environment. The system according to feature 1.

7. The monitoring unit, When monitoring learning progress, referencing the child's past learning history improves the accuracy of monitoring. The system according to feature 1.

8. The monitoring unit, The system estimates the child's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system according to feature 1.

9. The monitoring unit, When monitoring learning progress, data should be collected while taking into account the child's health condition. The system according to feature 1.

10. The monitoring unit, When monitoring learning progress, analyze children's social media activity to assess its impact on their learning. The system according to feature 1.

11. The aforementioned plan provision department, The system estimates the child's emotions and adjusts the learning plan based on those estimated emotions. The system according to feature 1.

Images