system

Abstract

The system according to this embodiment aims to ensure that students receive high-quality learning support regardless of their home environment. [Solution] The system according to the embodiment comprises a supplementation unit, a response unit, and a planning unit. The supplementation unit supplements the learning content. The response unit responds to questions based on the learning content supplemented by the supplementation unit. The planning unit creates a learning plan based on the information obtained by the response unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to receive high-quality learning support without depending on the home environment, and there is a risk of learning gaps.

[0005] The system according to the embodiment aims to enable students to receive high-quality learning support regardless of the home environment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a complementing unit, a response unit, and a planning unit. The complementing unit complements learning content. The response unit responds to questions based on the learning content complemented by the complementing unit. The planning unit creates a learning plan based on the information obtained by the response unit.

Effects of the Invention

[0007] The system according to this embodiment can enable students to receive high-quality learning support regardless of their home environment. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent "HomeTutor" according to an embodiment of the present invention is a system that enables students to receive high-quality learning support regardless of their home environment. This system corrects disparities in home education and provides equitable learning opportunities. For example, even in households where parental support is difficult, it can create an environment where students can learn effectively at home. HomeTutor has functions such as supplementing learning content, answering questions, creating learning plans, optimizing the learning environment, and providing feedback to parents. For example, HomeTutor can review and reinforce school learning content to deepen understanding. The AI ​​analyzes the student's learning history and generates learning materials according to their level of understanding. Next, HomeTutor provides 24-hour support for any questions the student may have. The AI ​​uses natural language processing to provide accurate answers to the student's questions. Furthermore, HomeTutor provides a home learning plan tailored to the student's situation. The AI ​​analyzes the student's learning progress and proposes an optimal learning plan. HomeTutor also suggests environmental settings and break times to enhance concentration. The AI ​​analyzes the student's learning patterns and proposes an optimal learning environment. Finally, HomeTutor reports learning progress to parents, facilitating support at home. The AI ​​analyzes students' learning data and reports their progress to parents. In this way, HomeTutor utilizes AI to support students' learning from multiple angles, providing equitable learning opportunities regardless of home environment. As a result, HomeTutor can provide an environment where students can receive high-quality learning support.

[0029] The HomeTutor system according to this embodiment comprises a supplementation unit, a response unit, and a planning unit. The supplementation unit supplements the learning content. For example, the supplementation unit can review and reinforce the learning content from school and deepen understanding. The supplementation unit provides additional teaching materials and supplementary explanations. For example, the supplementation unit analyzes the student's learning history and generates teaching materials according to their level of understanding. The supplementation unit can use AI to analyze the student's learning history and generate teaching materials according to their level of understanding. The response unit responds to questions based on the learning content supplemented by the supplementation unit. For example, the response unit provides 24-hour support for questions the student has. The response unit provides text-based answers and voice responses. For example, the response unit uses natural language processing to provide accurate answers to the student's questions. The response unit can use AI to perform natural language processing on the student's questions and provide accurate answers. The planning unit creates a learning plan based on the information obtained by the response unit. For example, the planning unit provides a home learning plan tailored to the student's situation. The planning department creates plans based on learning objectives and time allocation. For example, the planning department analyzes students' learning progress and proposes an optimal learning plan. The planning department can use AI to analyze students' learning progress and propose an optimal learning plan. As a result, the HomeTutor system according to this embodiment can supplement learning content, answer questions, and create learning plans.

[0030] The supplementary learning section complements the learning content. For example, it can review and reinforce what students have learned in school, deepening their understanding. Specifically, the supplementary learning section provides materials to help students reconfirm what they have learned in school and focus on reinforcing areas where their understanding is insufficient. For example, it can provide additional materials that explain in detail how to use formulas learned in math class or the background of events learned in history class. The supplementary learning section can use AI to analyze students' learning history and generate materials tailored to their level of understanding. The AI ​​analyzes the content of problems students have solved in the past and reports they have submitted to identify areas where they are struggling. For example, if a student frequently makes mistakes in using a particular formula in math problems, it will provide additional practice problems or explanatory videos related to that formula. The AI ​​can also adjust the difficulty level of the materials according to the student's learning pace and level of understanding. For example, it will provide more application problems to students with high levels of understanding and more basic problems to students with low levels of understanding. In this way, the supplementary learning section can provide optimal materials tailored to each student's learning situation, maximizing learning effectiveness. Furthermore, the supplementary learning section regularly checks students' understanding and updates the materials as needed. For example, based on the results of regular tests, supplementary materials are provided to reinforce areas where understanding is insufficient. This allows the supplementary department to continuously support students' learning and ensure retention of the learned material.

[0031] The response unit answers questions based on the learning content supplemented by the supplementary unit. The response unit provides 24 / 7 support for students' questions, for example. Specifically, the response unit provides real-time answers to questions and doubts that students have while learning. The response unit provides text-based answers and voice responses. For example, if a student is solving a math problem and doesn't know how to use a formula, they can send a question to the response unit, and the AI ​​will analyze the question and provide a text or voice answer explaining how to use the formula correctly. The response unit can use AI to perform natural language processing on students' questions and provide accurate answers. The AI ​​analyzes the content of the student's question, searches for relevant information in the database, and generates an answer. For example, if a student asks about the background of an event learned in history class, the AI ​​will provide detailed information about that event. The response unit also records the student's question history and can provide more accurate answers based on past questions. For example, if the same student repeatedly asks the same question, the response unit can identify the part that the student is having difficulty understanding and provide more detailed explanations or additional materials. This allows the response unit to support students' learning and resolve their questions, thereby facilitating smooth progress in their studies. Furthermore, the response unit supports multiple languages, accommodating students who speak different languages. This enables the response unit to function effectively even in a global learning environment.

[0032] The planning unit creates learning plans based on information obtained by the response unit. For example, the planning unit provides home study plans tailored to the student's situation. Specifically, the planning unit analyzes the student's learning progress and level of understanding and proposes an optimal learning plan. For example, it provides specific suggestions on how to allocate daily study time, which subjects to focus on, and which materials to use. The planning unit creates plans based on learning objectives and plans based on time allocation. For example, when creating a study plan for final exams, it considers the remaining time until the exam and distributes study time for each subject in a balanced manner. Also, if the student's understanding of a particular subject or unit is low, it proposes a learning plan that focuses on that area. The planning unit can use AI to analyze the student's learning progress and propose an optimal learning plan. The AI ​​analyzes the student's past learning data and test results to identify where they are struggling. For example, if the student's understanding of a particular unit in mathematics is insufficient, it proposes a learning plan that focuses on that unit. Furthermore, the AI ​​can flexibly adjust the learning plan according to the student's learning pace and level of understanding. For example, the planning department might propose a plan with many application problems for students with a high level of understanding, and a plan with many basic problems for students with a lower level of understanding. This allows the planning department to provide each student with an optimal learning plan and maximize learning effectiveness. Furthermore, the planning department regularly reviews the learning plans and makes revisions as needed. For example, they re-evaluate the learning plans based on the results of periodic tests and propose plans to reinforce areas where understanding is insufficient. In this way, the planning department can continuously support students' learning and help them achieve their learning goals.

[0033] The HomeTutor system includes an optimization unit that optimizes the learning environment. This unit optimizes the learning environment, for example, by suggesting environmental settings and break times to enhance concentration. It also adjusts lighting and removes noise. For instance, the optimization unit analyzes a student's learning patterns and proposes an optimal learning environment. Using AI, the optimization unit can analyze student learning patterns and propose an optimal learning environment, thereby enabling the optimization of the learning environment.

[0034] The HomeTutor system includes a reporting unit that reports learning progress to parents. The reporting unit reports learning progress to parents. For example, it reports learning progress to parents to facilitate support at home. The reporting unit provides reports via email and app notifications. For example, the reporting unit analyzes the student's learning data and reports the learning progress to parents. The reporting unit can use AI to analyze the student's learning data and report the learning progress to parents. This makes it possible to report learning progress.

[0035] The supplementary unit can analyze students' learning history and generate learning materials tailored to their level of understanding. For example, the supplementary unit analyzes learning history using data mining techniques and statistical analysis. The supplementary unit also performs evaluations based on test results and generates learning materials using AI. For instance, the supplementary unit analyzes students' learning history and generates learning materials tailored to their level of understanding. The supplementary unit can use AI to analyze students' learning history and generate learning materials tailored to their level of understanding. This makes it possible to generate learning materials tailored to the student's level of understanding.

[0036] The response unit can provide accurate answers to students' questions using natural language processing. The response unit employs natural language processing techniques such as morphological analysis and grammatical analysis. The response unit provides accurate answers based on accuracy and relevance. For example, the response unit provides accurate answers to students' questions using natural language processing. The response unit can use AI to perform natural language processing on students' questions and provide accurate answers. This enables the provision of accurate answers using natural language processing.

[0037] The planning department can analyze students' learning progress and propose optimal learning plans. For example, the planning department records study time and analyzes test results. Based on learning goals and learning styles, the planning department proposes optimal learning plans. For instance, the planning department analyzes students' learning progress and proposes optimal learning plans. The planning department can use AI to analyze students' learning progress and propose optimal learning plans. This enables the proposal of optimal learning plans.

[0038] The supplementary component can analyze a student's past learning history and determine the optimal order in which learning materials are presented. For example, the supplementary component analyzes learning history using data mining techniques and statistical analysis. It determines the optimal order of materials based on learning objectives and progress. For instance, it can provide materials that prioritize reviewing areas where the student has previously struggled. It can also provide materials that allow students to focus on areas where they struggle, prioritizing areas where they excel. Based on the student's learning history, the supplementary component can also provide materials that repeatedly cover topics where the student has a low level of understanding. This enables the determination of the optimal order in which learning materials are presented.

[0039] The supplementary component can add relevant topics based on students' interests when supplementing learning content. The supplementary component identifies students' interests using methods such as surveys and behavioral history analysis. It adds relevant topics based on the relevance and degree of interest of the learning content. For example, it provides materials related to topics students are interested in. It can also provide additional learning materials related to areas students have shown interest in. The supplementary component can also provide materials containing relevant topics based on students' interests. This enables the addition of relevant topics based on students' interests.

[0040] The supplementary learning component can provide region-specific teaching materials by considering students' geographical location when supplementing learning content. For example, the supplementary component can utilize GPS data or provide region-specific teaching materials. The supplementary component can also provide region-specific teaching materials based on local history and culture. For example, it can provide history and geography materials related to the area where students live. It can also provide materials on culture and traditions related to the student's region. Furthermore, it can provide science and technology materials related to the student's region. This enables the provision of region-specific teaching materials.

[0041] The supplementary learning component can analyze students' social media activity and provide relevant materials when supplementing learning content. For example, it can analyze posts and followers. It provides relevant materials based on the degree of interest and relevance. For instance, it provides materials related to topics students have shown interest in on social media. It can also provide materials related to areas of interest based on students' social media activity. The supplementary learning component can analyze students' social media activity and provide materials that include relevant topics. This enables the provision of materials based on social media activity.

[0042] The response unit can provide the most appropriate answer by referring to the student's past question history when answering questions. For example, the response unit can use a database or search through history. The response unit provides the best answer based on accuracy and relevance. For example, it provides relevant answers based on questions the student has asked in the past. The response unit can also provide answers tailored to the student's level of understanding based on their past question history. The response unit can also refer to questions the student has asked in the past and provide answers that include additional explanations. This makes it possible to provide the most appropriate answer based on past question history.

[0043] The response unit can provide additional explanations and examples during question-answering, depending on the student's level of understanding. The response unit assesses understanding using, for example, test results or learning progress. Based on the level of understanding and the question, the response unit provides additional explanations and examples. For example, if the student's understanding is low, the response unit provides an answer with a detailed explanation. If the student's understanding is high, the response unit can also provide a concise answer. Depending on the student's understanding, the response unit can also provide answers with specific examples. This enables the provision of additional explanations and examples tailored to the student's level of understanding.

[0044] The response unit can prioritize answers based on when students submit their questions. For example, the response unit can record submission dates and set deadlines. The response unit prioritizes answers based on submission timing and the importance of the question. For instance, it prioritizes answers based on when students submit their questions. The response unit can also provide quick answers if students are in a hurry. The response unit can also adjust the priority of answers depending on when students submit their questions. This allows for prioritizing answers based on submission timing.

[0045] The response unit can improve the accuracy of its answers by referring to the student's relevant learning history when answering questions. For example, the response unit can use databases and search through history. The response unit improves the accuracy of its answers based on accuracy and relevance. For example, it can refer to the student's learning history and provide answers that include relevant information. The response unit can also provide answers tailored to the student's level of understanding based on their learning history. Furthermore, it can analyze the student's learning history and provide the most appropriate answer. This enables improved accuracy of answers based on relevant learning history.

[0046] The planning department can propose the optimal learning plan by referring to the student's past learning progress when creating a learning plan. The planning department can, for example, utilize databases and search history. The planning department proposes the optimal plan based on learning objectives and learning styles. For example, the planning department proposes the optimal learning plan based on the student's past learning progress. The planning department can also propose a learning plan tailored to the student's level of understanding based on their learning history. The planning department can also analyze the student's past learning progress and provide the optimal learning plan. This makes it possible to propose the optimal plan based on past learning progress.

[0047] The planning department can provide customized learning plans tailored to students' goals and preferences when creating them. The planning department identifies goals and preferences using methods such as questionnaires and interviews. The planning department provides customized plans based on individual learning objectives and learning styles. For example, the planning department provides a customized learning plan according to a student's goals. The planning department can also propose the optimal learning plan based on the student's preferences. The planning department can also provide customized learning plans that take into account the student's goals and preferences. This makes it possible to provide customized plans that meet individual goals and preferences.

[0048] The planning department can provide optimal learning plans by taking into account students' daily routines and schedules when creating learning plans. For example, the planning department identifies daily routines and schedules using daily activity logs and schedule management apps. The planning department provides optimal learning plans based on learning goals and learning styles. For example, the planning department provides optimal learning plans tailored to students' daily routines. The planning department can also propose learning plans considering students' schedules. The planning department can also provide optimal learning plans based on students' daily routines and schedules. This makes it possible to provide optimal learning plans that are tailored to students' daily routines and schedules.

[0049] The planning department can improve the accuracy of learning plans by referring to students' relevant learning history when creating them. For example, the planning department can use databases and search through history. The planning department improves the accuracy of plans based on learning objectives and learning styles. For example, the planning department can refer to students' learning history to provide optimal learning plans. The planning department can also propose learning plans tailored to students' understanding levels based on their past learning history. The planning department can also analyze students' learning history to improve the accuracy of plans. This enables improved plan accuracy based on relevant learning history.

[0050] The optimization unit can propose the optimal learning environment by referring to the student's past learning patterns when optimizing the learning environment. For example, the optimization unit can utilize databases and search history. The optimization unit proposes the optimal environment based on learning goals and learning style. For example, the optimization unit proposes the optimal learning environment based on the student's past learning patterns. The optimization unit can also provide an environment that enhances concentration based on the student's learning history. The optimization unit can also analyze the student's past learning patterns and provide the optimal learning environment. This makes it possible to propose the optimal learning environment based on past learning patterns.

[0051] The optimization unit can provide an optimal learning environment by considering the student's device information when optimizing the learning environment. For example, the optimization unit can provide optimal display settings and a learning environment based on the device's screen size and performance. The optimization unit can also provide an optimal environment based on learning goals and learning style. For example, the optimization unit can provide optimal display settings to match the screen size of the device the student is using. The optimization unit can also suggest an optimal learning environment according to the performance of the device the student is using. The optimization unit can also provide an optimal learning environment based on the student's device information. This makes it possible to provide an optimal learning environment based on device information.

[0052] The reporting department can select the most appropriate reporting method when reporting to parents by referring to the student's past learning data. For example, the reporting department can utilize databases and search history. The reporting department selects the most appropriate reporting method based on learning objectives and learning styles. For example, the reporting department can provide detailed reports to parents based on the student's past learning data. The reporting department can also provide reports that are easy for parents to understand based on the student's learning history. The reporting department can also refer to the student's past learning data and select the most appropriate reporting method for parents. This makes it possible to select the most appropriate reporting method based on past learning data.

[0053] The reporting department can provide the most suitable reporting method to parents, taking into account the student's daily rhythm and schedule. For example, the reporting department can identify the student's daily rhythm and schedule using daily activity logs or schedule management apps. The reporting department provides the most suitable reporting method based on learning goals and learning styles. For example, the reporting department can provide the most suitable reporting method to parents, tailored to the student's daily rhythm. The reporting department can also report to parents, taking the student's schedule into consideration. The reporting department can also provide the most suitable reporting method based on the student's daily rhythm and schedule. This makes it possible to provide the most suitable reporting method according to the student's daily rhythm and schedule.

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

[0055] The HomeTutor system can also be equipped with a motivation enhancement section. This section provides functions to increase students' motivation to learn. For example, the motivation enhancement section can provide rewards based on students' learning progress. Rewards may be provided in the form of badges, points, or perks. The motivation enhancement section can also display messages celebrating students' goal achievements. Furthermore, the motivation enhancement section can customize learning content based on students' interests and preferences to maintain their motivation. This can increase students' motivation to learn and promote continuous learning.

[0056] The HomeTutor system can also include a collaborative learning section. This section provides features to promote collaboration among students. For example, it can provide a platform for students to work together online. Students can collaborate on projects and participate in group discussions. The collaborative learning section can also include features for students to provide feedback to each other. Furthermore, it can provide chat and video call functions to facilitate communication among students. This promotes collaboration among students and enhances learning effectiveness.

[0057] The HomeTutor system can also include a health management section. This section monitors students' health and provides appropriate advice. For example, it can analyze students' sleep patterns and suggest appropriate sleep durations. It can also record students' diets and recommend balanced meals. Furthermore, it can monitor students' exercise habits and suggest appropriate exercise levels. This helps maintain students' health and enhances their learning effectiveness.

[0058] The HomeTutor system can also include a Career Support Department. This department provides functions to support students' future career choices. For example, it can suggest appropriate occupations based on students' interests and aptitudes. It can also provide learning plans to help students acquire the skills and qualifications necessary for their desired occupations. Furthermore, it can offer opportunities for work experience and internships. This helps support students' future career choices and promotes learning toward achieving their goals.

[0059] The HomeTutor system can also include a Cultural Exchange Department. This department provides functions to offer students opportunities to experience different cultures. For example, it can offer online international exchange programs, allowing students to interact with students from other countries and learn about different cultures. The Cultural Exchange Department can also provide educational materials and content related to different cultures. Furthermore, it can organize events and workshops on intercultural topics. This increases students' opportunities to experience different cultures and broadens their international perspectives.

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

[0061] Step 1: The supplementary section complements the learning content. For example, it can review and reinforce what was learned at school, deepening understanding. The supplementary section provides additional materials and supplementary explanations. The supplementary section analyzes the student's learning history and generates materials tailored to their level of understanding. Using AI, it can analyze the student's learning history and generate materials tailored to their level of understanding. Step 2: The response unit responds to questions based on the learning content supplemented by the supplementary unit. For example, it provides 24-hour support for students' questions. The response unit provides text-based answers, voice responses, etc. The response unit uses natural language processing to provide accurate answers to students' questions. AI can be used to perform natural language processing on students' questions and provide accurate answers. Step 3: The planning unit creates a learning plan based on the information obtained by the response unit. For example, it provides a home study plan tailored to the student's situation. The planning unit creates plans based on learning objectives and plans based on time allocation. The planning unit analyzes the student's learning progress and proposes the optimal learning plan. Using AI, it is possible to analyze the student's learning progress and propose the optimal learning plan.

[0062] (Example of form 2) The AI ​​agent "HomeTutor" according to an embodiment of the present invention is a system that enables students to receive high-quality learning support regardless of their home environment. This system corrects disparities in home education and provides equitable learning opportunities. For example, even in households where parental support is difficult, it can create an environment where students can learn effectively at home. HomeTutor has functions such as supplementing learning content, answering questions, creating learning plans, optimizing the learning environment, and providing feedback to parents. For example, HomeTutor can review and reinforce school learning content to deepen understanding. The AI ​​analyzes the student's learning history and generates learning materials according to their level of understanding. Next, HomeTutor provides 24-hour support for any questions the student may have. The AI ​​uses natural language processing to provide accurate answers to the student's questions. Furthermore, HomeTutor provides a home learning plan tailored to the student's situation. The AI ​​analyzes the student's learning progress and proposes an optimal learning plan. HomeTutor also suggests environmental settings and break times to enhance concentration. The AI ​​analyzes the student's learning patterns and proposes an optimal learning environment. Finally, HomeTutor reports learning progress to parents, facilitating support at home. The AI ​​analyzes students' learning data and reports their progress to parents. In this way, HomeTutor utilizes AI to support students' learning from multiple angles, providing equitable learning opportunities regardless of home environment. As a result, HomeTutor can provide an environment where students can receive high-quality learning support.

[0063] The HomeTutor system according to this embodiment comprises a supplementation unit, a response unit, and a planning unit. The supplementation unit supplements the learning content. For example, the supplementation unit can review and reinforce the learning content from school and deepen understanding. The supplementation unit provides additional teaching materials and supplementary explanations. For example, the supplementation unit analyzes the student's learning history and generates teaching materials according to their level of understanding. The supplementation unit can use AI to analyze the student's learning history and generate teaching materials according to their level of understanding. The response unit responds to questions based on the learning content supplemented by the supplementation unit. For example, the response unit provides 24-hour support for questions the student has. The response unit provides text-based answers and voice responses. For example, the response unit uses natural language processing to provide accurate answers to the student's questions. The response unit can use AI to perform natural language processing on the student's questions and provide accurate answers. The planning unit creates a learning plan based on the information obtained by the response unit. For example, the planning unit provides a home learning plan tailored to the student's situation. The planning department creates plans based on learning objectives and time allocation. For example, the planning department analyzes students' learning progress and proposes an optimal learning plan. The planning department can use AI to analyze students' learning progress and propose an optimal learning plan. As a result, the HomeTutor system according to this embodiment can supplement learning content, answer questions, and create learning plans.

[0064] The supplementary learning section complements the learning content. For example, it can review and reinforce what students have learned in school, deepening their understanding. Specifically, the supplementary learning section provides materials to help students reconfirm what they have learned in school and focus on reinforcing areas where their understanding is insufficient. For example, it can provide additional materials that explain in detail how to use formulas learned in math class or the background of events learned in history class. The supplementary learning section can use AI to analyze students' learning history and generate materials tailored to their level of understanding. The AI ​​analyzes the content of problems students have solved in the past and reports they have submitted to identify areas where they are struggling. For example, if a student frequently makes mistakes in using a particular formula in math problems, it will provide additional practice problems or explanatory videos related to that formula. The AI ​​can also adjust the difficulty level of the materials according to the student's learning pace and level of understanding. For example, it will provide more application problems to students with high levels of understanding and more basic problems to students with low levels of understanding. In this way, the supplementary learning section can provide optimal materials tailored to each student's learning situation, maximizing learning effectiveness. Furthermore, the supplementary learning section regularly checks students' understanding and updates the materials as needed. For example, based on the results of regular tests, supplementary materials are provided to reinforce areas where understanding is insufficient. This allows the supplementary department to continuously support students' learning and ensure retention of the learned material.

[0065] The response unit answers questions based on the learning content supplemented by the supplementary unit. The response unit provides 24 / 7 support for students' questions, for example. Specifically, the response unit provides real-time answers to questions and doubts that students have while learning. The response unit provides text-based answers and voice responses. For example, if a student is solving a math problem and doesn't know how to use a formula, they can send a question to the response unit, and the AI ​​will analyze the question and provide a text or voice answer explaining how to use the formula correctly. The response unit can use AI to perform natural language processing on students' questions and provide accurate answers. The AI ​​analyzes the content of the student's question, searches for relevant information in the database, and generates an answer. For example, if a student asks about the background of an event learned in history class, the AI ​​will provide detailed information about that event. The response unit also records the student's question history and can provide more accurate answers based on past questions. For example, if the same student repeatedly asks the same question, the response unit can identify the part that the student is having difficulty understanding and provide more detailed explanations or additional materials. This allows the response unit to support students' learning and resolve their questions, thereby facilitating smooth progress in their studies. Furthermore, the response unit supports multiple languages, accommodating students who speak different languages. This enables the response unit to function effectively even in a global learning environment.

[0066] The planning unit creates learning plans based on information obtained by the response unit. For example, the planning unit provides home study plans tailored to the student's situation. Specifically, the planning unit analyzes the student's learning progress and level of understanding and proposes an optimal learning plan. For example, it provides specific suggestions on how to allocate daily study time, which subjects to focus on, and which materials to use. The planning unit creates plans based on learning objectives and plans based on time allocation. For example, when creating a study plan for final exams, it considers the remaining time until the exam and distributes study time for each subject in a balanced manner. Also, if the student's understanding of a particular subject or unit is low, it proposes a learning plan that focuses on that area. The planning unit can use AI to analyze the student's learning progress and propose an optimal learning plan. The AI ​​analyzes the student's past learning data and test results to identify where they are struggling. For example, if the student's understanding of a particular unit in mathematics is insufficient, it proposes a learning plan that focuses on that unit. Furthermore, the AI ​​can flexibly adjust the learning plan according to the student's learning pace and level of understanding. For example, the planning department might propose a plan with many application problems for students with a high level of understanding, and a plan with many basic problems for students with a lower level of understanding. This allows the planning department to provide each student with an optimal learning plan and maximize learning effectiveness. Furthermore, the planning department regularly reviews the learning plans and makes revisions as needed. For example, they re-evaluate the learning plans based on the results of periodic tests and propose plans to reinforce areas where understanding is insufficient. In this way, the planning department can continuously support students' learning and help them achieve their learning goals.

[0067] The HomeTutor system includes an optimization unit that optimizes the learning environment. This unit optimizes the learning environment, for example, by suggesting environmental settings and break times to enhance concentration. It also adjusts lighting and removes noise. For instance, the optimization unit analyzes a student's learning patterns and proposes an optimal learning environment. Using AI, the optimization unit can analyze student learning patterns and propose an optimal learning environment, thereby enabling the optimization of the learning environment.

[0068] The HomeTutor system includes a reporting unit that reports learning progress to parents. The reporting unit reports learning progress to parents. For example, it reports learning progress to parents to facilitate support at home. The reporting unit provides reports via email and app notifications. For example, the reporting unit analyzes the student's learning data and reports the learning progress to parents. The reporting unit can use AI to analyze the student's learning data and report the learning progress to parents. This makes it possible to report learning progress.

[0069] The supplementary unit can analyze students' learning history and generate learning materials tailored to their level of understanding. For example, the supplementary unit analyzes learning history using data mining techniques and statistical analysis. The supplementary unit also performs evaluations based on test results and generates learning materials using AI. For instance, the supplementary unit analyzes students' learning history and generates learning materials tailored to their level of understanding. The supplementary unit can use AI to analyze students' learning history and generate learning materials tailored to their level of understanding. This makes it possible to generate learning materials tailored to the student's level of understanding.

[0070] The response unit can provide accurate answers to students' questions using natural language processing. The response unit employs natural language processing techniques such as morphological analysis and grammatical analysis. The response unit provides accurate answers based on accuracy and relevance. For example, the response unit provides accurate answers to students' questions using natural language processing. The response unit can use AI to perform natural language processing on students' questions and provide accurate answers. This enables the provision of accurate answers using natural language processing.

[0071] The planning department can analyze students' learning progress and propose optimal learning plans. For example, the planning department records study time and analyzes test results. Based on learning goals and learning styles, the planning department proposes optimal learning plans. For instance, the planning department analyzes students' learning progress and proposes optimal learning plans. The planning department can use AI to analyze students' learning progress and propose optimal learning plans. This enables the proposal of optimal learning plans.

[0072] The supplementary unit can estimate students' emotions and adjust the difficulty level of the learning materials based on those estimated emotions. The supplementary unit estimates students' emotions using technologies such as facial recognition and speech analysis. It also adjusts the difficulty level of the materials based on comprehension and learning progress. For example, if a student is stressed, the supplementary unit provides materials with many easy problems. If a student is relaxed, it may also provide materials with more difficult problems. If a student is excited, it may also provide materials with interesting topics. This allows for adjustment of the difficulty level of the materials according to the student's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The supplementary component can analyze a student's past learning history and determine the optimal order in which learning materials are presented. For example, the supplementary component analyzes learning history using data mining techniques and statistical analysis. It determines the optimal order of materials based on learning objectives and progress. For instance, it can provide materials that prioritize reviewing areas where the student has previously struggled. It can also provide materials that allow students to focus on areas where they struggle, prioritizing areas where they excel. Based on the student's learning history, the supplementary component can also provide materials that repeatedly cover topics where the student has a low level of understanding. This enables the determination of the optimal order in which learning materials are presented.

[0074] The supplementary component can add relevant topics based on students' interests when supplementing learning content. The supplementary component identifies students' interests using methods such as surveys and behavioral history analysis. It adds relevant topics based on the relevance and degree of interest of the learning content. For example, it provides materials related to topics students are interested in. It can also provide additional learning materials related to areas students have shown interest in. The supplementary component can also provide materials containing relevant topics based on students' interests. This enables the addition of relevant topics based on students' interests.

[0075] The complementary unit can estimate students' emotions and select the format of learning materials based on those estimated emotions. The complementary unit estimates students' emotions using technologies such as facial recognition and speech analysis. The complementary unit selects the format of learning materials based on learning style and comprehension level. For example, if a student prefers visual learning, the complementary unit provides video materials. If a student prefers auditory learning, the complementary unit can also provide audio materials. If a student prefers text-based learning, the complementary unit can also provide text materials. This enables the selection of learning material formats tailored to students' emotions. 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.

[0076] The supplementary learning component can provide region-specific teaching materials by considering students' geographical location when supplementing learning content. For example, the supplementary component can utilize GPS data or provide region-specific teaching materials. The supplementary component can also provide region-specific teaching materials based on local history and culture. For example, it can provide history and geography materials related to the area where students live. It can also provide materials on culture and traditions related to the student's region. Furthermore, it can provide science and technology materials related to the student's region. This enables the provision of region-specific teaching materials.

[0077] The supplementary learning component can analyze students' social media activity and provide relevant materials when supplementing learning content. For example, it can analyze posts and followers. It provides relevant materials based on the degree of interest and relevance. For instance, it provides materials related to topics students have shown interest in on social media. It can also provide materials related to areas of interest based on students' social media activity. The supplementary learning component can analyze students' social media activity and provide materials that include relevant topics. This enables the provision of materials based on social media activity.

[0078] The response unit can estimate the student's emotions and adjust the way it expresses its response based on those emotions. The response unit estimates the student's emotions using technologies such as facial recognition and speech analysis. It adjusts the way it expresses its response based on the degree of emotion and understanding. For example, if the student is nervous, the response unit will respond using gentle language. If the student is relaxed, the response unit may also provide a response that includes detailed explanations. If the student is in a hurry, the response unit may also provide a concise and quick response. This allows for adjustment of the response's expression according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The response unit can provide the most appropriate answer by referring to the student's past question history when answering questions. For example, the response unit can use a database or search through history. The response unit provides the best answer based on accuracy and relevance. For example, it provides relevant answers based on questions the student has asked in the past. The response unit can also provide answers tailored to the student's level of understanding based on their past question history. The response unit can also refer to questions the student has asked in the past and provide answers that include additional explanations. This makes it possible to provide the most appropriate answer based on past question history.

[0080] The response unit can provide additional explanations and examples during question-answering, depending on the student's level of understanding. The response unit assesses understanding using, for example, test results or learning progress. Based on the level of understanding and the question, the response unit provides additional explanations and examples. For example, if the student's understanding is low, the response unit provides an answer with a detailed explanation. If the student's understanding is high, the response unit can also provide a concise answer. Depending on the student's understanding, the response unit can also provide answers with specific examples. This enables the provision of additional explanations and examples tailored to the student's level of understanding.

[0081] The response unit can estimate the student's emotions and adjust the level of detail in its response based on the estimated emotions. The response unit estimates the student's emotions using technologies such as facial recognition and speech analysis. It adjusts the level of detail in its response based on the degree of emotion and understanding. For example, if the student is nervous, the response unit provides a concise and easy-to-understand response. If the student is relaxed, the response unit may also provide a response with detailed explanations. If the student is in a hurry, the response unit may also provide a quick and concise response. This allows for adjustment of the level of detail in the response according to the student's 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.

[0082] The response unit can prioritize answers based on when students submit their questions. For example, the response unit can record submission dates and set deadlines. The response unit prioritizes answers based on submission timing and the importance of the question. For instance, it prioritizes answers based on when students submit their questions. The response unit can also provide quick answers if students are in a hurry. The response unit can also adjust the priority of answers depending on when students submit their questions. This allows for prioritizing answers based on submission timing.

[0083] The response unit can improve the accuracy of its answers by referring to the student's relevant learning history when answering questions. For example, the response unit can use databases and search through history. The response unit improves the accuracy of its answers based on accuracy and relevance. For example, it can refer to the student's learning history and provide answers that include relevant information. The response unit can also provide answers tailored to the student's level of understanding based on their learning history. Furthermore, it can analyze the student's learning history and provide the most appropriate answer. This enables improved accuracy of answers based on relevant learning history.

[0084] The planning unit can estimate students' emotions and adjust the pace of the learning plan based on the estimated emotions. The planning unit estimates students' emotions using technologies such as facial recognition and voice analysis. The planning unit adjusts the pace of the learning plan based on the degree of emotion and learning progress. For example, if a student is stressed, the planning unit will slow down the pace. If a student is relaxed, the planning unit may also speed up the pace. If a student is excited, the planning unit may also adjust the pace. This makes it possible to adjust the pace of the learning plan according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The planning department can propose the optimal learning plan by referring to the student's past learning progress when creating a learning plan. The planning department can, for example, utilize databases and search history. The planning department proposes the optimal plan based on learning objectives and learning styles. For example, the planning department proposes the optimal learning plan based on the student's past learning progress. The planning department can also propose a learning plan tailored to the student's level of understanding based on their learning history. The planning department can also analyze the student's past learning progress and provide the optimal learning plan. This makes it possible to propose the optimal plan based on past learning progress.

[0086] The planning department can provide customized learning plans tailored to students' goals and preferences when creating them. The planning department identifies goals and preferences using methods such as questionnaires and interviews. The planning department provides customized plans based on individual learning objectives and learning styles. For example, the planning department provides a customized learning plan according to a student's goals. The planning department can also propose the optimal learning plan based on the student's preferences. The planning department can also provide customized learning plans that take into account the student's goals and preferences. This makes it possible to provide customized plans that meet individual goals and preferences.

[0087] The planning unit can estimate students' emotions and determine the priority of learning plans based on those estimated emotions. The planning unit estimates students' emotions using technologies such as facial recognition and voice analysis. The planning unit determines the priority of learning plans based on the degree of emotion and learning objectives. For example, if a student is stressed, the planning unit adjusts the priority. If a student is relaxed, the planning unit may also change the priority. If a student is excited, the planning unit may also determine the priority. This makes it possible to determine the priority of learning plans in accordance with students' 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.

[0088] The planning department can provide optimal learning plans by taking into account students' daily routines and schedules when creating learning plans. For example, the planning department identifies daily routines and schedules using daily activity logs and schedule management apps. The planning department provides optimal learning plans based on learning goals and learning styles. For example, the planning department provides optimal learning plans tailored to students' daily routines. The planning department can also propose learning plans considering students' schedules. The planning department can also provide optimal learning plans based on students' daily routines and schedules. This makes it possible to provide optimal learning plans that are tailored to students' daily routines and schedules.

[0089] The planning department can improve the accuracy of learning plans by referring to students' relevant learning history when creating them. For example, the planning department can use databases and search through history. The planning department improves the accuracy of plans based on learning objectives and learning styles. For example, the planning department can refer to students' learning history to provide optimal learning plans. The planning department can also propose learning plans tailored to students' understanding levels based on their past learning history. The planning department can also analyze students' learning history to improve the accuracy of plans. This enables improved plan accuracy based on relevant learning history.

[0090] The optimization unit can estimate students' emotions and adjust the learning environment settings based on the estimated emotions. The optimization unit estimates students' emotions using technologies such as facial recognition and voice analysis. The optimization unit adjusts the learning environment settings based on the degree of emotion and learning progress. For example, if a student is tense, the optimization unit suggests a relaxing environment. If a student is relaxed, the optimization unit can also provide an environment that enhances concentration. If a student is excited, the optimization unit can also suggest a calm environment. This makes it possible to adjust the learning environment settings according to the student's 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.

[0091] The optimization unit can propose the optimal learning environment by referring to the student's past learning patterns when optimizing the learning environment. For example, the optimization unit can utilize databases and search history. The optimization unit proposes the optimal environment based on learning goals and learning style. For example, the optimization unit proposes the optimal learning environment based on the student's past learning patterns. The optimization unit can also provide an environment that enhances concentration based on the student's learning history. The optimization unit can also analyze the student's past learning patterns and provide the optimal learning environment. This makes it possible to propose the optimal learning environment based on past learning patterns.

[0092] The optimization unit can estimate students' emotions and adjust break times based on the estimated emotions. The optimization unit estimates students' emotions using technologies such as facial recognition and voice analysis. The optimization unit adjusts break times based on the degree of emotion and learning progress. For example, if a student is tired, the optimization unit will suggest a break. The optimization unit can also delay breaks if a student is concentrating. The optimization unit can also suggest the optimal break time according to the student's emotions. This makes it possible to adjust break times according to the student's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The optimization unit can provide an optimal learning environment by considering the student's device information when optimizing the learning environment. For example, the optimization unit can provide optimal display settings and a learning environment based on the device's screen size and performance. The optimization unit can also provide an optimal environment based on learning goals and learning style. For example, the optimization unit can provide optimal display settings to match the screen size of the device the student is using. The optimization unit can also suggest an optimal learning environment according to the performance of the device the student is using. The optimization unit can also provide an optimal learning environment based on the student's device information. This makes it possible to provide an optimal learning environment based on device information.

[0094] The reporting system can estimate a student's emotions and adjust the content of reports to parents based on the estimated emotions. The reporting system estimates students' emotions using technologies such as facial recognition and voice analysis. It adjusts the report content based on the degree of emotion and learning progress. For example, if a student is stressed, the reporting system will provide a report encouraging support to parents. If a student is relaxed, the reporting system can also provide a detailed report on learning progress. If a student is excited, the reporting system can provide a positive report to parents. This allows for adjustment of report content according to the student's 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.

[0095] The reporting department can select the most appropriate reporting method when reporting to parents by referring to the student's past learning data. For example, the reporting department can utilize databases and search history. The reporting department selects the most appropriate reporting method based on learning objectives and learning styles. For example, the reporting department can provide detailed reports to parents based on the student's past learning data. The reporting department can also provide reports that are easy for parents to understand based on the student's learning history. The reporting department can also refer to the student's past learning data and select the most appropriate reporting method for parents. This makes it possible to select the most appropriate reporting method based on past learning data.

[0096] The reporting unit can estimate students' emotions and adjust the frequency of reports based on the estimated emotions. The reporting unit estimates students' emotions using technologies such as facial recognition and voice analysis. The reporting unit adjusts the frequency of reports based on the degree of emotion and learning progress. For example, the reporting unit increases the frequency of reports when a student is stressed. The reporting unit can also decrease the frequency of reports when a student is relaxed. The reporting unit can also adjust the frequency of reports according to the student's emotions. This makes it possible to adjust the frequency of reports according to the student's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The reporting department can provide the most suitable reporting method to parents, taking into account the student's daily rhythm and schedule. For example, the reporting department can identify the student's daily rhythm and schedule using daily activity logs or schedule management apps. The reporting department provides the most suitable reporting method based on learning goals and learning styles. For example, the reporting department can provide the most suitable reporting method to parents, tailored to the student's daily rhythm. The reporting department can also report to parents, taking the student's schedule into consideration. The reporting department can also provide the most suitable reporting method based on the student's daily rhythm and schedule. This makes it possible to provide the most suitable reporting method according to the student's daily rhythm and schedule.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The HomeTutor system can also be equipped with a motivation enhancement section. This section provides functions to increase students' motivation to learn. For example, the motivation enhancement section can provide rewards based on students' learning progress. Rewards may be provided in the form of badges, points, or perks. The motivation enhancement section can also display messages celebrating students' goal achievements. Furthermore, the motivation enhancement section can customize learning content based on students' interests and preferences to maintain their motivation. This can increase students' motivation to learn and promote continuous learning.

[0100] The HomeTutor system can also include a collaborative learning section. This section provides features to promote collaboration among students. For example, it can provide a platform for students to work together online. Students can collaborate on projects and participate in group discussions. The collaborative learning section can also include features for students to provide feedback to each other. Furthermore, it can provide chat and video call functions to facilitate communication among students. This promotes collaboration among students and enhances learning effectiveness.

[0101] The HomeTutor system can also include a health management section. This section monitors students' health and provides appropriate advice. For example, it can analyze students' sleep patterns and suggest appropriate sleep durations. It can also record students' diets and recommend balanced meals. Furthermore, it can monitor students' exercise habits and suggest appropriate exercise levels. This helps maintain students' health and enhances their learning effectiveness.

[0102] The HomeTutor system can also include a Career Support Department. This department provides functions to support students' future career choices. For example, it can suggest appropriate occupations based on students' interests and aptitudes. It can also provide learning plans to help students acquire the skills and qualifications necessary for their desired occupations. Furthermore, it can offer opportunities for work experience and internships. This helps support students' future career choices and promotes learning toward achieving their goals.

[0103] The HomeTutor system can also include a Cultural Exchange Department. This department provides functions to offer students opportunities to experience different cultures. For example, it can offer online international exchange programs, allowing students to interact with students from other countries and learn about different cultures. The Cultural Exchange Department can also provide educational materials and content related to different cultures. Furthermore, it can organize events and workshops on intercultural topics. This increases students' opportunities to experience different cultures and broadens their international perspectives.

[0104] The HomeTutor system can further monitor students' stress levels using emotion estimation capabilities and suggest appropriate countermeasures. For example, it can analyze students' facial expressions and voice using emotion estimation to estimate their stress levels. If the stress level is high, it can suggest breathing exercises or stretches to help them relax. If the stress level is low, it can provide advice to improve concentration. Furthermore, it can adjust learning plans according to stress levels to promote stress-free learning. This reduces student stress and improves learning effectiveness.

[0105] The HomeTutor system can further analyze student motivation using emotion estimation capabilities and provide appropriate feedback. For example, it can analyze a student's facial expressions and voice using emotion estimation to estimate their motivation level. If motivation is low, it can provide encouraging messages and advice for achieving goals. If motivation is high, it can provide challenges to encourage further effort. Furthermore, it can adjust learning content according to the student's motivation level to maintain their enthusiasm for learning. This can increase student motivation and promote continuous learning.

[0106] The HomeTutor system can further analyze students' learning styles using emotion estimation capabilities and suggest optimal learning methods. For example, it can analyze students' facial expressions and voices using emotion estimation to estimate their learning style. If a student prefers visual learning, visual materials can be provided. If a student prefers auditory learning, audio materials can be provided. Furthermore, if a student prefers experiential learning, practical tasks can be provided. This allows the system to suggest the most suitable learning methods for each student, thereby enhancing learning effectiveness.

[0107] The HomeTutor system can further analyze students' learning pace using emotion estimation capabilities and provide appropriate learning plans. For example, it can analyze students' facial expressions and voice using emotion estimation to estimate their learning pace. If a student is tired, it can suggest slowing down their learning pace. Conversely, if a student is focused, it can suggest speeding up their learning pace. Furthermore, it can adjust break times according to the learning pace to promote efficient learning. This allows for the provision of an optimal learning plan tailored to each student's learning pace, thereby enhancing learning effectiveness.

[0108] The HomeTutor system can further analyze students' understanding of the learning material using emotion estimation capabilities and provide appropriate feedback. For example, it can analyze students' facial expressions and voice using emotion estimation to estimate their level of understanding. If understanding is low, additional explanations or supplementary materials can be provided. If understanding is high, assignments can be given to help students move on to the next step. Furthermore, the learning content can be adjusted according to the level of understanding to promote effective learning. This allows for the provision of optimal feedback tailored to each student's level of understanding, thereby enhancing learning effectiveness.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The supplementary section complements the learning content. For example, it can review and reinforce what was learned at school, deepening understanding. The supplementary section provides additional materials and supplementary explanations. The supplementary section analyzes the student's learning history and generates materials tailored to their level of understanding. Using AI, it can analyze the student's learning history and generate materials tailored to their level of understanding. Step 2: The response unit responds to questions based on the learning content supplemented by the supplementary unit. For example, it provides 24-hour support for students' questions. The response unit provides text-based answers, voice responses, etc. The response unit uses natural language processing to provide accurate answers to students' questions. AI can be used to perform natural language processing on students' questions and provide accurate answers. Step 3: The planning unit creates a learning plan based on the information obtained by the response unit. For example, it provides a home study plan tailored to the student's situation. The planning unit creates plans based on learning objectives and plans based on time allocation. The planning unit analyzes the student's learning progress and proposes the optimal learning plan. Using AI, it is possible to analyze the student's learning progress and propose the optimal learning plan.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the complementation unit, response unit, planning unit, optimization unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the complementation unit is implemented by the control unit 46A of the smart device 14, which reviews and reinforces the learning content at school and generates teaching materials according to the level of understanding. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs natural language processing on student questions and provides accurate answers. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning progress and proposes an optimal learning plan. The optimization unit is implemented by the control unit 46A of the smart device 14, which analyzes the student's learning patterns and proposes an optimal learning environment. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning data and reports the learning status to the parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the complementation unit, response unit, planning unit, optimization unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the complementation unit is implemented by the control unit 46A of the smart glasses 214, which reviews and reinforces the content learned at school and generates teaching materials according to the level of understanding. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs natural language processing on student questions and provides accurate answers. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning progress and proposes an optimal learning plan. The optimization unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the student's learning patterns and proposes an optimal learning environment. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning data and reports the learning status to the parents. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the complementation unit, response unit, planning unit, optimization unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the complementation unit is implemented by the control unit 46A of the headset terminal 314, which reviews and reinforces the learning content at school and generates teaching materials according to the level of understanding. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs natural language processing on student questions and provides accurate answers. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning progress and proposes an optimal learning plan. The optimization unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the student's learning patterns and proposes an optimal learning environment. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning data and reports the learning status to the parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the complementation unit, response unit, planning unit, optimization unit, and reporting unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the complementation unit is implemented by the control unit 46A of the robot 414, which reviews and reinforces the learning content at school and generates teaching materials according to the level of understanding. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs natural language processing on student questions and provides accurate answers. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning progress and proposes an optimal learning plan. The optimization unit is implemented by the control unit 46A of the robot 414, which analyzes the student's learning patterns and proposes an optimal learning environment. The reporting unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the student's learning data and reports the learning status to the parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A supplementary section that complements the learning content, A response unit that responds to questions based on the learning content supplemented by the aforementioned supplementary unit, A planning unit that creates a learning plan based on the information obtained from the response unit, Equipped with A system characterized by the following features. (Note 2) It includes an optimization unit to optimize the learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a reporting department that reports learning progress to parents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supplementary unit is, Analyze students' learning history and generate learning materials tailored to their level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 5) The response unit is Using natural language processing, we provide accurate answers to students' questions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, We analyze students' learning progress and propose the optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supplementary unit is, The system estimates students' emotions and adjusts the difficulty level of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supplementary unit is, We analyze students' past learning history to determine the optimal order in which learning materials are presented. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supplementary unit is, When supplementing learning content, add relevant topics based on students' interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supplementary unit is, The system estimates students' emotions and selects the format of teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supplementary unit is, When supplementing learning content, provide materials relevant to the student's region, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supplementary unit is, When supplementing learning content, analyze students' social media activity and provide relevant materials. The system described in Appendix 1, characterized by the features described herein. (Note 13) The response unit is The system estimates the students' emotions and adjusts the way they express their responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The response unit is When answering questions, refer to the student's past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 15) The response unit is When answering questions, provide additional explanations and examples according to the students' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 16) The response unit is The system estimates students' emotions and adjusts the level of detail in their responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The response unit is When answering questions, prioritize answers based on when students submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 18) The response unit is When answering questions, refer to the student's relevant learning history to improve the accuracy of the answers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, The system estimates students' emotions and adjusts the pace of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, When creating a learning plan, we refer to the student's past learning progress to propose the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When creating a learning plan, we provide a customized plan tailored to the student's goals and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, The system estimates students' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, When creating a study plan, we provide the optimal study plan by taking into account the student's daily rhythm and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, When creating a study plan, refer to the student's relevant learning history to improve the accuracy of the plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, The system estimates students' emotions and adjusts the learning environment settings based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The optimization unit, When optimizing the learning environment, we refer to students' past learning patterns to suggest the optimal environment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The optimization unit, The system estimates students' emotions and adjusts break times based on those estimates. The system described in Appendix 2, characterized by the features described herein. (Note 28) The optimization unit, When optimizing the learning environment, we take students' device information into consideration to provide the optimal environment. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned reporting department, We estimate the student's emotions and adjust the content of the report to the parents based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned reporting department, When reporting to parents, the most appropriate reporting method is selected by referring to the student's past learning data. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned reporting department, The system estimates the students' emotions and adjusts the frequency of reports based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned reporting department, When reporting to parents, we provide the most appropriate reporting method, taking into account the student's daily routine and schedule. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A supplementary section that complements the learning content, A response unit that responds to questions based on the learning content supplemented by the aforementioned supplementary unit, A planning unit that creates a learning plan based on the information obtained from the response unit, Equipped with A system characterized by the following features.

2. It includes an optimization unit to optimize the learning environment. The system according to feature 1.

3. It includes a reporting department that reports learning progress to parents. The system according to feature 1.

4. The aforementioned supplementary unit is, Analyze students' learning history and generate learning materials tailored to their level of understanding. The system according to feature 1.

5. The response unit is Using natural language processing, we provide accurate answers to students' questions. The system according to feature 1.

6. The aforementioned planning department, We analyze students' learning progress and propose the optimal learning plan. The system according to feature 1.

7. The aforementioned supplementary unit is, The system estimates students' emotions and adjusts the difficulty level of the learning materials based on those estimated emotions. The system according to feature 1.

8. The aforementioned supplementary unit is, We analyze students' past learning history to determine the optimal order in which learning materials are presented. The system according to feature 1.

9. The aforementioned supplementary unit is, When supplementing learning content, add relevant topics based on students' interests and concerns. The system according to feature 1.

10. The aforementioned supplementary unit is, The system estimates students' emotions and selects the format of teaching materials based on those estimated emotions. The system according to feature 1.

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