system

Abstract

The system according to this embodiment aims to grasp students' learning progress in real time and provide them with an optimal learning plan. [Solution] The system according to the embodiment comprises a monitoring unit, a plan provision unit, a communication support unit, and a question answering unit. The monitoring unit monitors the student's learning progress in real time. The plan provision unit provides an optimal learning plan based on the data collected by the monitoring unit. The communication support unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. The question answering unit answers the student's questions in real time based on the learning plan provided by the plan provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to grasp the learning progress of students in real time and provide an optimal learning plan.

[0005] The system according to the embodiment aims to grasp the learning progress of students in real time and provide an optimal learning plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a plan provision unit, a communication support unit, and a question answering unit. The monitoring unit monitors the student's learning progress in real time. The plan provision unit provides an optimal learning plan based on the data collected by the monitoring unit. The communication support unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. The question answering unit answers the student's questions in real time based on the learning plan provided by the plan provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp students' learning progress in real time and provide an optimal learning plan. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is an educational service that provides individualized instruction to children of dual-income families using generative AI. This educational support system monitors students' learning progress in real time and provides an optimal learning plan. Furthermore, it aims to improve the quality of education by supporting communication between parents and teachers. For example, when a student is solving a math problem, the educational support system analyzes the answer and evaluates the level of understanding. This allows the system to identify the student's weaknesses and provide an individually optimized learning plan. The educational support system also compiles the student's learning progress in a report format and provides it to parents and teachers. This makes it easier for parents to understand their child's learning situation, and allows teachers to adjust their teaching methods based on the student's progress. Furthermore, the educational support system answers students' questions in real time. For example, if a student asks, "I don't know how to solve this problem," the educational support system analyzes the question and provides an appropriate answer. This allows students to resolve their doubts immediately and improves their learning efficiency. In this way, the educational support system aims to improve the quality of education by providing individualized instruction to children of dual-income families using generative AI technology and supporting communication between parents and teachers. This allows the educational support system to monitor students' learning progress in real time, provide optimal learning plans, support communication between parents and teachers, and answer students' questions in real time.

[0029] The educational support system according to this embodiment comprises a monitoring unit, a plan provision unit, a communication support unit, and a question answering unit. The monitoring unit monitors the student's learning progress in real time. For example, the monitoring unit analyzes the student's answers to problems and evaluates their level of understanding. The monitoring unit can also collect the student's learning log and record their progress. Furthermore, the monitoring unit can analyze the student's learning style and patterns and collect data to provide an individually optimized learning plan. The plan provision unit provides an optimal learning plan based on the data collected by the monitoring unit. For example, the plan provision unit identifies the student's weaknesses and creates a learning plan to overcome those weaknesses. The plan provision unit can also provide short-term and long-term learning plans based on the student's learning goals. Furthermore, the plan provision unit can provide a customized learning plan tailored to the student's learning style. The communication support unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. For example, the communication support unit compiles the student's learning progress in report format and provides it to parents and teachers. Furthermore, the Communication Support Unit can provide a platform for parents and teachers to exchange opinions about students' learning progress. The Communication Support Unit can also support parents and teachers in coordinating teaching methods based on students' learning goals. The Question Answering Unit answers students' questions in real time based on the learning plan provided by the Plan Provisioning Unit. For example, if a student asks, "I don't know how to solve this problem," the Question Answering Unit analyzes the question and provides an appropriate answer. The Question Answering Unit can also explain the answer in an easy-to-understand manner for students. Furthermore, the Question Answering Unit can provide relevant learning materials and reference books based on the student's questions. As a result, the educational support system according to this embodiment can monitor students' learning progress in real time, provide optimal learning plans, support communication between parents and teachers, and answer students' questions in real time.

[0030] The monitoring department monitors students' learning progress in real time. Specifically, it analyzes the answers to problems students have solved and evaluates their level of understanding. For example, it can use AI to analyze students' answer patterns and quantify their level of understanding based on the correct answer rate and answer time. The monitoring department can also collect students' learning logs and record their progress. The learning logs include learning time, learning content, and answer results, and this data can be used to understand students' learning habits and tendencies. Furthermore, the monitoring department can analyze students' learning styles and patterns and collect data to provide individually optimized learning plans. For example, it can analyze when students can concentrate best on learning and what types of problems they are strong in, and use this to create individualized learning plans. This allows the monitoring department to have a detailed understanding of each student's learning situation and provide support tailored to their individual needs. In addition, the monitoring department stores the collected data on the cloud and makes it accessible to teachers and parents, making it easy to share learning progress. This allows teachers and parents to check students' learning progress in real time and provide appropriate guidance and support.

[0031] The plan provision department provides optimal learning plans based on data collected by the monitoring department. Specifically, it identifies students' weaknesses and creates learning plans to overcome them. For example, it can use AI to analyze students' answer data and automatically extract weaknesses in specific areas or question formats. The plan provision department can also provide short-term and long-term learning plans based on students' learning goals. Short-term learning plans may aim to prepare for the next test or review specific units, while long-term learning plans include comprehensive learning plans for end-of-year exams and entrance exams. Furthermore, the plan provision department can provide customized learning plans tailored to each student's learning style. For example, it can provide materials that make extensive use of diagrams and graphs for students who prefer visual learning, and materials that include audio explanations for students who prefer auditory learning. This allows the plan provision department to provide optimal learning plans that meet the learning needs of each individual student, maximizing learning effectiveness. In addition, the plan provision department can regularly evaluate the progress of the learning plan and revise it as needed. This enables flexible responses to students' learning situations and realizes effective learning support.

[0032] The Communication Support Department supports communication between parents and teachers based on the learning plans provided by the Plan Provision Department. Specifically, it compiles student learning progress into reports and provides them to parents and teachers. These reports include study time, answer results, and comprehension assessments, allowing parents and teachers to grasp the student's learning status at a glance. The Communication Support Department can also provide a platform for parents and teachers to exchange opinions on the student's learning status. For example, it supports real-time exchange of opinions and information sharing using online conferencing systems and chat functions. Furthermore, the Communication Support Department can support parents and teachers in coordinating teaching methods based on the student's learning goals. For example, they can jointly create teaching plans tailored to the student's learning goals and revise the plans as progress is made. In this way, the Communication Support Department enables parents and teachers to work together to support the student's learning and achieve effective instruction. In addition, the Communication Support Department can collect feedback from parents and teachers and use it to improve the entire system. In this way, the Communication Support Department can always provide effective support based on the latest information and maximize the effectiveness of student learning.

[0033] The question-answering unit provides real-time answers to students' questions based on the learning plans provided by the plan provider unit. Specifically, when a student asks, "I don't know how to solve this problem," the unit analyzes the question and provides an appropriate answer. For example, it can use AI to analyze the content of the student's question and search for relevant answers in a database. The question-answering unit can also explain the answers in an easy-to-understand manner for students. For example, it can use step-by-step explanations and diagrams to explain how to solve a problem in detail. Furthermore, the question-answering unit can provide relevant learning materials and reference books based on the content of the student's question. For example, it can provide links to pages in reference books or online materials related to a specific problem, supporting students in their independent learning. In this way, the question-answering unit can quickly resolve students' doubts and improve the efficiency of their learning. In addition, the question-answering unit can record the student's question history and use it to improve future learning plans. For example, it can create additional learning plans for specific subjects or problem formats based on frequently asked questions. This allows the question-answering unit to respond flexibly to students' learning needs, enabling effective learning support.

[0034] The monitoring unit can analyze a student's past learning history and select the optimal monitoring timing. For example, the monitoring unit can monitor a student during times when the student has shown high levels of concentration in the past. The monitoring unit can also intensify monitoring when a student is studying a subject they have struggled with in the past. Furthermore, the monitoring unit can intensify monitoring during periods when the student has fallen behind in their learning progress in the past. This enables effective monitoring by selecting the optimal monitoring timing based on the student's past learning history. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0035] The monitoring unit can filter data when monitoring learning progress, taking into account the student's current learning environment and level of concentration. For example, if a student is learning in a quiet environment, the monitoring unit will perform detailed monitoring. Conversely, if a student is learning in a noisy environment, the monitoring unit can reduce the accuracy of the monitoring. Furthermore, if a student is concentrating, the monitoring unit can increase the frequency of monitoring. By adjusting the monitoring according to the student's learning environment and level of concentration, more accurate monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0036] The monitoring unit can prioritize the collection of highly relevant data when monitoring learning progress, taking into account the student's geographical location. For example, if a student is studying at home, the monitoring unit will prioritize collecting learning data from home. It can also prioritize collecting learning data from school if the student is studying at school. Furthermore, if the student is studying in the library, the monitoring unit will prioritize collecting learning data from the library. This allows for more effective monitoring by prioritizing the collection of highly relevant data based on the student's geographical location. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0037] The monitoring unit can analyze students' social media activity and collect relevant data when monitoring learning progress. For example, if a student posts about their studies on social media, the monitoring unit can analyze those posts and reflect them in the learning progress. The monitoring unit can also analyze questions students ask about their studies on social media and reflect those questions in the learning progress. Furthermore, if a student participates in discussions about their studies on social media, the monitoring unit can analyze those discussions and reflect those discussions in the learning progress. This allows for more accurate monitoring of learning progress by collecting relevant data based on students' social media activity. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0038] The plan provider can adjust the level of detail in a learning plan based on the importance of the learning content. For example, the plan provider can provide a detailed learning plan before an important exam. It can also provide a simple learning plan for daily study. Furthermore, if the plan provider wants to focus on a particular subject, it can provide a detailed learning plan for that subject. By adjusting the level of detail in the plan based on the importance of the learning content, it can provide an effective learning plan. Some or all of the above processing in the plan provider may be performed using generative AI, or it may be performed without using generative AI.

[0039] The plan provider can apply different plan algorithms to each subject when providing learning plans. For example, the plan provider can detail problem-solving steps in a mathematics learning plan. It can also consider the balance between listening and reading in an English learning plan. Furthermore, it can include both experimentation and theory in a science learning plan. This allows for the provision of more effective learning plans by applying different plan algorithms to each subject. Some or all of the above processing in the plan provider may be performed using generative AI, or it may be performed without using generative AI.

[0040] The plan provider can prioritize learning plans based on the submission deadlines for learning content when providing them. For example, if an important exam is approaching, the plan provider will prioritize learning plans related to that exam. Similarly, if an assignment deadline is approaching, the plan provider can prioritize learning plans related to that assignment. Furthermore, if a regular test is approaching, the plan provider can prioritize learning plans related to that test. This allows for the provision of effective learning plans by prioritizing them based on the submission deadlines for learning content. Some or all of the above processing in the plan provider may be performed using or without generative AI.

[0041] The plan provider can adjust the order of learning plans based on the relevance of the learning content when providing them. For example, the plan provider can provide a plan in which students solve application problems after learning the basics of mathematics. It can also provide a plan in which students read after learning English grammar. Furthermore, it can provide a plan in which students conduct experiments after learning scientific theories. By adjusting the order of plans based on the relevance of the learning content, an effective learning plan can be provided. Some or all of the above processing in the plan provider may be performed using generative AI, or it may be performed without using generative AI.

[0042] The communication support unit can select the optimal support method by referring to past communication history during communication support. For example, the communication support unit can select the optimal support method based on the past communication history between parents and teachers. The communication support unit can also prioritize communication methods that parents have preferred in the past. Furthermore, the communication support unit can prioritize communication methods that teachers have found effective in the past. This enables effective communication support by selecting the optimal support method based on past communication history. Some or all of the above processing in the communication support unit may be performed using generative AI, or it may be performed without using generative AI.

[0043] The communication support department can select the optimal support method when providing communication support, taking into account the geographical location information of parents and teachers. For example, if a parent lives far away, the communication support department can prioritize online communication. It can also prioritize face-to-face communication if the teacher is at school. Furthermore, if a parent is on a business trip, the communication support department can prioritize communication via email or messaging. This enables effective communication support by selecting the optimal support method based on the geographical location information of parents and teachers. Some or all of the above processing in the communication support department may be performed using generative AI, or it may be performed without using generative AI.

[0044] The question-answering unit can select the optimal response method by referring to the student's past question history when answering questions. For example, the question-answering unit can select the optimal response method based on the content of questions the student has asked in the past. The question-answering unit can also prioritize response methods that the student found easy to understand in the past. Furthermore, the question-answering unit can provide detailed explanations for questions that the student has had difficulty with in the past. In this way, by selecting the optimal response method based on the student's past question history, effective question-answering becomes possible. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0045] The question-answering unit can customize its response methods based on the student's current learning situation when answering questions. For example, if a student is solving a math problem, the question-answering unit can provide an answer using mathematical formulas. It can also provide answers related to grammar and vocabulary if a student is solving an English problem. Furthermore, if a student is conducting a science experiment, the question-answering unit can provide answers related to the experimental procedure. This allows for the provision of more appropriate answers by customizing the response methods according to the student's current learning situation. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0046] The question-answering unit can select the optimal response method when answering questions, taking into account the student's geographical location. For example, if the student is studying at home, the question-answering unit will provide an answer suitable for home study. It can also provide an answer suitable for school study if the student is studying at school. Furthermore, if the student is studying in the library, it can provide an answer suitable for library study. This enables effective question-answering by selecting the optimal response method based on the student's geographical location. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0047] The question-answering unit can analyze a student's social media activity and suggest a response method when answering questions. For example, if a student asks a question about learning on social media, the question-answering unit will provide an answer to that question. Furthermore, if a student participates in a discussion about learning on social media, the question-answering unit can provide an answer based on that discussion. In addition, if a student makes a post about learning on social media, the question-answering unit can provide an answer based on that post. This allows for the provision of more appropriate answers by suggesting a response method based on the student's social media activity. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The educational support system can also include a style analysis unit that analyzes students' learning styles. This unit analyzes students' learning history and behavioral patterns to identify the optimal learning style. For example, if a student prefers visual learning, it can provide a learning plan that makes extensive use of visual aids. If a student prefers auditory learning, it can provide a learning plan centered on audio materials. Furthermore, if a student prefers practical learning, it can provide a learning plan that includes experiments and practical exercises. This allows for the provision of optimal learning plans tailored to each student's learning style, thereby maximizing learning effectiveness.

[0050] The educational support system can also include an environmental monitoring unit that monitors students' learning environment. This unit monitors students' learning environment in real time and provides an optimal learning environment. For example, if a student is studying in a noisy environment, it can provide noise cancellation. It can also provide appropriate lighting if a student is studying in a dark environment. Furthermore, it can monitor whether students are studying at a comfortable temperature and adjust the temperature as needed. This optimizes the student's learning environment, thereby maximizing learning effectiveness.

[0051] The educational support system can also include a motivation enhancement unit to further improve students' learning motivation. This unit provides rewards and incentives based on students' learning progress. For example, it can award badges or points when students achieve specific goals. It can also offer special rewards for students who continue learning consistently. Furthermore, it can provide a ranking function to increase motivation by allowing students to compete with others. This maximizes learning effectiveness by improving students' learning motivation.

[0052] The educational support system can also include a predictive analytics unit that analyzes students' learning history and predicts future learning plans. The predictive analytics unit predicts future learning progress based on students' past learning data. For example, it can provide focused learning plans for subjects students have struggled with in the past. It can also offer further challenges for subjects students excel in. Furthermore, it can suggest appropriate learning schedules according to the student's learning pace. This allows for the maximization of learning effectiveness by predicting future learning plans based on students' learning history.

[0053] The educational support system can also include a progress visualization unit that visualizes students' learning progress. This unit displays students' learning progress using graphs and charts, making it easier to understand visually. For example, it can display students' learning progress as a line graph, or show their achievement of learning goals as a pie chart. Furthermore, it can display students' study time as a bar graph. By visualizing students' learning progress, the effectiveness of learning can be maximized.

[0054] The educational support system can also include a performance evaluation unit that assesses students' learning performance. This unit quantitatively evaluates students' learning outcomes and provides feedback. For example, it can evaluate scores and correct answer rates based on students' test results. It can also evaluate submission rates and completion rates based on students' assignment submission status. Furthermore, it can evaluate learning efficiency based on students' study time. This allows for the maximization of learning effectiveness by evaluating students' learning performance.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The monitoring unit monitors students' learning progress in real time. Specifically, it analyzes the answers to problems students have solved and evaluates their level of understanding. It also collects students' learning logs and records their progress. Furthermore, it analyzes students' learning styles and patterns and collects data to provide individually optimized learning plans. Step 2: The plan provision department provides the optimal learning plan based on the data collected by the monitoring department. Specifically, it identifies the student's weaknesses and creates a learning plan to overcome those weaknesses. It also provides short-term and long-term learning plans based on the student's learning goals. Furthermore, it provides a customized learning plan tailored to the student's learning style. Step 3: The Communication Support Department supports communication between parents and teachers based on the learning plan provided by the Plan Provision Department. Specifically, it compiles reports on students' learning progress and provides them to parents and teachers. It also provides a platform for parents and teachers to exchange opinions on students' learning status. Furthermore, it supports parents and teachers in coordinating teaching methods based on students' learning objectives. Step 4: The question-answering unit answers students' questions in real time based on the learning plan provided by the plan provider unit. Specifically, when a student asks, "I don't know how to solve this problem," the unit analyzes the question and provides an appropriate answer. It also explains the answer in an easy-to-understand way so that the student can easily grasp it. Furthermore, it provides relevant learning materials and reference books based on the content of the student's question.

[0057] (Example of form 2) The educational support system according to an embodiment of the present invention is an educational service that provides individualized instruction to children of dual-income families using generative AI. This educational support system monitors students' learning progress in real time and provides an optimal learning plan. Furthermore, it aims to improve the quality of education by supporting communication between parents and teachers. For example, when a student is solving a math problem, the educational support system analyzes the answer and evaluates the level of understanding. This allows the system to identify the student's weaknesses and provide an individually optimized learning plan. The educational support system also compiles the student's learning progress in a report format and provides it to parents and teachers. This makes it easier for parents to understand their child's learning situation, and allows teachers to adjust their teaching methods based on the student's progress. Furthermore, the educational support system answers students' questions in real time. For example, if a student asks, "I don't know how to solve this problem," the educational support system analyzes the question and provides an appropriate answer. This allows students to resolve their doubts immediately and improves their learning efficiency. In this way, the educational support system aims to improve the quality of education by providing individualized instruction to children of dual-income families using generative AI technology and supporting communication between parents and teachers. This allows the educational support system to monitor students' learning progress in real time, provide optimal learning plans, support communication between parents and teachers, and answer students' questions in real time.

[0058] The educational support system according to this embodiment comprises a monitoring unit, a plan provision unit, a communication support unit, and a question answering unit. The monitoring unit monitors the student's learning progress in real time. For example, the monitoring unit analyzes the student's answers to problems and evaluates their level of understanding. The monitoring unit can also collect the student's learning log and record their progress. Furthermore, the monitoring unit can analyze the student's learning style and patterns and collect data to provide an individually optimized learning plan. The plan provision unit provides an optimal learning plan based on the data collected by the monitoring unit. For example, the plan provision unit identifies the student's weaknesses and creates a learning plan to overcome those weaknesses. The plan provision unit can also provide short-term and long-term learning plans based on the student's learning goals. Furthermore, the plan provision unit can provide a customized learning plan tailored to the student's learning style. The communication support unit supports communication between parents and teachers based on the learning plan provided by the plan provision unit. For example, the communication support unit compiles the student's learning progress in report format and provides it to parents and teachers. Furthermore, the Communication Support Unit can provide a platform for parents and teachers to exchange opinions about students' learning progress. The Communication Support Unit can also support parents and teachers in coordinating teaching methods based on students' learning goals. The Question Answering Unit answers students' questions in real time based on the learning plan provided by the Plan Provisioning Unit. For example, if a student asks, "I don't know how to solve this problem," the Question Answering Unit analyzes the question and provides an appropriate answer. The Question Answering Unit can also explain the answer in an easy-to-understand manner for students. Furthermore, the Question Answering Unit can provide relevant learning materials and reference books based on the student's questions. As a result, the educational support system according to this embodiment can monitor students' learning progress in real time, provide optimal learning plans, support communication between parents and teachers, and answer students' questions in real time.

[0059] The monitoring department monitors students' learning progress in real time. Specifically, it analyzes the answers to problems students have solved and evaluates their level of understanding. For example, it can use AI to analyze students' answer patterns and quantify their level of understanding based on the correct answer rate and answer time. The monitoring department can also collect students' learning logs and record their progress. The learning logs include learning time, learning content, and answer results, and this data can be used to understand students' learning habits and tendencies. Furthermore, the monitoring department can analyze students' learning styles and patterns and collect data to provide individually optimized learning plans. For example, it can analyze when students can concentrate best on learning and what types of problems they are strong in, and use this to create individualized learning plans. This allows the monitoring department to have a detailed understanding of each student's learning situation and provide support tailored to their individual needs. In addition, the monitoring department stores the collected data on the cloud and makes it accessible to teachers and parents, making it easy to share learning progress. This allows teachers and parents to check students' learning progress in real time and provide appropriate guidance and support.

[0060] The plan provision department provides optimal learning plans based on data collected by the monitoring department. Specifically, it identifies students' weaknesses and creates learning plans to overcome them. For example, it can use AI to analyze students' answer data and automatically extract weaknesses in specific areas or question formats. The plan provision department can also provide short-term and long-term learning plans based on students' learning goals. Short-term learning plans may aim to prepare for the next test or review specific units, while long-term learning plans include comprehensive learning plans for end-of-year exams and entrance exams. Furthermore, the plan provision department can provide customized learning plans tailored to each student's learning style. For example, it can provide materials that make extensive use of diagrams and graphs for students who prefer visual learning, and materials that include audio explanations for students who prefer auditory learning. This allows the plan provision department to provide optimal learning plans that meet the learning needs of each individual student, maximizing learning effectiveness. In addition, the plan provision department can regularly evaluate the progress of the learning plan and revise it as needed. This enables flexible responses to students' learning situations and realizes effective learning support.

[0061] The Communication Support Department supports communication between parents and teachers based on the learning plans provided by the Plan Provision Department. Specifically, it compiles student learning progress into reports and provides them to parents and teachers. These reports include study time, answer results, and comprehension assessments, allowing parents and teachers to grasp the student's learning status at a glance. The Communication Support Department can also provide a platform for parents and teachers to exchange opinions on the student's learning status. For example, it supports real-time exchange of opinions and information sharing using online conferencing systems and chat functions. Furthermore, the Communication Support Department can support parents and teachers in coordinating teaching methods based on the student's learning goals. For example, they can jointly create teaching plans tailored to the student's learning goals and revise the plans as progress is made. In this way, the Communication Support Department enables parents and teachers to work together to support the student's learning and achieve effective instruction. In addition, the Communication Support Department can collect feedback from parents and teachers and use it to improve the entire system. In this way, the Communication Support Department can always provide effective support based on the latest information and maximize the effectiveness of student learning.

[0062] The question-answering unit provides real-time answers to students' questions based on the learning plans provided by the plan provider unit. Specifically, when a student asks, "I don't know how to solve this problem," the unit analyzes the question and provides an appropriate answer. For example, it can use AI to analyze the content of the student's question and search for relevant answers in a database. The question-answering unit can also explain the answers in an easy-to-understand manner for students. For example, it can use step-by-step explanations and diagrams to explain how to solve a problem in detail. Furthermore, the question-answering unit can provide relevant learning materials and reference books based on the content of the student's question. For example, it can provide links to pages in reference books or online materials related to a specific problem, supporting students in their independent learning. In this way, the question-answering unit can quickly resolve students' doubts and improve the efficiency of their learning. In addition, the question-answering unit can record the student's question history and use it to improve future learning plans. For example, it can create additional learning plans for specific subjects or problem formats based on frequently asked questions. This allows the question-answering unit to respond flexibly to students' learning needs, enabling effective learning support.

[0063] The monitoring unit can estimate a student's emotions and adjust the method of monitoring learning progress based on the estimated emotions. For example, if a student is feeling stressed, the monitoring unit can reduce the frequency of monitoring learning progress and provide a more relaxing environment. The monitoring unit can also perform detailed monitoring and meticulously record learning progress when a student is focused. Furthermore, if a student is tired, the monitoring unit can temporarily suspend monitoring and encourage a break. This allows for more appropriate monitoring by adjusting the method of monitoring learning progress 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.

[0064] The monitoring unit can analyze a student's past learning history and select the optimal monitoring timing. For example, the monitoring unit can monitor a student during times when the student has shown high levels of concentration in the past. The monitoring unit can also intensify monitoring when a student is studying a subject they have struggled with in the past. Furthermore, the monitoring unit can intensify monitoring during periods when the student has fallen behind in their learning progress in the past. This enables effective monitoring by selecting the optimal monitoring timing based on the student's past learning history. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0065] The monitoring unit can filter data when monitoring learning progress, taking into account the student's current learning environment and level of concentration. For example, if a student is learning in a quiet environment, the monitoring unit will perform detailed monitoring. Conversely, if a student is learning in a noisy environment, the monitoring unit can reduce the accuracy of the monitoring. Furthermore, if a student is concentrating, the monitoring unit can increase the frequency of monitoring. By adjusting the monitoring according to the student's learning environment and level of concentration, more accurate monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0066] The monitoring unit can estimate students' emotions and prioritize monitoring results based on the estimated emotions. For example, if a student is stressed, the monitoring unit will prioritize displaying monitoring results related to stress reduction. It can also prioritize displaying monitoring results related to learning progress if the student is relaxed. Furthermore, if the student is focused, it can prioritize displaying monitoring results related to the quality of learning. This allows for the priority of important information by prioritizing monitoring results 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.

[0067] The monitoring unit can prioritize the collection of highly relevant data when monitoring learning progress, taking into account the student's geographical location. For example, if a student is studying at home, the monitoring unit will prioritize collecting learning data from home. It can also prioritize collecting learning data from school if the student is studying at school. Furthermore, if the student is studying in the library, the monitoring unit will prioritize collecting learning data from the library. This allows for more effective monitoring by prioritizing the collection of highly relevant data based on the student's geographical location. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0068] The monitoring unit can analyze students' social media activity and collect relevant data when monitoring learning progress. For example, if a student posts about their studies on social media, the monitoring unit can analyze those posts and reflect them in the learning progress. The monitoring unit can also analyze questions students ask about their studies on social media and reflect those questions in the learning progress. Furthermore, if a student participates in discussions about their studies on social media, the monitoring unit can analyze those discussions and reflect those discussions in the learning progress. This allows for more accurate monitoring of learning progress by collecting relevant data based on students' social media activity. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0069] The learning plan provider can estimate a student's emotions and adjust the presentation of the learning plan based on those emotions. For example, if a student is feeling stressed, the plan provider can provide a simple and easy-to-understand learning plan. If a student is relaxed, the plan provider can also provide a detailed learning plan. Furthermore, if a student is focused, the plan provider can provide a challenging learning plan. By adjusting the presentation of the learning plan according to the student's emotions, a more effective learning plan can be provided. 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.

[0070] The plan provider can adjust the level of detail in a learning plan based on the importance of the learning content. For example, the plan provider can provide a detailed learning plan before an important exam. It can also provide a simple learning plan for daily study. Furthermore, if the plan provider wants to focus on a particular subject, it can provide a detailed learning plan for that subject. By adjusting the level of detail in the plan based on the importance of the learning content, it can provide an effective learning plan. Some or all of the above processing in the plan provider may be performed using generative AI, or it may be performed without using generative AI.

[0071] The plan provider can apply different plan algorithms to each subject when providing learning plans. For example, the plan provider can detail problem-solving steps in a mathematics learning plan. It can also consider the balance between listening and reading in an English learning plan. Furthermore, it can include both experimentation and theory in a science learning plan. This allows for the provision of more effective learning plans by applying different plan algorithms to each subject. Some or all of the above processing in the plan provider may be performed using generative AI, or it may be performed without using generative AI.

[0072] The plan provider can estimate a student's emotions and adjust the length of the learning plan based on those emotions. For example, if a student is feeling stressed, the plan provider can provide a shorter learning plan. Conversely, if a student is relaxed, it can provide a longer learning plan. Furthermore, if a student is focused, it can provide a learning plan of appropriate length. This allows for the provision of more effective learning plans by adjusting the length 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.

[0073] The plan provider can prioritize learning plans based on the submission deadlines for learning content when providing them. For example, if an important exam is approaching, the plan provider will prioritize learning plans related to that exam. Similarly, if an assignment deadline is approaching, the plan provider can prioritize learning plans related to that assignment. Furthermore, if a regular test is approaching, the plan provider can prioritize learning plans related to that test. This allows for the provision of effective learning plans by prioritizing them based on the submission deadlines for learning content. Some or all of the above processing in the plan provider may be performed using or without generative AI.

[0074] The plan provider can adjust the order of learning plans based on the relevance of the learning content when providing them. For example, the plan provider can provide a plan in which students solve application problems after learning the basics of mathematics. It can also provide a plan in which students read after learning English grammar. Furthermore, it can provide a plan in which students conduct experiments after learning scientific theories. By adjusting the order of plans based on the relevance of the learning content, an effective learning plan can be provided. Some or all of the above processing in the plan provider may be performed using generative AI, or it may be performed without using generative AI.

[0075] The communication support unit can estimate the emotions of parents and teachers and adjust communication methods based on these estimates. For example, if a parent is feeling stressed, the communication support unit can provide a concise and easy-to-understand report. It can also provide detailed feedback if the teacher is relaxed. Furthermore, if a parent is busy, the communication support unit can provide a short, to-the-point report. This allows for more effective communication by adjusting communication methods according to the emotions of parents and teachers. 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 communication support unit can select the optimal support method by referring to past communication history during communication support. For example, the communication support unit can select the optimal support method based on the past communication history between parents and teachers. The communication support unit can also prioritize communication methods that parents have preferred in the past. Furthermore, the communication support unit can prioritize communication methods that teachers have found effective in the past. This enables effective communication support by selecting the optimal support method based on past communication history. Some or all of the above processing in the communication support unit may be performed using generative AI, or it may be performed without using generative AI.

[0077] The communication support unit can estimate the emotions of parents and teachers and prioritize communication based on these estimated emotions. For example, if a parent is feeling stressed, the communication support unit will prioritize providing important information. It can also provide detailed information if the teacher is relaxed. Furthermore, if a parent is busy, the communication support unit can prioritize providing concise information. This allows for the prioritization of important information by determining communication priorities according to the emotions of parents and teachers. 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.

[0078] The communication support department can select the optimal support method when providing communication support, taking into account the geographical location information of parents and teachers. For example, if a parent lives far away, the communication support department can prioritize online communication. It can also prioritize face-to-face communication if the teacher is at school. Furthermore, if a parent is on a business trip, the communication support department can prioritize communication via email or messaging. This enables effective communication support by selecting the optimal support method based on the geographical location information of parents and teachers. Some or all of the above processing in the communication support department may be performed using generative AI, or it may be performed without using generative AI.

[0079] The question-answering unit can estimate a student's emotions and adjust its question-answering method based on the estimated emotions. For example, if a student is stressed, the question-answering unit can provide a concise and easy-to-understand answer. If the student is relaxed, it can also provide a detailed explanation. Furthermore, if the student is focused, it can provide answers to challenging questions. By adjusting the question-answering method according to the student's emotions, more effective answers can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The question-answering unit can select the optimal response method by referring to the student's past question history when answering questions. For example, the question-answering unit can select the optimal response method based on the content of questions the student has asked in the past. The question-answering unit can also prioritize response methods that the student found easy to understand in the past. Furthermore, the question-answering unit can provide detailed explanations for questions that the student has had difficulty with in the past. In this way, by selecting the optimal response method based on the student's past question history, effective question-answering becomes possible. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0081] The question-answering unit can customize its response methods based on the student's current learning situation when answering questions. For example, if a student is solving a math problem, the question-answering unit can provide an answer using mathematical formulas. It can also provide answers related to grammar and vocabulary if a student is solving an English problem. Furthermore, if a student is conducting a science experiment, the question-answering unit can provide answers related to the experimental procedure. This allows for the provision of more appropriate answers by customizing the response methods according to the student's current learning situation. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0082] The question-answering unit can estimate a student's emotions and determine the priority of question-answering based on the estimated emotions. For example, if a student is feeling stressed, the question-answering unit will prioritize answering that student's questions. It can also prioritize questions from other students if the student is relaxed. Furthermore, if a student is focused, the question-answering unit can answer their questions quickly. This allows for prioritizing important questions by determining the priority of question-answering 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.

[0083] The question-answering unit can select the optimal response method when answering questions, taking into account the student's geographical location. For example, if the student is studying at home, the question-answering unit will provide an answer suitable for home study. It can also provide an answer suitable for school study if the student is studying at school. Furthermore, if the student is studying in the library, it can provide an answer suitable for library study. This enables effective question-answering by selecting the optimal response method based on the student's geographical location. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0084] The question-answering unit can analyze a student's social media activity and suggest a response method when answering questions. For example, if a student asks a question about learning on social media, the question-answering unit will provide an answer to that question. Furthermore, if a student participates in a discussion about learning on social media, the question-answering unit can provide an answer based on that discussion. In addition, if a student makes a post about learning on social media, the question-answering unit can provide an answer based on that post. This allows for the provision of more appropriate answers by suggesting a response method based on the student's social media activity. Some or all of the above processing in the question-answering unit may be performed using generative AI, or it may be performed without using generative AI.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The educational support system can also include a style analysis unit that analyzes students' learning styles. This unit analyzes students' learning history and behavioral patterns to identify the optimal learning style. For example, if a student prefers visual learning, it can provide a learning plan that makes extensive use of visual aids. If a student prefers auditory learning, it can provide a learning plan centered on audio materials. Furthermore, if a student prefers practical learning, it can provide a learning plan that includes experiments and practical exercises. This allows for the provision of optimal learning plans tailored to each student's learning style, thereby maximizing learning effectiveness.

[0087] The educational support system may also include an emotion adjustment unit that estimates students' emotions and adjusts the learning plan based on those emotions. If a student is feeling stressed, the emotion adjustment unit provides a relaxing learning plan, for example, by increasing break times to reduce stress. If the student is relaxed, it can also provide challenging tasks to enhance concentration. Furthermore, if the student is focused, it can provide additional tasks to accelerate learning progress. This allows for maximizing learning effectiveness by adjusting the learning plan according to the student's emotions.

[0088] The educational support system can also include an environmental monitoring unit that monitors students' learning environment. This unit monitors students' learning environment in real time and provides an optimal learning environment. For example, if a student is studying in a noisy environment, it can provide noise cancellation. It can also provide appropriate lighting if a student is studying in a dark environment. Furthermore, it can monitor whether students are studying at a comfortable temperature and adjust the temperature as needed. This optimizes the student's learning environment, thereby maximizing learning effectiveness.

[0089] The educational support system can also include a motivation enhancement unit to further improve students' learning motivation. This unit provides rewards and incentives based on students' learning progress. For example, it can award badges or points when students achieve specific goals. It can also offer special rewards for students who continue learning consistently. Furthermore, it can provide a ranking function to increase motivation by allowing students to compete with others. This maximizes learning effectiveness by improving students' learning motivation.

[0090] The educational support system may also include a feedback adjustment unit that estimates the student's emotions and adjusts the feedback on learning progress based on those emotions. The feedback adjustment unit provides positive feedback when the student is stressed, for example, by sending a message praising their effort. It can also provide detailed feedback when the student is relaxed. Furthermore, it can provide feedback indicating specific areas for improvement when the student is focused. This allows for maximizing learning effectiveness by adjusting feedback according to the student's emotions.

[0091] The educational support system can also include a predictive analytics unit that analyzes students' learning history and predicts future learning plans. The predictive analytics unit predicts future learning progress based on students' past learning data. For example, it can provide focused learning plans for subjects students have struggled with in the past. It can also offer further challenges for subjects students excel in. Furthermore, it can suggest appropriate learning schedules according to the student's learning pace. This allows for the maximization of learning effectiveness by predicting future learning plans based on students' learning history.

[0092] The educational support system can also include a content customization unit that estimates students' emotions and customizes learning content based on those emotions. If a student is feeling stressed, the customization unit can provide relaxing content, such as relaxation exercises to reduce stress. If a student is relaxed, it can also provide challenging content to enhance concentration. Furthermore, if a student is focused, it can provide additional content to accelerate learning progress. This allows for maximizing learning effectiveness by customizing learning content according to students' emotions.

[0093] The educational support system can also include a progress visualization unit that visualizes students' learning progress. This unit displays students' learning progress using graphs and charts, making it easier to understand visually. For example, it can display students' learning progress as a line graph, or show their achievement of learning goals as a pie chart. Furthermore, it can display students' study time as a bar graph. By visualizing students' learning progress, the effectiveness of learning can be maximized.

[0094] The educational support system may also include a progress adjustment unit that estimates students' emotions and adjusts learning progress based on those emotions. If a student is stressed, the progress adjustment unit will slow down the learning pace, for example, by reducing the amount of material. Conversely, if a student is relaxed, it can accelerate the learning pace. Furthermore, if a student is focused, it can provide additional tasks to optimize learning progress. This allows for maximizing learning effectiveness by adjusting learning progress according to students' emotions.

[0095] The educational support system can also include a performance evaluation unit that assesses students' learning performance. This unit quantitatively evaluates students' learning outcomes and provides feedback. For example, it can evaluate scores and correct answer rates based on students' test results. It can also evaluate submission rates and completion rates based on students' assignment submission status. Furthermore, it can evaluate learning efficiency based on students' study time. This allows for the maximization of learning effectiveness by evaluating students' learning performance.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The monitoring unit monitors students' learning progress in real time. Specifically, it analyzes the answers to problems students have solved and evaluates their level of understanding. It also collects students' learning logs and records their progress. Furthermore, it analyzes students' learning styles and patterns and collects data to provide individually optimized learning plans. Step 2: The plan provision department provides the optimal learning plan based on the data collected by the monitoring department. Specifically, it identifies the student's weaknesses and creates a learning plan to overcome those weaknesses. It also provides short-term and long-term learning plans based on the student's learning goals. Furthermore, it provides a customized learning plan tailored to the student's learning style. Step 3: The Communication Support Department supports communication between parents and teachers based on the learning plan provided by the Plan Provision Department. Specifically, it compiles reports on students' learning progress and provides them to parents and teachers. It also provides a platform for parents and teachers to exchange opinions on students' learning status. Furthermore, it supports parents and teachers in coordinating teaching methods based on students' learning objectives. Step 4: The question-answering unit answers students' questions in real time based on the learning plan provided by the plan provider unit. Specifically, when a student asks, "I don't know how to solve this problem," the unit analyzes the question and provides an appropriate answer. It also explains the answer in an easy-to-understand way so that the student can easily grasp it. Furthermore, it provides relevant learning materials and reference books based on the content of the student's question.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication support unit, and question answering unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit uses the camera 42 and microphone 38B of the smart device 14 to monitor the student's learning progress in real time and analyzes it using the control unit 46A. The plan provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an optimal learning plan based on the collected data. The communication support unit is implemented, for example, by the control unit 46A of the smart device 14 and supports communication between parents and teachers. The question answering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and answers students' questions in real time. 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.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication support unit, and question answering unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit uses the camera 42 and microphone 238 of the smart glasses 214 to monitor the student's learning progress in real time and analyzes it using the control unit 46A. The plan provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an optimal learning plan based on the collected data. The communication support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and supports communication between parents and teachers. The question answering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and answers students' questions in real time. 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.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication support unit, and question answering unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the student's learning progress in real time using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it using the control unit 46A. The plan provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides an optimal learning plan based on the collected data. The communication support unit is implemented in the specific processing unit 46A of the headset terminal 314 and supports communication between parents and teachers. The question answering unit is implemented in the specific processing unit 290 of the data processing unit 12 and answers students' questions in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In 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.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 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.

[0150] Each of the multiple elements described above, including the monitoring unit, plan provision unit, communication support unit, and question answering unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the monitoring unit uses the camera 42 and microphone 238 of the robot 414 to monitor the student's learning progress in real time and analyzes it using the control unit 46A. The plan provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an optimal learning plan based on the collected data. The communication support unit is implemented, for example, by the control unit 46A of the robot 414 and supports communication between parents and teachers. The question answering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and answers students' questions in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The monitoring department monitors students' learning progress in real time, A plan provision unit provides an optimal learning plan based on the data collected by the monitoring unit, A communication support department that supports communication between parents and teachers based on the learning plan provided by the aforementioned plan provision department, The system includes a question answering unit that answers students' questions in real time based on the learning plan provided by the aforementioned plan provisioning unit. A system characterized by the following features. (Note 2) The monitoring unit, Estimate students' emotions and adjust the method of monitoring learning progress based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, Analyze students' past learning history to select the optimal monitoring timing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, When monitoring learning progress, filtering is performed considering the student's current learning environment and level of concentration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, The system estimates students' emotions and prioritizes monitoring results based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, When monitoring learning progress, prioritize collecting highly relevant data by considering students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, When monitoring learning progress, analyze students' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned plan provision department, The system estimates students' emotions and adjusts the way learning plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned plan provision department, When providing a learning plan, we adjust the level of detail in the plan based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned plan provision department, When providing a learning plan, a different plan algorithm is applied for each subject. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned plan provision department, The system estimates students' emotions and adjusts the length of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned plan provision department, When providing a learning plan, we will prioritize the plan based on when the learning content will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan provision department, When providing a learning plan, the order of the plan will be adjusted based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Communication Support Department It estimates the emotions of parents and teachers and adjusts communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Communication Support Department When providing communication support, the system will refer to past communication history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Communication Support Department It estimates the emotions of parents and teachers and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Communication Support Department When providing communication support, the most suitable support method will be selected considering the geographical location information of both parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question answering unit is The system estimates students' emotions and adjusts the question-answering method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question answering unit is When answering questions, the system will refer to the student's past question history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question answering unit is When answering questions, customize the response method based on the student's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question answering unit is The system estimates the students' emotions and prioritizes question-answering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question answering unit is When answering questions, the most appropriate response method is selected considering the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question answering unit is When answering questions, analyze students' social media activity and suggest ways to respond. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors students' learning progress in real time, A plan provision unit provides an optimal learning plan based on the data collected by the monitoring unit, A communication support department that supports communication between parents and teachers based on the learning plan provided by the aforementioned plan provision department, The system includes a question answering unit that answers students' questions in real time based on the learning plan provided by the aforementioned plan provisioning unit. A system characterized by the following features.

2. The monitoring unit, Estimate students' emotions and adjust the method of monitoring learning progress based on the estimated emotions. The system according to feature 1.

3. The monitoring unit, Analyze students' past learning history to select the optimal monitoring timing. The system according to feature 1.

4. The monitoring unit, When monitoring learning progress, filtering is performed considering the student's current learning environment and level of concentration. The system according to feature 1.

5. The monitoring unit, The system estimates students' emotions and prioritizes monitoring results based on these estimated emotions. The system according to feature 1.

6. The monitoring unit, When monitoring learning progress, prioritize collecting highly relevant data by considering students' geographical location. The system according to feature 1.

7. The monitoring unit, When monitoring learning progress, analyze students' social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned plan provision department, The system estimates students' emotions and adjusts the way learning plans are presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned plan provision department, When providing a learning plan, we adjust the level of detail in the plan based on the importance of the learning content. The system according to feature 1.

10. The aforementioned plan provision department, When providing a learning plan, a different plan algorithm is applied for each subject. The system according to feature 1.

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