system

Abstract

The system according to this embodiment aims to provide an optimal curriculum based on students' learning progress and to answer questions in real time. [Solution] The system according to this embodiment comprises a collection unit, a curriculum provision unit, a question answering unit, and a report provision unit. The collection unit collects the students' learning progress. The curriculum provision unit provides an optimal curriculum based on the learning progress collected by the collection unit. The question answering unit responds to questions during learning in real time based on the curriculum provided by the curriculum provision unit. The report provision unit provides a report that visualizes the learning outcomes obtained by the question answering unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0007] The system according to this embodiment can provide an optimal curriculum based on students' learning progress and respond to questions in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The educational support agent "EduMate" according to an embodiment of the present invention is a system that provides an optimal curriculum based on the student's learning progress and supports effective student learning. EduMate uses AI to provide an optimized curriculum based on each student's learning progress and level of understanding. For example, if a student is struggling to understand a particular subject or topic, the AI ​​automatically generates relevant additional materials and simple explanatory content. This allows students to learn effectively at their own pace. Next, if a student encounters difficulties during learning, the AI ​​responds to questions in real time. For example, if a student cannot understand a particular problem, the AI ​​provides hints and additional materials for that problem. This allows students to progress smoothly without getting stuck during their learning. It also provides reports that visualize learning outcomes, enabling parents and guardians to understand their child's learning situation. For example, the AI ​​automatically generates reports that visually display learning data, making it easy for guardians to quickly understand their child's weaknesses and strengths and what actions to take next. This enhances educational support at home and deepens parent-child communication. Furthermore, it provides interactive content that allows students to learn in a game-like manner. For example, children can deepen their knowledge while having fun through quizzes and practice problems. This improves motivation to learn and promotes continuous learning. Finally, it provides a community where parents can share their educational experiences and knowledge. For example, they can exchange information and advice about education through an information-sharing forum for parents. This broadens the scope of educational support at home and strengthens collaboration among parents. In this way, EduMate not only supports effective learning by providing an optimal curriculum based on individual learning progress, but also makes it easier for parents to understand their child's learning situation and enhance educational support at home. As a result, EduMate can support effective learning by providing an optimal curriculum based on the student's learning progress, responding to learning questions in real time, and providing reports that visualize learning outcomes.

[0029] The educational support agent "EduMate" according to this embodiment comprises a data collection unit, a curriculum provision unit, a question answering unit, and a report provision unit. The data collection unit collects students' learning progress. The data collection unit can collect data such as test scores, assignment submission status, and study time. The data collection unit can also automatically collect students' learning progress using AI. The curriculum provision unit provides an optimal curriculum based on the learning progress collected by the data collection unit. The curriculum provision unit can generate individually optimized curricula according to the student's level of understanding and learning objectives, for example. The curriculum provision unit can also automatically optimize the curriculum based on the student's learning progress using AI. The question answering unit responds to questions in real time based on the curriculum provided by the curriculum provision unit. The question answering unit can respond to questions in real time using AI if a student encounters difficulties during their studies, for example. The question answering unit can also provide necessary hints and additional materials using AI. The report provision unit provides a report that visualizes the learning outcomes obtained by the question answering unit. The report provision unit can, for example, automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. The report provision unit can also use AI to automatically generate reports that visualize learning outcomes. As a result, the educational support agent "EduMate" according to this embodiment can support effective learning by providing an optimal curriculum based on the student's learning progress, responding to learning questions in real time, and providing reports that visualize learning outcomes.

[0030] The data collection unit collects student learning progress. For example, it can collect data such as test scores, assignment submission status, and study time. Specifically, the unit integrates with online learning platforms and learning management systems (LMS) to automatically retrieve test scores and assignment status from students. Regarding study time, it records the time students spend using learning applications, allowing for an understanding of their concentration levels and duration. Furthermore, the unit can use AI to automatically collect student learning progress. For example, AI analyzes students' learning patterns and behaviors, evaluating their learning progress in real time. This allows the unit to gain a detailed understanding of students' learning situations and collect foundational data to provide optimal learning support to individual students. The collected data is stored on a cloud server, making it accessible to other departments. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The curriculum provision department provides an optimal curriculum based on the learning progress collected by the data collection department. For example, the curriculum provision department can generate individually optimized curricula according to the student's level of understanding and learning objectives. Specifically, the curriculum provision department can also use AI to automatically optimize the curriculum based on the student's learning progress. The AI ​​analyzes the student's past learning data and current progress to identify areas of weak understanding and skills that need strengthening. Based on this, the AI ​​selects the most suitable learning content and materials for each student and develops a learning plan. For example, a student with a low level of understanding in mathematics will be provided with a curriculum that starts with basic problems and gradually increases in difficulty, while a student with a high level of understanding will be provided with a curriculum that includes applied problems and advanced content. The curriculum provision department can also provide options according to the student's learning objectives and interests. For example, a student interested in a particular field can be provided with materials and assignments related to that field to increase their motivation to learn. In this way, the curriculum provision department can provide an optimal learning environment for each student and support effective learning.

[0032] The question-answering unit responds in real time to questions students have while learning, based on the curriculum provided by the curriculum provider. For example, if a student encounters difficulties while learning, the question-answering unit can use AI to answer their questions in real time. Specifically, the AI ​​uses natural language processing technology to understand the student's question and generate an appropriate answer. For example, if a student doesn't know how to solve a math problem, the AI ​​analyzes the problem and provides a step-by-step explanation. The AI ​​can also provide necessary hints and additional materials. For example, if a student wants to learn more about a specific event in history class, the AI ​​will present relevant materials and references to help deepen the student's understanding. Furthermore, the question-answering unit records the student's question history and can refer to past questions and answers to provide faster and more accurate responses. This allows the question-answering unit to resolve students' doubts while learning and improve learning efficiency.

[0033] The Reporting Department provides reports that visualize the learning outcomes obtained by the Question and Answering Department. For example, the Reporting Department can automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. Specifically, the Reporting Department can also use AI to automatically generate reports that visualize learning outcomes. The AI ​​analyzes the collected learning data and visually represents the student's learning progress and outcomes in graphs and charts. For example, it allows for a quick overview of trends in test scores, assignment submission status, and fluctuations in study time. The reports also include the student's strengths and weaknesses and future learning goals, making it easier for parents and teachers to understand the student's learning situation. Furthermore, the Reporting Department can regularly update the reports to reflect the latest learning outcomes. This allows the Reporting Department to visualize the student's learning situation and provide information that enables parents and teachers to provide appropriate support.

[0034] The curriculum provider can automatically generate relevant supplementary materials and brief explanatory content if a student is struggling to understand a particular subject or topic. For example, if a student is struggling to understand a particular subject or topic, the curriculum provider can use AI to automatically generate relevant supplementary materials and brief explanatory content. The curriculum provider can also use AI to generate optimal supplementary materials and explanatory content based on the student's level of understanding. This allows for a deeper understanding of a particular subject or topic by automatically generating relevant supplementary materials and brief explanatory content when a student is struggling.

[0035] The question-answering unit can respond to questions in real time if students encounter difficulties during their studies, and can provide necessary hints and additional materials. For example, if a student encounters difficulties during their studies, the question-answering unit can use AI to respond to their questions in real time. The question-answering unit can also use AI to provide necessary hints and additional materials. This allows students to progress through their studies smoothly by providing real-time answers to their questions and necessary hints and additional materials if they encounter difficulties during their studies.

[0036] The reporting department can automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. For example, the reporting department can use AI to automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. The reporting department can also use AI to automatically generate reports that visualize learning outcomes. This allows for the automatic generation of reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses, thereby enhancing educational support at home.

[0037] The Interactive Content Provision Department provides interactive content that allows students to learn in a game-like manner. For example, the Interactive Content Provision Department provides interactive content that allows students to deepen their knowledge while having fun through quizzes and practice problems. The Interactive Content Provision Department can also use AI to provide optimal interactive content based on students' learning progress. This allows students to deepen their knowledge while having fun by providing interactive content that makes learning feel like a game.

[0038] The Community Provisioning Department provides a community where parents can share their educational experiences and knowledge. For example, the Community Provisioning Department provides a community where parents can exchange information and advice about education through an information sharing forum. The Community Provisioning Department can also use AI to provide an optimal community where parents can share their educational experiences and knowledge. By providing a community where parents can share their educational experiences and knowledge, the scope of educational support at home will be broadened.

[0039] The data collection unit can analyze students' past learning history and select the optimal data collection method. For example, the unit can prioritize data collection methods that have been effective for students in the past. The unit can also select data collection methods that are effective for specific time periods based on students' learning history. The unit can analyze students' learning history and select the most efficient data collection method. This enables effective progress tracking by analyzing students' past learning history and selecting the optimal data collection method.

[0040] The data collection unit can filter learning progress based on the student's current learning environment and areas of interest. For example, if a student is studying in a quiet environment, the unit can filter out noise during data collection. The unit can also collect only relevant progress based on the student's areas of interest. The unit can also select the optimal collection method according to the student's learning environment. This allows for the collection of highly relevant progress by filtering based on the student's current learning environment and areas of interest.

[0041] The data collection unit can prioritize collecting highly relevant progress by considering students' geographical location information when collecting learning progress. For example, if a student is in a specific region, the data collection unit can prioritize collecting progress related to that region. The data collection unit can also select the optimal collection method based on students' geographical location information. The data collection unit can also collect highly relevant progress by considering students' geographical location information. This allows for the effective collection of region-related progress by prioritizing the collection of highly relevant progress while considering students' geographical location information.

[0042] The data collection unit can analyze students' social media activity and collect relevant progress when collecting learning progress. For example, the data collection unit can collect learning-related information from students' social media activity. The data collection unit can also analyze students' social media activity and select the optimal collection method. The data collection unit can also collect relevant progress based on students' social media activity. This allows for progress collection based on students' interests and concerns by analyzing students' social media activity and collecting relevant progress.

[0043] The curriculum provider can adjust the level of detail in the curriculum based on the importance of the learning material. For example, the curriculum provider can provide a detailed curriculum for important topics. It can also provide a concise curriculum for less important topics. The curriculum provider can adjust the level of detail in the curriculum according to the importance of the learning material. This allows for the provision of detailed curricula for important topics by adjusting the level of detail based on the importance of the learning material.

[0044] The curriculum provider can apply different curriculum algorithms depending on the learning category when providing the curriculum. For example, the curriculum provider can apply an algorithm that includes experimental videos to the science curriculum. The curriculum provider can also apply an algorithm that includes many practice problems to the mathematics curriculum. The curriculum provider can also apply an algorithm that explains the historical background in detail to the social studies curriculum. By applying different curriculum algorithms depending on the learning category, the curriculum provider can provide the most suitable curriculum for each category.

[0045] The curriculum delivery department can prioritize curriculum based on students' learning progress when providing the curriculum. For example, the curriculum delivery department can prioritize providing important curriculum according to students' progress. The curriculum delivery department can also analyze students' progress and provide the optimal curriculum. The curriculum delivery department can also prioritize curriculum based on students' progress. This allows the curriculum delivery department to provide the optimal curriculum according to students' progress by prioritizing curriculum based on learning progress.

[0046] The curriculum provider can adjust the order of the curriculum based on the relevance of learning when providing the curriculum. For example, the curriculum provider can provide important topics first based on the relevance of learning. The curriculum provider can also analyze the relevance of learning and provide the curriculum in the optimal order. The curriculum provider can also adjust the order of the curriculum based on the relevance of learning. This improves students' learning efficiency by adjusting the order of the curriculum based on the relevance of learning.

[0047] The question-answering unit can select the optimal response method by referring to past question history during question-answering. For example, the question-answering unit can select the optimal response method based on the content of questions previously asked by students. The question-answering unit can also provide relevant information from past question history. The question-answering unit can also analyze past question history and select the most efficient response method. As a result, efficient question-answering becomes possible by selecting the optimal response method by referring to past question history.

[0048] The question-answering unit can adjust the level of detail in its responses based on the student's current level of understanding. For example, it can provide more detailed responses depending on the student's level of understanding. The question-answering unit can also analyze the student's level of understanding and select the most appropriate response method. By adjusting the level of detail in responses based on the student's current level of understanding, it becomes possible to provide optimal responses that deepen the student's understanding.

[0049] The question-answering unit can select the most appropriate response method when answering questions, taking into account the student's geographical location. For example, if a student is in a specific region, the question-answering unit can provide information relevant to that region. The question-answering unit can also select the most appropriate response method based on the student's geographical location. The question-answering unit can also provide relevant information while considering the student's geographical location. This allows for the effective provision of region-related information by selecting the most appropriate response method while considering the student's geographical location.

[0050] The question-answering unit can analyze students' social media activity and provide relevant responses during question-answering. For example, the question-answering unit can provide learning-related information from students' social media activity. The question-answering unit can also analyze students' social media activity and select the most appropriate response method. The question-answering unit can also provide relevant information based on students' social media activity. This allows for optimal responses based on students' interests and concerns by analyzing students' social media activity and providing relevant responses.

[0051] The report delivery department can optimize the content of reports by referring to past learning data when providing them. For example, the report delivery department can provide the most optimal report based on past learning data. The report delivery department can also analyze students' past learning data and provide the most effective report. The report delivery department can also optimize the content of reports by referring to past learning data. This allows for the provision of effective reports by optimizing the report content by referring to past learning data.

[0052] The reporting department can adjust the level of detail in reports based on the parents' needs. For example, if a parent requests detailed information, the department can provide a detailed report. If a parent requests concise information, the department can provide a simpler report. The reporting department can also adjust the level of detail in reports based on the parents' needs. This allows for the provision of optimal reports that meet the parents' requirements.

[0053] The reporting department can provide optimal reports by considering the geographical location information of parents when providing reports. For example, if a parent is in a specific area, the reporting department can provide a report that includes information relevant to that area. The reporting department can also provide optimal reports based on the geographical location information of parents. The reporting department can also provide reports that include relevant information by considering the geographical location information of parents. In this way, by providing optimal reports that consider the geographical location information of parents, it is possible to effectively provide information relevant to the region.

[0054] The reporting department can analyze parents' social media activity and provide relevant reports when providing reports. For example, the reporting department can provide reports that include education-related information based on parents' social media activity. The reporting department can also analyze parents' social media activity and provide optimal reports. The reporting department can also provide reports that include relevant information based on parents' social media activity. This makes it possible to provide optimal reports based on parents' interests and concerns by analyzing parents' social media activity and providing relevant reports.

[0055] The interactive content delivery unit can provide optimal content by referring to students' past learning history when delivering interactive content. For example, the interactive content delivery unit can provide optimal interactive content based on students' past learning history. The interactive content delivery unit can also analyze students' past learning history and provide the most effective interactive content. The interactive content delivery unit can also optimize the content of interactive content by referring to students' past learning history. This enables effective learning by providing optimal content by referring to students' past learning history.

[0056] The interactive content delivery unit can adjust the difficulty level of the content based on the student's current learning status when providing interactive content. For example, the interactive content delivery unit can provide interactive content of a higher difficulty level depending on the student's current learning status. The interactive content delivery unit can also analyze the student's current learning status and provide interactive content of the optimal difficulty level. The interactive content delivery unit can also adjust the difficulty level of the interactive content based on the student's current learning status. This makes it possible to provide optimal content that matches the student's level of understanding by adjusting the difficulty level of the content based on the student's current learning status.

[0057] The interactive content delivery unit can provide optimal content by considering the student's geographical location when delivering interactive content. For example, if a student is in a specific region, the interactive content delivery unit can provide interactive content related to that region. The interactive content delivery unit can also provide optimal interactive content based on the student's geographical location. The interactive content delivery unit can also provide relevant interactive content by considering the student's geographical location. This allows for the effective provision of region-related information by providing optimal content while considering the student's geographical location.

[0058] The Interactive Content Provision Department can analyze students' social media activity and provide relevant content when providing interactive content. For example, it can provide learning-related interactive content based on students' social media activity. The Interactive Content Provision Department can also analyze students' social media activity and provide optimal interactive content. The Interactive Content Provision Department can also provide relevant interactive content based on students' social media activity. This makes it possible to provide optimal content based on students' interests and concerns by analyzing students' social media activity and providing relevant content.

[0059] The community service provider can provide optimal information by referring to parents' past activity history when providing community services. For example, the community service provider can provide optimal information based on parents' past activity history. The community service provider can also analyze parents' past activity history and provide the most effective information. The community service provider can also optimize community content by referring to parents' past activity history. This enables effective information provision by referring to parents' past activity history and providing optimal information.

[0060] The community service provider can adjust the level of detail of information based on the parents' current needs when providing information to the community. For example, the community service provider can provide detailed information according to the parents' current needs. The community service provider can also analyze the parents' current needs and provide the most relevant information. The community service provider can also adjust the level of detail of information based on the parents' current needs. This allows for the provision of optimal information that meets the parents' requirements by adjusting the level of detail of information based on the parents' current needs.

[0061] The community service provider can provide optimal information by considering the geographical location of parents when providing community services. For example, if a parent is in a specific area, the community service provider can provide information relevant to that area. The community service provider can also provide optimal information based on the parent's geographical location. The community service provider can also provide relevant information by considering the parent's geographical location. In this way, by providing optimal information while considering the parent's geographical location, it is possible to effectively provide information relevant to the area.

[0062] The community service department can analyze parents' social media activity and provide relevant information when providing community services. For example, the community service department can provide education-related information based on parents' social media activity. The community service department can also analyze parents' social media activity and provide optimal information. The community service department can also provide relevant information based on parents' social media activity. This makes it possible to provide optimal information based on parents' interests and concerns by analyzing parents' social media activity and providing relevant information.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] EduMate not only provides an optimal curriculum based on students' learning progress, but also allows for the selection of curriculum formats tailored to students' learning styles. For example, students who prefer visual learning can be offered a curriculum that makes extensive use of videos and infographics. Students who prefer auditory learning can be offered a curriculum with audio explanations and podcast formats. Furthermore, students who prefer tactile learning can be offered a curriculum that includes interactive simulations and experiments. This maximizes learning effectiveness by providing a curriculum that is optimal for each student's learning style.

[0065] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the curriculum delivery method to suit the student's learning environment. For example, if a student is studying at home, an online curriculum can be provided. If the student is studying at school, a curriculum suitable for classroom lessons can be provided. Furthermore, for learning on the go, a curriculum optimized for mobile devices can be provided. This enables the delivery of the most suitable curriculum for each student's learning environment.

[0066] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the curriculum content to match students' learning objectives. For example, students studying for a specific exam can be provided with a curriculum tailored to that exam. Students who want to acquire a specific skill can be provided with a curriculum related to that skill. Furthermore, students with long-term learning goals can be provided with a step-by-step curriculum aimed at achieving those goals. This makes it possible to provide an optimal curriculum tailored to each student's learning objectives.

[0067] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the pace of the curriculum to match each student's learning speed. For example, fast learners can be provided with a curriculum containing more advanced content, while slower learners can be provided with a curriculum that allows them to repeatedly review basic content. Furthermore, for students whose learning pace fluctuates, the pace can be adjusted to the optimal level each time. This makes it possible to provide an optimal curriculum tailored to each student's learning pace.

[0068] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the curriculum content to suit students' learning motivation. For example, highly motivated students can be offered a curriculum that includes challenging assignments and projects. Students with low motivation can be offered a curriculum that includes engaging topics and entertainment elements. Furthermore, the curriculum can be adjusted to suit students whose learning motivation fluctuates. This makes it possible to provide an optimal curriculum tailored to each student's learning motivation.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects student learning progress. The data collection unit can collect data such as test scores, assignment submission status, and study time. The data collection unit can also use AI to automatically collect student learning progress. Step 2: The curriculum provider provides the optimal curriculum based on the learning progress collected by the data collection unit. The curriculum provider can, for example, generate individually optimized curricula according to the student's level of understanding and learning objectives. The curriculum provider can also use AI to automatically optimize the curriculum based on the student's learning progress. Step 3: The question-answering unit responds in real time to questions during learning based on the curriculum provided by the curriculum provider. For example, if a student encounters difficulties during their studies, the question-answering unit can use AI to answer their questions in real time. The question-answering unit can also use AI to provide necessary hints and additional materials. Step 4: The report provision unit provides a report that visualizes the learning outcomes obtained by the question-answering unit. For example, the report provision unit can automatically generate a report that visually shows the learning data, making it easier for parents to understand their child's weaknesses and strengths. The report provision unit can also use AI to automatically generate a report that visualizes the learning outcomes.

[0071] (Example of form 2) The educational support agent "EduMate" according to an embodiment of the present invention is a system that provides an optimal curriculum based on the student's learning progress and supports effective student learning. EduMate uses AI to provide an optimized curriculum based on each student's learning progress and level of understanding. For example, if a student is struggling to understand a particular subject or topic, the AI ​​automatically generates relevant additional materials and simple explanatory content. This allows students to learn effectively at their own pace. Next, if a student encounters difficulties during learning, the AI ​​responds to questions in real time. For example, if a student cannot understand a particular problem, the AI ​​provides hints and additional materials for that problem. This allows students to progress smoothly without getting stuck during their learning. It also provides reports that visualize learning outcomes, enabling parents and guardians to understand their child's learning situation. For example, the AI ​​automatically generates reports that visually display learning data, making it easy for guardians to quickly understand their child's weaknesses and strengths and what actions to take next. This enhances educational support at home and deepens parent-child communication. Furthermore, it provides interactive content that allows students to learn in a game-like manner. For example, children can deepen their knowledge while having fun through quizzes and practice problems. This improves motivation to learn and promotes continuous learning. Finally, it provides a community where parents can share their educational experiences and knowledge. For example, they can exchange information and advice about education through an information-sharing forum for parents. This broadens the scope of educational support at home and strengthens collaboration among parents. In this way, EduMate not only supports effective learning by providing an optimal curriculum based on individual learning progress, but also makes it easier for parents to understand their child's learning situation and enhance educational support at home. As a result, EduMate can support effective learning by providing an optimal curriculum based on the student's learning progress, responding to learning questions in real time, and providing reports that visualize learning outcomes.

[0072] The educational support agent "EduMate" according to this embodiment comprises a data collection unit, a curriculum provision unit, a question answering unit, and a report provision unit. The data collection unit collects students' learning progress. The data collection unit can collect data such as test scores, assignment submission status, and study time. The data collection unit can also automatically collect students' learning progress using AI. The curriculum provision unit provides an optimal curriculum based on the learning progress collected by the data collection unit. The curriculum provision unit can generate individually optimized curricula according to the student's level of understanding and learning objectives, for example. The curriculum provision unit can also automatically optimize the curriculum based on the student's learning progress using AI. The question answering unit responds to questions in real time based on the curriculum provided by the curriculum provision unit. The question answering unit can respond to questions in real time using AI if a student encounters difficulties during their studies, for example. The question answering unit can also provide necessary hints and additional materials using AI. The report provision unit provides a report that visualizes the learning outcomes obtained by the question answering unit. The report provision unit can, for example, automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. The report provision unit can also use AI to automatically generate reports that visualize learning outcomes. As a result, the educational support agent "EduMate" according to this embodiment can support effective learning by providing an optimal curriculum based on the student's learning progress, responding to learning questions in real time, and providing reports that visualize learning outcomes.

[0073] The data collection unit collects student learning progress. For example, it can collect data such as test scores, assignment submission status, and study time. Specifically, the unit integrates with online learning platforms and learning management systems (LMS) to automatically retrieve test scores and assignment status from students. Regarding study time, it records the time students spend using learning applications, allowing for an understanding of their concentration levels and duration. Furthermore, the unit can use AI to automatically collect student learning progress. For example, AI analyzes students' learning patterns and behaviors, evaluating their learning progress in real time. This allows the unit to gain a detailed understanding of students' learning situations and collect foundational data to provide optimal learning support to individual students. The collected data is stored on a cloud server, making it accessible to other departments. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The curriculum provision department provides an optimal curriculum based on the learning progress collected by the data collection department. For example, the curriculum provision department can generate individually optimized curricula according to the student's level of understanding and learning objectives. Specifically, the curriculum provision department can also use AI to automatically optimize the curriculum based on the student's learning progress. The AI ​​analyzes the student's past learning data and current progress to identify areas of weak understanding and skills that need strengthening. Based on this, the AI ​​selects the most suitable learning content and materials for each student and develops a learning plan. For example, a student with a low level of understanding in mathematics will be provided with a curriculum that starts with basic problems and gradually increases in difficulty, while a student with a high level of understanding will be provided with a curriculum that includes applied problems and advanced content. The curriculum provision department can also provide options according to the student's learning objectives and interests. For example, a student interested in a particular field can be provided with materials and assignments related to that field to increase their motivation to learn. In this way, the curriculum provision department can provide an optimal learning environment for each student and support effective learning.

[0075] The question-answering unit responds in real time to questions students have while learning, based on the curriculum provided by the curriculum provider. For example, if a student encounters difficulties while learning, the question-answering unit can use AI to answer their questions in real time. Specifically, the AI ​​uses natural language processing technology to understand the student's question and generate an appropriate answer. For example, if a student doesn't know how to solve a math problem, the AI ​​analyzes the problem and provides a step-by-step explanation. The AI ​​can also provide necessary hints and additional materials. For example, if a student wants to learn more about a specific event in history class, the AI ​​will present relevant materials and references to help deepen the student's understanding. Furthermore, the question-answering unit records the student's question history and can refer to past questions and answers to provide faster and more accurate responses. This allows the question-answering unit to resolve students' doubts while learning and improve learning efficiency.

[0076] The Reporting Department provides reports that visualize the learning outcomes obtained by the Question and Answering Department. For example, the Reporting Department can automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. Specifically, the Reporting Department can also use AI to automatically generate reports that visualize learning outcomes. The AI ​​analyzes the collected learning data and visually represents the student's learning progress and outcomes in graphs and charts. For example, it allows for a quick overview of trends in test scores, assignment submission status, and fluctuations in study time. The reports also include the student's strengths and weaknesses and future learning goals, making it easier for parents and teachers to understand the student's learning situation. Furthermore, the Reporting Department can regularly update the reports to reflect the latest learning outcomes. This allows the Reporting Department to visualize the student's learning situation and provide information that enables parents and teachers to provide appropriate support.

[0077] The curriculum provider can automatically generate relevant supplementary materials and brief explanatory content if a student is struggling to understand a particular subject or topic. For example, if a student is struggling to understand a particular subject or topic, the curriculum provider can use AI to automatically generate relevant supplementary materials and brief explanatory content. The curriculum provider can also use AI to generate optimal supplementary materials and explanatory content based on the student's level of understanding. This allows for a deeper understanding of a particular subject or topic by automatically generating relevant supplementary materials and brief explanatory content when a student is struggling.

[0078] The question-answering unit can respond to questions in real time if students encounter difficulties during their studies, and can provide necessary hints and additional materials. For example, if a student encounters difficulties during their studies, the question-answering unit can use AI to respond to their questions in real time. The question-answering unit can also use AI to provide necessary hints and additional materials. This allows students to progress through their studies smoothly by providing real-time answers to their questions and necessary hints and additional materials if they encounter difficulties during their studies.

[0079] The reporting department can automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. For example, the reporting department can use AI to automatically generate reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. The reporting department can also use AI to automatically generate reports that visualize learning outcomes. This allows for the automatic generation of reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses, thereby enhancing educational support at home.

[0080] The Interactive Content Provision Department provides interactive content that allows students to learn in a game-like manner. For example, the Interactive Content Provision Department provides interactive content that allows students to deepen their knowledge while having fun through quizzes and practice problems. The Interactive Content Provision Department can also use AI to provide optimal interactive content based on students' learning progress. This allows students to deepen their knowledge while having fun by providing interactive content that makes learning feel like a game.

[0081] The Community Provisioning Department provides a community where parents can share their educational experiences and knowledge. For example, the Community Provisioning Department provides a community where parents can exchange information and advice about education through an information sharing forum. The Community Provisioning Department can also use AI to provide an optimal community where parents can share their educational experiences and knowledge. By providing a community where parents can share their educational experiences and knowledge, the scope of educational support at home will be broadened.

[0082] The data collection unit can estimate students' emotions and adjust the timing of learning progress collection based on the estimated emotions. For example, if a student is stressed, the collection unit can delay the collection timing to collect data when the student is relaxed. If a student is focused, the collection unit can advance the collection timing to efficiently collect progress. If a student is tired, the collection unit can adjust the collection timing to collect data after a break. In this way, by adjusting the timing of learning progress collection based on students' emotions, progress can be collected at the optimal time according to the student's state. 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.

[0083] The data collection unit can analyze students' past learning history and select the optimal data collection method. For example, the unit can prioritize data collection methods that have been effective for students in the past. The unit can also select data collection methods that are effective for specific time periods based on students' learning history. The unit can analyze students' learning history and select the most efficient data collection method. This enables effective progress tracking by analyzing students' past learning history and selecting the optimal data collection method.

[0084] The data collection unit can filter learning progress based on the student's current learning environment and areas of interest. For example, if a student is studying in a quiet environment, the unit can filter out noise during data collection. The unit can also collect only relevant progress based on the student's areas of interest. The unit can also select the optimal collection method according to the student's learning environment. This allows for the collection of highly relevant progress by filtering based on the student's current learning environment and areas of interest.

[0085] The data collection unit can estimate a student's emotions and determine the priority of learning progress to collect based on the estimated emotions. For example, if a student is excited, the data collection unit can prioritize collecting important progress. If a student is relaxed, the data collection unit can also prioritize collecting detailed progress. If a student is tired, the data collection unit can also prioritize collecting simple progress. This allows for optimal progress collection tailored to the student's state by prioritizing learning progress collection based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The data collection unit can prioritize collecting highly relevant progress by considering students' geographical location information when collecting learning progress. For example, if a student is in a specific region, the data collection unit can prioritize collecting progress related to that region. The data collection unit can also select the optimal collection method based on students' geographical location information. The data collection unit can also collect highly relevant progress by considering students' geographical location information. This allows for the effective collection of region-related progress by prioritizing the collection of highly relevant progress while considering students' geographical location information.

[0087] The data collection unit can analyze students' social media activity and collect relevant progress when collecting learning progress. For example, the data collection unit can collect learning-related information from students' social media activity. The data collection unit can also analyze students' social media activity and select the optimal collection method. The data collection unit can also collect relevant progress based on students' social media activity. This allows for progress collection based on students' interests and concerns by analyzing students' social media activity and collecting relevant progress.

[0088] The curriculum provider can estimate students' emotions and adjust the presentation of the curriculum based on those estimated emotions. For example, if a student is relaxed, the curriculum provider can provide a curriculum with detailed explanations. If a student is tense, the curriculum provider can provide a simple and highly visual curriculum. If a student is excited, the curriculum provider can provide a visually stimulating curriculum. By adjusting the presentation of the curriculum based on students' emotions, the curriculum provider can provide an optimal curriculum tailored to the student's state. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The curriculum provider can adjust the level of detail in the curriculum based on the importance of the learning material. For example, the curriculum provider can provide a detailed curriculum for important topics. It can also provide a concise curriculum for less important topics. The curriculum provider can adjust the level of detail in the curriculum according to the importance of the learning material. This allows for the provision of detailed curricula for important topics by adjusting the level of detail based on the importance of the learning material.

[0090] The curriculum provider can apply different curriculum algorithms depending on the learning category when providing the curriculum. For example, the curriculum provider can apply an algorithm that includes experimental videos to the science curriculum. The curriculum provider can also apply an algorithm that includes many practice problems to the mathematics curriculum. The curriculum provider can also apply an algorithm that explains the historical background in detail to the social studies curriculum. By applying different curriculum algorithms depending on the learning category, the curriculum provider can provide the most suitable curriculum for each category.

[0091] The curriculum provider can estimate students' emotions and adjust the length of the curriculum based on those emotions. For example, if a student is tired, the curriculum provider can provide a shorter curriculum. If a student is relaxed, the curriculum provider can provide a more detailed curriculum. If a student is excited, the curriculum provider can provide a visually stimulating curriculum. By adjusting the length of the curriculum based on students' emotions, the curriculum provider can provide an optimal curriculum tailored to the student's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The curriculum delivery department can prioritize curriculum based on students' learning progress when providing the curriculum. For example, the curriculum delivery department can prioritize providing important curriculum according to students' progress. The curriculum delivery department can also analyze students' progress and provide the optimal curriculum. The curriculum delivery department can also prioritize curriculum based on students' progress. This allows the curriculum delivery department to provide the optimal curriculum according to students' progress by prioritizing curriculum based on learning progress.

[0093] The curriculum provider can adjust the order of the curriculum based on the relevance of learning when providing the curriculum. For example, the curriculum provider can provide important topics first based on the relevance of learning. The curriculum provider can also analyze the relevance of learning and provide the curriculum in the optimal order. The curriculum provider can also adjust the order of the curriculum based on the relevance of learning. This improves students' learning efficiency by adjusting the order of the curriculum based on the relevance of learning.

[0094] The question-answering unit can estimate a student's emotions and adjust its question-answering method based on the estimated emotions. For example, if a student is nervous, the question-answering unit can provide a simple and visually clear response. If a student is relaxed, the question-answering unit can also provide a response that includes detailed information. If a student is excited, the question-answering unit can also provide a visually stimulating response. By adjusting the question-answering method based on the student's emotions, it becomes possible to provide an optimal response according to the student's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The question-answering unit can select the optimal response method by referring to past question history during question-answering. For example, the question-answering unit can select the optimal response method based on the content of questions previously asked by students. The question-answering unit can also provide relevant information from past question history. The question-answering unit can also analyze past question history and select the most efficient response method. As a result, efficient question-answering becomes possible by selecting the optimal response method by referring to past question history.

[0096] The question-answering unit can adjust the level of detail in its responses based on the student's current level of understanding. For example, it can provide more detailed responses depending on the student's level of understanding. The question-answering unit can also analyze the student's level of understanding and select the most appropriate response method. By adjusting the level of detail in responses based on the student's current level of understanding, it becomes possible to provide optimal responses that deepen the student's understanding.

[0097] The question-answering unit can estimate the student's emotions and determine the priority of question-answering based on the estimated emotions. For example, if the student is excited, the question-answering unit can prioritize answering important questions. If the student is relaxed, the question-answering unit can also prioritize answering detailed questions. If the student is tired, the question-answering unit can also prioritize answering simple questions. This allows for optimal question-answering tailored to the student's state by prioritizing question-answering based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The question-answering unit can select the most appropriate response method when answering questions, taking into account the student's geographical location. For example, if a student is in a specific region, the question-answering unit can provide information relevant to that region. The question-answering unit can also select the most appropriate response method based on the student's geographical location. The question-answering unit can also provide relevant information while considering the student's geographical location. This allows for the effective provision of region-related information by selecting the most appropriate response method while considering the student's geographical location.

[0099] The question-answering unit can analyze students' social media activity and provide relevant responses during question-answering. For example, the question-answering unit can provide learning-related information from students' social media activity. The question-answering unit can also analyze students' social media activity and select the most appropriate response method. The question-answering unit can also provide relevant information based on students' social media activity. This allows for optimal responses based on students' interests and concerns by analyzing students' social media activity and providing relevant responses.

[0100] The report delivery system can estimate a student's emotions and adjust how the report is displayed based on that estimation. For example, if a student is relaxed, the system can provide a detailed report. If a student is stressed, it can provide a simple, easy-to-read report. If a student is excited, it can provide a visually stimulating report. By adjusting the report display based on the student's emotions, it becomes possible to display the report optimally according to the student's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The report delivery department can optimize the content of reports by referring to past learning data when providing them. For example, the report delivery department can provide the most optimal report based on past learning data. The report delivery department can also analyze students' past learning data and provide the most effective report. The report delivery department can also optimize the content of reports by referring to past learning data. This allows for the provision of effective reports by optimizing the report content by referring to past learning data.

[0102] The reporting department can adjust the level of detail in reports based on the parents' needs. For example, if a parent requests detailed information, the department can provide a detailed report. If a parent requests concise information, the department can provide a simpler report. The reporting department can also adjust the level of detail in reports based on the parents' needs. This allows for the provision of optimal reports that meet the parents' requirements.

[0103] The report delivery system can estimate students' emotions and prioritize reports based on those emotions. For example, if a student is excited, the system can prioritize important reports. If a student is relaxed, the system can prioritize detailed reports. If a student is tired, the system can prioritize simple reports. This allows for optimal report delivery tailored to each student's state by prioritizing reports based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The reporting department can provide optimal reports by considering the geographical location information of parents when providing reports. For example, if a parent is in a specific area, the reporting department can provide a report that includes information relevant to that area. The reporting department can also provide optimal reports based on the geographical location information of parents. The reporting department can also provide reports that include relevant information by considering the geographical location information of parents. In this way, by providing optimal reports that consider the geographical location information of parents, it is possible to effectively provide information relevant to the region.

[0105] The reporting department can analyze parents' social media activity and provide relevant reports when providing reports. For example, the reporting department can provide reports that include education-related information based on parents' social media activity. The reporting department can also analyze parents' social media activity and provide optimal reports. The reporting department can also provide reports that include relevant information based on parents' social media activity. This makes it possible to provide optimal reports based on parents' interests and concerns by analyzing parents' social media activity and providing relevant reports.

[0106] The interactive content provider can estimate a student's emotions and adjust the content of the interactive content based on the estimated emotions. For example, if a student is relaxed, the interactive content provider can provide interactive content that includes detailed explanations. If a student is nervous, the interactive content provider can provide simple and highly visual interactive content. If a student is excited, the interactive content provider can provide visually stimulating interactive content. By adjusting the content of the interactive content based on the student's emotions, it becomes possible to provide optimal content tailored to the student's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The interactive content delivery unit can provide optimal content by referring to students' past learning history when delivering interactive content. For example, the interactive content delivery unit can provide optimal interactive content based on students' past learning history. The interactive content delivery unit can also analyze students' past learning history and provide the most effective interactive content. The interactive content delivery unit can also optimize the content of interactive content by referring to students' past learning history. This enables effective learning by providing optimal content by referring to students' past learning history.

[0108] The interactive content delivery unit can adjust the difficulty level of the content based on the student's current learning status when providing interactive content. For example, the interactive content delivery unit can provide interactive content of a higher difficulty level depending on the student's current learning status. The interactive content delivery unit can also analyze the student's current learning status and provide interactive content of the optimal difficulty level. The interactive content delivery unit can also adjust the difficulty level of the interactive content based on the student's current learning status. This makes it possible to provide optimal content that matches the student's level of understanding by adjusting the difficulty level of the content based on the student's current learning status.

[0109] The interactive content provider can estimate students' emotions and prioritize interactive content based on those emotions. For example, if a student is excited, the interactive content provider can prioritize important interactive content. If a student is relaxed, the interactive content provider can prioritize detailed interactive content. If a student is tired, the interactive content provider can prioritize simple interactive content. This allows for optimal content delivery tailored to the student's state by prioritizing interactive content based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The interactive content delivery unit can provide optimal content by considering the student's geographical location when delivering interactive content. For example, if a student is in a specific region, the interactive content delivery unit can provide interactive content related to that region. The interactive content delivery unit can also provide optimal interactive content based on the student's geographical location. The interactive content delivery unit can also provide relevant interactive content by considering the student's geographical location. This allows for the effective provision of region-related information by providing optimal content while considering the student's geographical location.

[0111] The Interactive Content Provision Department can analyze students' social media activity and provide relevant content when providing interactive content. For example, it can provide learning-related interactive content based on students' social media activity. The Interactive Content Provision Department can also analyze students' social media activity and provide optimal interactive content. The Interactive Content Provision Department can also provide relevant interactive content based on students' social media activity. This makes it possible to provide optimal content based on students' interests and concerns by analyzing students' social media activity and providing relevant content.

[0112] The community provider can estimate the parent's emotions and adjust how communities are displayed based on those estimated emotions. For example, if the parent is relaxed, the community provider can provide communities with detailed information. If the parent is stressed, the community provider can provide simple and highly visible communities. If the parent is agitated, the community provider can provide visually stimulating communities. By adjusting how communities are displayed based on the parent's emotions, it becomes possible to display communities optimally according to the parent's state. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The community service provider can provide optimal information by referring to parents' past activity history when providing community services. For example, the community service provider can provide optimal information based on parents' past activity history. The community service provider can also analyze parents' past activity history and provide the most effective information. The community service provider can also optimize community content by referring to parents' past activity history. This enables effective information provision by referring to parents' past activity history and providing optimal information.

[0114] The community service provider can adjust the level of detail of information based on the parents' current needs when providing information to the community. For example, the community service provider can provide detailed information according to the parents' current needs. The community service provider can also analyze the parents' current needs and provide the most relevant information. The community service provider can also adjust the level of detail of information based on the parents' current needs. This allows for the provision of optimal information that meets the parents' requirements by adjusting the level of detail of information based on the parents' current needs.

[0115] The community delivery unit can estimate the parent's emotions and prioritize community content based on those emotions. For example, if the parent is agitated, the community delivery unit can prioritize providing important information. If the parent is relaxed, the community delivery unit can prioritize providing detailed information. If the parent is tired, the community delivery unit can prioritize providing simple information. This allows for optimal information delivery tailored to the parent's state by prioritizing community content based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The community service provider can provide optimal information by considering the geographical location of parents when providing community services. For example, if a parent is in a specific area, the community service provider can provide information relevant to that area. The community service provider can also provide optimal information based on the parent's geographical location. The community service provider can also provide relevant information by considering the parent's geographical location. In this way, by providing optimal information while considering the parent's geographical location, it is possible to effectively provide information relevant to the area.

[0117] The community service department can analyze parents' social media activity and provide relevant information when providing community services. For example, the community service department can provide education-related information based on parents' social media activity. The community service department can also analyze parents' social media activity and provide optimal information. The community service department can also provide relevant information based on parents' social media activity. This makes it possible to provide optimal information based on parents' interests and concerns by analyzing parents' social media activity and providing relevant information.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] EduMate not only provides an optimal curriculum based on students' learning progress, but also allows for the selection of curriculum formats tailored to students' learning styles. For example, students who prefer visual learning can be offered a curriculum that makes extensive use of videos and infographics. Students who prefer auditory learning can be offered a curriculum with audio explanations and podcast formats. Furthermore, students who prefer tactile learning can be offered a curriculum that includes interactive simulations and experiments. This maximizes learning effectiveness by providing a curriculum that is optimal for each student's learning style.

[0120] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the curriculum delivery method to suit the student's learning environment. For example, if a student is studying at home, an online curriculum can be provided. If the student is studying at school, a curriculum suitable for classroom lessons can be provided. Furthermore, for learning on the go, a curriculum optimized for mobile devices can be provided. This enables the delivery of the most suitable curriculum for each student's learning environment.

[0121] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the curriculum content to match students' learning objectives. For example, students studying for a specific exam can be provided with a curriculum tailored to that exam. Students who want to acquire a specific skill can be provided with a curriculum related to that skill. Furthermore, students with long-term learning goals can be provided with a step-by-step curriculum aimed at achieving those goals. This makes it possible to provide an optimal curriculum tailored to each student's learning objectives.

[0122] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the pace of the curriculum to match each student's learning speed. For example, fast learners can be provided with a curriculum containing more advanced content, while slower learners can be provided with a curriculum that allows them to repeatedly review basic content. Furthermore, for students whose learning pace fluctuates, the pace can be adjusted to the optimal level each time. This makes it possible to provide an optimal curriculum tailored to each student's learning pace.

[0123] EduMate not only provides an optimal curriculum based on students' learning progress, but it can also adjust the curriculum content to suit students' learning motivation. For example, highly motivated students can be offered a curriculum that includes challenging assignments and projects. Students with low motivation can be offered a curriculum that includes engaging topics and entertainment elements. Furthermore, the curriculum can be adjusted to suit students whose learning motivation fluctuates. This makes it possible to provide an optimal curriculum tailored to each student's learning motivation.

[0124] EduMate can estimate students' emotions and adjust how learning progress is collected based on those emotions. For example, if a student is stressed, the collection method can be changed to allow for collection in a relaxed state. If a student is focused, the collection method can be changed to a more efficient way of collecting progress. If a student is tired, the collection method can be adjusted to allow for collection after a break. By adjusting the collection method of learning progress based on students' emotions, it becomes possible to collect data optimally according to the student's state.

[0125] EduMate can estimate students' emotions and adjust the curriculum content based on those estimates. For example, if a student is relaxed, it can provide a curriculum with detailed explanations. If a student is tense, it can provide a simple and highly visual curriculum. If a student is excited, it can provide a visually stimulating curriculum. By adjusting the curriculum content based on students' emotions, it becomes possible to provide an optimal curriculum tailored to each student's state.

[0126] EduMate can estimate a student's emotions and adjust its question-answering methods based on those estimates. For example, if a student is nervous, it can provide a simple and visually clear response. If a student is relaxed, it can provide a response that includes more detailed information. If a student is excited, it can provide a visually stimulating response. By adjusting the question-answering method based on the student's emotions, it enables the provision of optimal responses tailored to the student's state.

[0127] EduMate can estimate students' emotions and adjust how reports are displayed based on those estimates. For example, if a student is relaxed, it can provide a report with detailed information. If a student is stressed, it can provide a simple and easy-to-read report. If a student is excited, it can provide a visually stimulating report. By adjusting how reports are displayed based on students' emotions, it enables the display of reports that are optimal for each student's state.

[0128] EduMate can estimate students' emotions and adjust the content of interactive materials based on those emotions. For example, if a student is relaxed, it can provide interactive materials with detailed explanations. If a student is nervous, it can provide simple and highly visual interactive materials. If a student is excited, it can provide visually stimulating interactive materials. By adjusting the content of interactive materials based on students' emotions, it becomes possible to provide optimal content tailored to each student's state.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The data collection unit collects student learning progress. The data collection unit can collect data such as test scores, assignment submission status, and study time. The data collection unit can also use AI to automatically collect student learning progress. Step 2: The curriculum provider provides the optimal curriculum based on the learning progress collected by the data collection unit. The curriculum provider can, for example, generate individually optimized curricula according to the student's level of understanding and learning objectives. The curriculum provider can also use AI to automatically optimize the curriculum based on the student's learning progress. Step 3: The question-answering unit responds in real time to questions during learning based on the curriculum provided by the curriculum provider. For example, if a student encounters difficulties during their studies, the question-answering unit can use AI to answer their questions in real time. The question-answering unit can also use AI to provide necessary hints and additional materials. Step 4: The report provision unit provides a report that visualizes the learning outcomes obtained by the question-answering unit. For example, the report provision unit can automatically generate a report that visually shows the learning data, making it easier for parents to understand their child's weaknesses and strengths. The report provision unit can also use AI to automatically generate a report that visualizes the learning outcomes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, curriculum provision unit, question answering unit, report provision unit, interactive content provision unit, and community provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects students' learning progress using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The curriculum provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal curriculum based on the collected learning progress. The question answering unit is implemented, for example, by the control unit 46A of the smart device 14 and responds to students' questions in real time. The report provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a report that visualizes learning outcomes. The interactive content provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides content that allows students to learn while having fun. The community provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a community where parents can share information with each other. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, curriculum provision unit, question answering unit, report provision unit, interactive content provision unit, and community provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects students' learning progress using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The curriculum provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates an optimal curriculum based on the collected learning progress. The question answering unit is implemented, for example, by the control unit 46A of the smart glasses 214 and responds to students' questions in real time. The report provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a report that visualizes learning outcomes. The interactive content provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides content that allows students to learn while having fun. The community provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides a community where parents can share information with each other. 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.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, curriculum provision unit, question answering unit, report provision unit, interactive content provision unit, and community provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects students' learning progress using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The curriculum provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal curriculum based on the collected learning progress. The question answering unit is implemented by, for example, the control unit 46A of the headset terminal 314 and responds to students' questions in real time. The report provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a report that visualizes learning outcomes. The interactive content provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides content that allows students to learn while having fun. The community provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides a community where parents can share information with each other. 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.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the data collection unit, curriculum provision unit, question answering unit, report provision unit, interactive content provision unit, and community provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects students' learning progress using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The curriculum provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal curriculum based on the collected learning progress. The question answering unit is implemented by, for example, the control unit 46A of the robot 414 and responds to students' questions in real time. The report provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a report that visualizes learning outcomes. The interactive content provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides content that allows students to learn while having fun. The community provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a community where parents can share information with each other. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) The collection department collects information on students' learning progress, A curriculum provision unit provides an optimal curriculum based on the learning progress collected by the aforementioned collection unit, A question answering unit that responds in real time to questions asked during learning based on the curriculum provided by the aforementioned curriculum provisioning unit, A report providing unit that provides a report visualizing the learning outcomes obtained by the aforementioned question answering unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned curriculum provision department, If you are behind in understanding a particular subject or topic, the system will automatically generate relevant additional materials and brief explanatory content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned question answering unit is If you encounter any difficulties while learning, we will answer your questions in real time and provide any necessary hints or additional materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned report provision department, The system automatically generates reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features an interactive content provision department that offers interactive content that allows users to learn in a game-like manner. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a community service department that provides a community where parents can share their educational experiences and knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of learning progress data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting learning progress data, filter it based on the student's current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates students' emotions and prioritizes the learning progress to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting learning progress data, the system prioritizes collecting highly relevant progress data by considering students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting learning progress data, analyze students' social media activity and collect relevant progress. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned curriculum provision department, We estimate students' emotions and adjust the curriculum's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned curriculum provision department, When providing the curriculum, adjust the level of detail based on the importance of the learning material. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned curriculum provision department, When providing the curriculum, different curriculum algorithms are applied depending on the learning category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned curriculum provision department, Estimate students' emotions and adjust the curriculum length based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned curriculum provision department, When providing the curriculum, prioritize the curriculum based on the student's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned curriculum provision department, When providing the curriculum, adjust the order of the curriculum based on the relevance of the learning material. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question answering unit is The system estimates students' emotions and adjusts the question-answering method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question answering unit is When answering questions, the system will refer to past question history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question answering unit is When answering questions, adjust the level of detail in the response based on the student's current level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question answering unit is The system estimates students' emotions and prioritizes question-answering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question answering unit is When answering questions, the most appropriate response method will be selected considering the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question answering unit is During the Q&A session, we analyze students' social media activity to provide relevant responses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned report provision department, The system estimates students' emotions and adjusts how reports are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned report provision department, When providing reports, we optimize the report content by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned report provision department, When providing reports, we adjust the level of detail in the reports based on the parents' needs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned report provision department, Estimate students' emotions and prioritize reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned report provision department, When providing reports, we will consider the parents' geographical location to provide the most appropriate report. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned report provision department, When providing reports, we analyze parents' social media activity and provide relevant reports. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned interactive content provision unit is: The system estimates students' emotions and adjusts the content of interactive materials based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned interactive content provision unit is: When providing interactive content, we refer to students' past learning history to deliver the most suitable content. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned interactive content provision unit is: When providing interactive content, adjust the difficulty level of the content based on the student's current learning status. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned interactive content provision unit is: The system estimates students' emotions and prioritizes interactive content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned interactive content provision unit is: When providing interactive content, we will consider students' geographical location to deliver the most suitable content. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned interactive content provision unit is: When providing interactive content, analyze students' social media activity and provide relevant content. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned community provision unit is We estimate parental sentiment and adjust how communities are displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned community provision unit is When providing community support, we refer to parents' past activity history to provide the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned community provision unit is When providing community information, adjust the level of detail based on the parents' current needs. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned community provision unit is It estimates parental sentiment and determines community priorities based on the estimated parental sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned community provision unit is When providing community information, we take into account the parents' geographical location to provide the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned community provision unit is When providing community support, we analyze parents' social media activity and provide relevant information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0203] 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 collection department collects information on students' learning progress, A curriculum provision unit provides an optimal curriculum based on the learning progress collected by the aforementioned collection unit, A question answering unit that responds in real time to questions asked during learning based on the curriculum provided by the aforementioned curriculum provisioning unit, A report providing unit that provides a report visualizing the learning outcomes obtained by the aforementioned question answering unit, Equipped with A system characterized by the following features.

2. The aforementioned curriculum provision department, If you are behind in understanding a particular subject or topic, the system will automatically generate relevant additional materials and brief explanatory content. The system according to feature 1.

3. The aforementioned question answering unit is If you encounter any difficulties while learning, we will answer your questions in real time and provide any necessary hints or additional materials. The system according to feature 1.

4. The aforementioned report provision department, The system automatically generates reports that visually represent learning data, making it easier for parents to understand their child's strengths and weaknesses. The system according to feature 1.

5. It features an interactive content provision department that offers interactive content that allows users to learn in a game-like manner. The system according to feature 1.

6. It includes a community service department that provides a community where parents can share their educational experiences and knowledge. The system according to feature 1.

7. The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of learning progress data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting learning progress data, filter it based on the student's current learning environment and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is The system estimates students' emotions and prioritizes the learning progress to collect based on those estimated emotions. The system according to feature 1.

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