System

Abstract

An object of a system according to an embodiment is to reduce the burden on a teacher and improve the quality of education for students.SOLUTION: A system includes a question analysis unit, an answer generation unit, a feedback unit, and a data storage unit. The question analysis unit analyzes a question from a teacher or a student. The answer generation unit generates an answer to the question analyzed by the question analysis unit. The feedback unit provides the answer generated by the answer generation unit. The data storage unit stores questions and answers.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies are insufficient to address the issue of overworked teachers in the educational field and the need to improve the quality of education for students, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the burden on teachers and improve the quality of education for students. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit, an answer generation unit, a feedback unit, and a data accumulation unit. The question analysis unit analyzes questions from teachers or students. The answer generation unit generates answers to the questions analyzed by the question analysis unit. The feedback unit provides the answers generated by the answer generation unit. The data accumulation unit accumulates the questions and answers. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on teachers and improve the quality of education for students. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The education support system according to an embodiment of the present invention is a system that reduces teachers' excessive workload and improves the quality of education for students. In this system, a generative AI functions as a second teacher during class, delving deeper into the content of the class and resolving any questions. This reduces the burden on teachers and creates more interactive time between teachers and students.

[0029] An education support system according to an embodiment includes a question analysis unit, an answer generation unit, a feedback unit, and a data accumulation unit. The question analysis unit analyzes questions from teachers or students. For example, the question analysis unit analyzes text questions using natural language processing technology. The question analysis unit can also analyze audio questions using speech recognition technology. The question analysis unit can also classify questions related to a specific academic field. For example, the question analysis unit analyzes text questions to understand the intent of the question. Audio questions are converted into text using speech recognition technology and then analyzed. Questions related to a specific academic field are automatically classified based on the content of the question. The answer generation unit generates answers to the questions analyzed by the question analysis unit. For example, the answer generation unit generates text answers using a generation AI. The answer generation unit can also generate audio answers. The answer generation unit can also generate answers including visual materials. For example, the generation AI has learned a large amount of data and has advanced natural language processing capabilities. The generation AI generates appropriate answers based on the content of the question. The voice-format answer generates a text-format answer using speech synthesis technology. The answer including visual materials generates an answer including a chart or illustration. The feedback unit provides the answer generated by the answer generation unit. For example, the feedback unit provides answers in real time. The feedback unit can also provide periodic feedback. The feedback unit can also provide individual feedback. For example, real-time feedback provides immediate answers during class. Periodic feedback provides feedback collectively after class. Individual feedback provides feedback customized according to the student's level of understanding. The data accumulation unit accumulates questions and answers. For example, the data accumulation unit stores questions and answers using a database. The data accumulation unit can also set a data retention period. The data accumulation unit can also analyze the accumulated data to improve the accuracy of the generation AI. For example, the database efficiently stores a large number of questions and answers. The data retention period is set to automatically delete old data after a certain period.The accumulated data is used as learning data for the generative AI, contributing to improved accuracy. As a result, the educational support system according to the embodiment can provide prompt and appropriate answers to questions from teachers and students, improving the quality of education. For example, the generative AI can provide instant answers during class, making it easier for students to resolve their doubts and deepen their understanding. This also allows teachers to concentrate on the progress of the class, improving the quality of lessons. Furthermore, as question data from schools across the country is accumulated, the accuracy and quality of the generative AI can be improved, enabling more effective educational support.

[0030] The question analysis unit can automatically add historical background and scientific evidence related to the question and provide a detailed explanation. For example, in a history class, when a generative AI asks, "What caused World War II?", the question analysis unit automatically adds relevant historical background and scientific evidence and provides a detailed explanation. For example, it generates an answer that includes the economic factors and political background of the war. In a science class, when a generative AI asks, "What is the principle of light refraction?", the question analysis unit automatically adds relevant scientific evidence and experimental data and provides a detailed explanation. For example, it generates an answer that includes information on the refractive index and wavelength of light. In a literature class, when a generative AI asks, "What are the characteristics of Shakespeare's works?", the question analysis unit automatically adds relevant historical background and literary analysis and provides a detailed explanation. For example, it generates an answer that includes information on Shakespeare's historical background and the themes of his works. This allows for detailed explanations to be provided to deepen students' understanding.

[0031] The question analysis unit can evaluate students' understanding in real time and provide additional information and supplemental explanations based on their level of understanding. For example, the question analysis unit uses a generative AI to evaluate students' understanding in real time, and for students with low levels of understanding, it re-explains basic concepts and provides concrete examples. For example, in a mathematics class, a student who does not understand how to calculate the area of ​​a triangle is provided with basic formulas and specific calculation examples. The question analysis unit also provides more advanced content and applied problems to students with high levels of understanding. For example, in a physics class, a student who understands Newton's laws of motion is provided with applied problems and experimental data analysis. The question analysis unit also customizes lesson content and provides individual feedback based on the student's level of understanding. For example, in a history class, a student who does not have a deep understanding of a particular era can be provided with detailed background information about that era and additional explanations of related events. This makes it possible to provide information based on the student's level of understanding and achieve individualized learning support.

[0032] The question analysis unit can show connections with other academic fields and promote understanding from an interdisciplinary perspective. For example, in a history class, if the generative AI were to ask, "What were the effects of the Industrial Revolution?", it would show connections from the perspectives of economics and sociology, promoting interdisciplinary understanding. For example, it would explain the impact of the Industrial Revolution on the economy and changes in social structure. In a science class, if the question analysis unit were to ask, "What is the structure of DNA?", it would show connections not only from the perspectives of biology but also from the perspectives of chemistry and physics, promoting interdisciplinary understanding. For example, it would explain the chemical structure and physical properties of DNA. In a literature class, if the question analysis unit were to ask, "What are the characteristics of Shakespeare's works?", it would show connections from the perspectives of history and sociology, promoting interdisciplinary understanding. For example, it would explain Shakespeare's historical background and social impact. This promotes understanding from an interdisciplinary perspective and supports comprehensive learning.

[0033] The question analysis unit can automatically generate visual materials or simulations to facilitate visual understanding. For example, in a science class, when the generative AI asks, "What is the principle of refraction of light?", the question analysis unit automatically generates visual materials or simulations to facilitate visual understanding. For example, it provides animations and simulations showing the refraction of light. In a history class, when the question is asked, "What caused World War II?", the question analysis unit visually displays related historical events and maps to promote understanding. For example, it provides timelines and maps showing the progress of the war. In a mathematics class, when the question is asked, "I don't know how to find the area of ​​a triangle," the question analysis unit automatically generates visual diagrams and animations to facilitate visual understanding. For example, it provides an animation showing the steps to find the area of ​​a triangle. This allows the provision of visual materials and simulations to promote understanding.

[0034] The answer generation unit can provide examples and case studies to resolve questions and promote concrete understanding. For example, in a mathematics class, when the generation AI answers a question such as "I don't know how to find the area of ​​a triangle," the answer generation unit provides specific examples and case studies to promote understanding. For example, it presents a problem to find the area of ​​an actual triangle and explains the solution. In a history class, the answer generation unit provides related examples and case studies to promote concrete understanding. For example, it explains by citing the policies and events of a specific country. In a science class, the answer generation unit provides specific examples and case studies to promote understanding. For example, it explains based on actual experimental data and observation results. In this way, it is possible to provide specific examples and case studies to promote the resolution of questions.

[0035] The answer generation unit can refer to past question data and provide the most effective answer. For example, the generation AI in the answer generation unit refers to past question data and provides the most effective answer to a similar question. For example, in a math class, the answer generation unit provides the optimal solution based on past data for a question such as "I don't know how to find the area of ​​a triangle." In a history class, the answer generation unit refers to past question data and provides the most effective answer for a question such as "What caused World War II?" For example, if many students have had the same question in the past, the answer generation unit provides the optimal answer based on that data. In a science class, the answer generation unit refers to past question data and provides the most effective answer. For example, the answer is provided based on past experimental data and observation results. This makes it possible to utilize past question data to provide the optimal answer.

[0036] The answer generation unit can present different answer methods when resolving a question, allowing students to select the method that is easiest to understand. For example, in a mathematics class, the generation AI presents different answer methods to a question such as "I don't know how to find the area of ​​a triangle," allowing students to select the method that is easiest to understand. For example, it presents a solution using a formula or a solution using a diagram. In a history class, the answer generation unit presents different answer methods to a question such as "What caused World War II?" allowing students to select the method that is easiest to understand. For example, it explains economic factors and political factors, respectively. In a science class, the answer generation unit presents different answer methods to a question such as "What is the principle of light refraction?" allowing students to select the method that is easiest to understand. For example, it presents a solution using experimental data and a theoretical solution. In this way, different answer methods can be presented, allowing students to select the method that is easiest to understand.

[0037] The answer generation unit can automatically generate videos or animations to resolve questions and provide visual answers. For example, in a mathematics class, when a generation AI asks, "I don't know how to find the area of ​​a triangle," the answer generation unit automatically generates related videos or animations to provide a visual answer. For example, it provides an animation showing the steps to find the area of ​​a triangle. In a history class, the answer generation unit automatically generates related videos or animations to provide a visual answer. For example, it displays an animated timeline or map showing the progress of the war. In a science class, the answer generation unit automatically generates related videos or animations to provide a visual answer. For example, it provides an animation or simulation showing the refraction of light. This allows for providing a visual answer and promoting students' understanding.

[0038] The feedback unit can provide individualized feedback based on the student's answer history. For example, the generation AI analyzes the student's answer history and provides individualized feedback. For example, based on the history of questions answered by a student in a math class, the feedback unit points out mistakes or areas of insufficient understanding and explains them again. The feedback unit also provides an individualized learning plan based on the student's answer history. For example, in a history class, the feedback unit provides additional materials or assignments for topics that the student does not understand. The generation AI also provides individualized feedback in real time based on the student's answer history. For example, in a science class, the feedback unit provides instant feedback on questions answered by a student to deepen their understanding. This makes it possible to provide individualized feedback based on the student's answer history and improve learning effectiveness.

[0039] The feedback unit can analyze student responses in real time and adjust the progress of the lesson. For example, in a mathematics class, if it is determined that a student does not understand, the unit slows down the progress of the lesson and provides another explanation. The feedback unit also adjusts the progress of the lesson based on the student responses to promote understanding. For example, in a history class, the unit adjusts the progress of the lesson to provide a more detailed explanation on a topic that the student is interested in. The feedback unit can analyze student responses in real time and adjust the progress of the lesson. For example, in a science class, if it is determined that a student does not understand, the unit slows down the progress of the lesson and provides another explanation. This makes it possible to adjust the progress of the lesson based on the student responses and promote understanding.

[0040] The feedback section can add a function to promote group discussions or collaborative work. For example, the generative AI randomly divides students into groups and provides discussion topics to promote group discussions. For example, in a history class, a group discussion might be held on the "causes of World War II." To promote collaborative work, the generative AI also provides students with tasks to work on together. For example, in a science class, groups might conduct an "experiment on the refraction of light" and share the results. The generative AI also monitors group discussions and collaborative work in real time and provides feedback as needed. For example, in a mathematics class, the generative AI supports the progress of group discussions to deepen understanding. This can promote group discussions and collaborative work and support collaborative learning.

[0041] The feedback unit can incorporate game elements into lessons to increase student participation. For example, the generation AI can incorporate game elements into lessons to increase student participation. For example, a quiz-style game can be played in a math class, with points awarded according to the number of correct answers. The feedback unit can also promote student understanding through lessons that incorporate game elements. For example, a "history quiz competition" can be held in a history class, with prizes awarded according to the number of correct answers. The feedback unit can also monitor lessons in which the generation AI has incorporated game elements in real time, and provide feedback as needed. For example, an "experiment quiz" can be held in a science class, with points awarded according to the number of correct answers. In this way, incorporating game elements can increase student participation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The educational support system can further include a learning style analysis unit that analyzes students' learning styles. The learning style analysis unit analyzes students' learning history and response patterns to identify learning styles such as visual, auditory, or experiential. For example, visual students can be provided with learning materials that make extensive use of diagrams and graphs, while auditory students can be provided with enhanced audio commentary. Furthermore, experiential students can be provided with more experiments and simulations. This makes it possible to provide optimal learning materials tailored to each student's learning style and improve learning effectiveness.

[0044] The educational support system can also be equipped with a progress monitoring unit that monitors students' learning progress in real time. The progress monitoring unit monitors students' learning status in real time and issues an alert if progress is falling behind. For example, it can suggest additional learning materials or supplementary lessons for students who have not yet made progress in a particular unit. It can also provide more advanced content and applied problems to students who are progressing quickly. This makes it possible to provide appropriate support according to students' learning progress and maximize learning effectiveness.

[0045] The educational support system can further include a study plan suggestion unit that proposes future study plans based on the student's study history. The study plan suggestion unit analyzes the student's past study history and grades to propose the optimal study plan. For example, for a student who has difficulty with a particular subject, the unit can propose a plan to focus on that unit. For strong subjects, the unit can propose a plan to study more in-depth content. This allows the system to provide the optimal study plan for each student, improving learning effectiveness.

[0046] The educational support system can further include a goal setting unit that sets individual learning goals based on the student's learning history. The goal setting unit analyzes the student's past grades and learning history to set achievable learning goals. For example, it can set a goal of achieving a certain grade in a specific subject and provide a specific learning plan to achieve that. It can also monitor progress toward goal achievement in real time and adjust goals as necessary. This allows the system to set optimal learning goals for each student and improve learning effectiveness.

[0047] The education support system can further include a progress visualization unit that visualizes the student's learning progress based on their learning history. The progress visualization unit displays the student's learning history and grades in graphs and charts, allowing the student to check their learning progress at a glance. For example, it displays graphs showing the progress of grades in a specific subject and changes in study time. It also suggests what content to study next based on the student's progress. This makes it easier for students to grasp their learning progress and increases their motivation to learn.

[0048] The educational support system can further include a progress prediction unit that predicts the student's learning progress based on their learning history. The progress prediction unit analyzes the student's past learning history and grades to predict their future learning progress. For example, it predicts the likelihood of grade improvement in a specific subject or an increase or decrease in study time. It also proposes appropriate study plans and supplementary lessons based on the prediction results. This makes it possible to predict the student's learning progress and provide appropriate support.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The question analyzer analyzes questions from teachers or students. For example, the question analyzer can analyze text questions using natural language processing technology and voice questions using speech recognition technology. It can also classify questions related to specific academic fields. Step 2: The answer generation unit generates an answer to the question analyzed by the question analysis unit. For example, the generation AI can be used to generate a text answer, an audio answer, or an answer including visual materials. Step 3: The feedback unit provides the answer generated by the answer generation unit. For example, the feedback unit may provide answers in real time, or may provide periodic or individual feedback. Step 4: The data storage unit stores the questions and answers. For example, a database can be used to store the questions and answers, and a storage period can be set. The stored data can also be analyzed to improve the accuracy of the generation AI.

[0051] (Example 2) The education support system according to an embodiment of the present invention is a system that reduces teachers' excessive workload and improves the quality of education for students. In this system, a generative AI functions as a second teacher during class, delving deeper into the content of the class and resolving any questions. This reduces the burden on teachers and creates more interactive time between teachers and students.

[0052] An education support system according to an embodiment includes a question analysis unit, an answer generation unit, a feedback unit, and a data accumulation unit. The question analysis unit analyzes questions from teachers or students. For example, the question analysis unit analyzes text questions using natural language processing technology. The question analysis unit can also analyze audio questions using speech recognition technology. The question analysis unit can also classify questions related to a specific academic field. For example, the question analysis unit analyzes text questions to understand the intent of the question. Audio questions are converted into text using speech recognition technology and then analyzed. Questions related to a specific academic field are automatically classified based on the content of the question. The answer generation unit generates answers to the questions analyzed by the question analysis unit. For example, the answer generation unit generates text answers using a generation AI. The answer generation unit can also generate audio answers. The answer generation unit can also generate answers including visual materials. For example, the generation AI has learned a large amount of data and has advanced natural language processing capabilities. The generation AI generates appropriate answers based on the content of the question. The voice-format answer generates a text-format answer using speech synthesis technology. The answer including visual materials generates an answer including a chart or illustration. The feedback unit provides the answer generated by the answer generation unit. For example, the feedback unit provides answers in real time. The feedback unit can also provide periodic feedback. The feedback unit can also provide individual feedback. For example, real-time feedback provides immediate answers during class. Periodic feedback provides feedback collectively after class. Individual feedback provides feedback customized according to the student's level of understanding. The data accumulation unit accumulates questions and answers. For example, the data accumulation unit stores questions and answers using a database. The data accumulation unit can also set a data retention period. The data accumulation unit can also analyze the accumulated data to improve the accuracy of the generation AI. For example, the database efficiently stores a large number of questions and answers. The data retention period is set to automatically delete old data after a certain period.The accumulated data is used as learning data for the generative AI, contributing to improved accuracy. As a result, the educational support system according to the embodiment can provide prompt and appropriate answers to questions from teachers and students, improving the quality of education. For example, the generative AI can provide instant answers during class, making it easier for students to resolve their doubts and deepen their understanding. This also allows teachers to concentrate on the progress of the class, improving the quality of lessons. Furthermore, as question data from schools across the country is accumulated, the accuracy and quality of the generative AI can be improved, enabling more effective educational support.

[0053] The question analysis unit can automatically add historical background and scientific evidence related to the question and provide a detailed explanation. For example, in a history class, when a generative AI asks, "What caused World War II?", the question analysis unit automatically adds relevant historical background and scientific evidence and provides a detailed explanation. For example, it generates an answer that includes the economic factors and political background of the war. In a science class, when a generative AI asks, "What is the principle of light refraction?", the question analysis unit automatically adds relevant scientific evidence and experimental data and provides a detailed explanation. For example, it generates an answer that includes information on the refractive index and wavelength of light. In a literature class, when a generative AI asks, "What are the characteristics of Shakespeare's works?", the question analysis unit automatically adds relevant historical background and literary analysis and provides a detailed explanation. For example, it generates an answer that includes information on Shakespeare's historical background and the themes of his works. This allows for detailed explanations to be provided to deepen students' understanding.

[0054] The question analysis unit can evaluate students' understanding in real time and provide additional information and supplemental explanations based on their level of understanding. For example, the question analysis unit uses a generative AI to evaluate students' understanding in real time, and for students with low levels of understanding, it re-explains basic concepts and provides concrete examples. For example, in a mathematics class, a student who does not understand how to calculate the area of ​​a triangle is provided with basic formulas and specific calculation examples. The question analysis unit also provides more advanced content and applied problems to students with high levels of understanding. For example, in a physics class, a student who understands Newton's laws of motion is provided with applied problems and experimental data analysis. The question analysis unit also customizes lesson content and provides individual feedback based on the student's level of understanding. For example, in a history class, a student who does not have a deep understanding of a particular era can be provided with detailed background information about that era and additional explanations of related events. This makes it possible to provide information based on the student's level of understanding and achieve individualized learning support.

[0055] The question analysis unit can use the emotion estimation function to estimate students' interests and customize lesson content based on that. For example, the question analysis unit uses the emotion estimation function to estimate themes and topics that students are interested in and customize lesson content based on that. For example, if a student is interested in science, it can add specific examples and experiments related to science. The question analysis unit also analyzes students' emotional responses in real time and, if it determines that interest is low, it can change the lesson content to add more engaging topics. For example, if it determines that interest is low in a history class, it can introduce related movies or documentaries. The question analysis unit also provides assignments and projects related to students' fields of interest based on the emotion estimation data. For example, in a literature class, if a student is interested in a particular author, it can provide assignments based on the author's works. This allows lesson content to be customized based on students' interests and enhance their motivation to learn.

[0056] The question analysis unit can show connections with other academic fields and promote understanding from an interdisciplinary perspective. For example, in a history class, if the generative AI were to ask, "What were the effects of the Industrial Revolution?", it would show connections from the perspectives of economics and sociology, promoting interdisciplinary understanding. For example, it would explain the impact of the Industrial Revolution on the economy and changes in social structure. In a science class, if the question analysis unit were to ask, "What is the structure of DNA?", it would show connections not only from the perspectives of biology but also from the perspectives of chemistry and physics, promoting interdisciplinary understanding. For example, it would explain the chemical structure and physical properties of DNA. In a literature class, if the question analysis unit were to ask, "What are the characteristics of Shakespeare's works?", it would show connections from the perspectives of history and sociology, promoting interdisciplinary understanding. For example, it would explain Shakespeare's historical background and social impact. This promotes understanding from an interdisciplinary perspective and supports comprehensive learning.

[0057] The question analysis unit can automatically generate visual materials or simulations to facilitate visual understanding. For example, in a science class, when the generative AI asks, "What is the principle of refraction of light?", the question analysis unit automatically generates visual materials or simulations to facilitate visual understanding. For example, it provides animations and simulations showing the refraction of light. In a history class, when the question is asked, "What caused World War II?", the question analysis unit visually displays related historical events and maps to promote understanding. For example, it provides timelines and maps showing the progress of the war. In a mathematics class, when the question is asked, "I don't know how to find the area of ​​a triangle," the question analysis unit automatically generates visual diagrams and animations to facilitate visual understanding. For example, it provides an animation showing the steps to find the area of ​​a triangle. This allows the provision of visual materials and simulations to promote understanding.

[0058] The question analysis unit can use the emotion estimation function to adjust the direction of in-depth learning of lesson content based on students' emotional responses. For example, the question analysis unit uses the emotion estimation function to analyze students' emotional responses in real time, and if it determines that students are highly interested, it will delve deeper into the topic. For example, in a science class, it will provide additional detailed explanations of the procedures and theory behind an experiment that students showed interest in. Furthermore, if it determines that students are not very interested based on the students' emotional responses, it will modify the lesson content to add a more interesting topic. For example, if it determines that students are not very interested in a history class, it will introduce related movies or documentaries. Furthermore, the question analysis unit can provide assignments and projects related to students' areas of interest based on the emotion estimation data. For example, in a literature class, if a student is interested in a particular author, it will provide an assignment based on that author's works. This allows the lesson content to be adjusted based on the students' emotional responses, improving learning effectiveness.

[0059] The answer generation unit can provide examples and case studies to resolve questions and promote concrete understanding. For example, in a mathematics class, when the generation AI answers a question such as "I don't know how to find the area of ​​a triangle," the answer generation unit provides specific examples and case studies to promote understanding. For example, it presents a problem to find the area of ​​an actual triangle and explains the solution. In a history class, the answer generation unit provides related examples and case studies to promote concrete understanding. For example, it explains by citing the policies and events of a specific country. In a science class, the answer generation unit provides specific examples and case studies to promote understanding. For example, it explains based on actual experimental data and observation results. In this way, it is possible to provide specific examples and case studies to promote the resolution of questions.

[0060] The answer generation unit can refer to past question data and provide the most effective answer. For example, the generation AI in the answer generation unit refers to past question data and provides the most effective answer to a similar question. For example, in a math class, the answer generation unit provides the optimal solution based on past data for a question such as "I don't know how to find the area of ​​a triangle." In a history class, the answer generation unit refers to past question data and provides the most effective answer for a question such as "What caused World War II?" For example, if many students have had the same question in the past, the answer generation unit provides the optimal answer based on that data. In a science class, the answer generation unit refers to past question data and provides the most effective answer. For example, the answer is provided based on past experimental data and observation results. This makes it possible to utilize past question data to provide the optimal answer.

[0061] The answer generation unit can use the emotion estimation function to analyze students' emotional responses to questions and select the optimal answering method. The answer generation unit, for example, uses the emotion estimation function to analyze students' emotional responses to questions in real time and select the optimal answering method. For example, in a mathematics class, for a question such as "I don't know how to find the area of ​​a triangle," the answer generation unit adjusts the answering method based on the emotional response. The answer generation unit also selects an answering method to a question based on the student's emotional response to promote understanding. For example, in a history class, for a question such as "What were the causes of World War II?" the answer generation unit adjusts the answering method based on the emotional response. The answer generation unit also selects an optimal answering method to a student's question based on the emotion estimation data to promote understanding. For example, in a science class, for a question such as "What is the principle of light refraction?" the answer generation unit adjusts the answering method based on the emotional response. In this way, the optimal answering method can be provided based on the student's emotional response to promote understanding.

[0062] The answer generation unit can present different answer methods when resolving a question, allowing students to select the method that is easiest to understand. For example, in a mathematics class, the generation AI presents different answer methods to a question such as "I don't know how to find the area of ​​a triangle," allowing students to select the method that is easiest to understand. For example, it presents a solution using a formula or a solution using a diagram. In a history class, the answer generation unit presents different answer methods to a question such as "What caused World War II?" allowing students to select the method that is easiest to understand. For example, it explains economic factors and political factors, respectively. In a science class, the answer generation unit presents different answer methods to a question such as "What is the principle of light refraction?" allowing students to select the method that is easiest to understand. For example, it presents a solution using experimental data and a theoretical solution. In this way, different answer methods can be presented, allowing students to select the method that is easiest to understand.

[0063] The answer generation unit can automatically generate videos or animations to resolve questions and provide visual answers. For example, in a mathematics class, when a generation AI asks, "I don't know how to find the area of ​​a triangle," the answer generation unit automatically generates related videos or animations to provide a visual answer. For example, it provides an animation showing the steps to find the area of ​​a triangle. In a history class, the answer generation unit automatically generates related videos or animations to provide a visual answer. For example, it displays an animated timeline or map showing the progress of the war. In a science class, the answer generation unit automatically generates related videos or animations to provide a visual answer. For example, it provides an animation or simulation showing the refraction of light. This allows for providing a visual answer and promoting students' understanding.

[0064] The answer generation unit can use the emotion estimation function to adjust the answer approach based on the student's emotional response to the question. The answer generation unit, for example, uses the emotion estimation function to analyze the student's emotional response to the question in real time and adjust the answer approach. For example, in a mathematics class, for a question such as "I don't know how to find the area of ​​a triangle," the answer method is adjusted based on the student's emotional response. The answer generation unit also selects an answer method for the question based on the student's emotional response to promote understanding. For example, in a history class, for a question such as "What were the causes of World War II?" the answer method is adjusted based on the student's emotional response. The answer generation unit also selects the optimal answer method for the student's question based on the emotion estimation data to promote understanding. For example, in a science class, for a question such as "What is the principle of light refraction?" the answer method is adjusted based on the student's emotional response. In this way, the answer approach can be adjusted based on the student's emotional response to promote understanding.

[0065] The feedback unit can provide individualized feedback based on the student's answer history. For example, the generation AI analyzes the student's answer history and provides individualized feedback. For example, based on the history of questions answered by a student in a math class, the feedback unit points out mistakes or areas of insufficient understanding and explains them again. The feedback unit also provides an individualized learning plan based on the student's answer history. For example, in a history class, the feedback unit provides additional materials or assignments for topics that the student does not understand. The generation AI also provides individualized feedback in real time based on the student's answer history. For example, in a science class, the feedback unit provides instant feedback on questions answered by a student to deepen their understanding. This makes it possible to provide individualized feedback based on the student's answer history and improve learning effectiveness.

[0066] The feedback unit can analyze student responses in real time and adjust the progress of the lesson. For example, in a mathematics class, if it is determined that a student does not understand, the unit slows down the progress of the lesson and provides another explanation. The feedback unit also adjusts the progress of the lesson based on the student responses to promote understanding. For example, in a history class, the unit adjusts the progress of the lesson to provide a more detailed explanation on a topic that the student is interested in. The feedback unit can analyze student responses in real time and adjust the progress of the lesson. For example, in a science class, if it is determined that a student does not understand, the unit slows down the progress of the lesson and provides another explanation. This makes it possible to adjust the progress of the lesson based on the student responses and promote understanding.

[0067] The feedback unit can customize the content of interactive activities based on the emotional state of the student using the emotion estimation function. The feedback unit, for example, uses the emotion estimation function to analyze the emotional state of the student in real time and customize the content of the interactive activity. For example, if a student shows interest in a math class, it provides more difficult questions. The feedback unit also customizes the content of interactive activities based on the student's emotional state to promote understanding. For example, if a student shows interest in a history class, it provides related quizzes and discussions. The feedback unit also analyzes the student's emotional state based on the emotion estimation data and customizes the content of interactive activities. For example, if a student shows interest in a science class, it provides experiments and simulations. In this way, the content of interactive activities can be customized based on the student's emotional state to increase their motivation to learn.

[0068] The feedback section can add a function to promote group discussions or collaborative work. For example, the generative AI randomly divides students into groups and provides discussion topics to promote group discussions. For example, in a history class, a group discussion might be held on the "causes of World War II." To promote collaborative work, the generative AI also provides students with tasks to work on together. For example, in a science class, groups might conduct an "experiment on the refraction of light" and share the results. The generative AI also monitors group discussions and collaborative work in real time and provides feedback as needed. For example, in a mathematics class, the generative AI supports the progress of group discussions to deepen understanding. This can promote group discussions and collaborative work and support collaborative learning.

[0069] The feedback unit can incorporate game elements into lessons to increase student participation. For example, the generation AI can incorporate game elements into lessons to increase student participation. For example, a quiz-style game can be played in a math class, with points awarded according to the number of correct answers. The feedback unit can also promote student understanding through lessons that incorporate game elements. For example, a "history quiz competition" can be held in a history class, with prizes awarded according to the number of correct answers. The feedback unit can also monitor lessons in which the generation AI has incorporated game elements in real time, and provide feedback as needed. For example, an "experiment quiz" can be held in a science class, with points awarded according to the number of correct answers. In this way, incorporating game elements can increase student participation.

[0070] The feedback unit can adjust the progress of interactive activities based on students' emotional responses using the emotion estimation function. The feedback unit, for example, uses the emotion estimation function to analyze students' emotional responses in real time and adjust the progress of interactive activities. For example, if a student shows interest in a math class, more difficult questions are provided. The feedback unit also adjusts the progress of interactive activities based on the students' emotional responses to promote understanding. For example, if a student shows interest in a history class, related quizzes and discussions are provided. The feedback unit also analyzes students' emotional responses based on the emotion estimation data and adjusts the progress of interactive activities. For example, if a student shows interest in a science class, experiments and simulations are provided. In this way, the progress of interactive activities can be adjusted based on students' emotional responses, thereby improving learning effectiveness.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The educational support system can further include a learning style analysis unit that analyzes students' learning styles. The learning style analysis unit analyzes students' learning history and response patterns to identify learning styles such as visual, auditory, or experiential. For example, visual students can be provided with learning materials that make extensive use of diagrams and graphs, while auditory students can be provided with enhanced audio commentary. Furthermore, experiential students can be provided with more experiments and simulations. This makes it possible to provide optimal learning materials tailored to each student's learning style and improve learning effectiveness.

[0073] The educational support system can also be equipped with a progress monitoring unit that monitors students' learning progress in real time. The progress monitoring unit monitors students' learning status in real time and issues an alert if progress is falling behind. For example, it can suggest additional learning materials or supplementary lessons for students who have not yet made progress in a particular unit. It can also provide more advanced content and applied problems to students who are progressing quickly. This makes it possible to provide appropriate support according to students' learning progress and maximize learning effectiveness.

[0074] The educational support system can further include a study plan suggestion unit that proposes future study plans based on the student's study history. The study plan suggestion unit analyzes the student's past study history and grades to propose the optimal study plan. For example, for a student who has difficulty with a particular subject, the unit can propose a plan to focus on that unit. For strong subjects, the unit can propose a plan to study more in-depth content. This allows the system to provide the optimal study plan for each student, improving learning effectiveness.

[0075] The educational support system can further include a motivation management unit to increase students' motivation to learn. The motivation management unit monitors students' motivation to learn in real time and takes appropriate measures if it is declining. For example, it can provide encouraging messages and rewards to students whose motivation to learn is declining. It can also provide tasks that encourage further challenges to students who are highly motivated to learn. This can maintain students' motivation to learn and improve learning outcomes.

[0076] The education support system can also use a student emotion estimation function to monitor stress levels during class. For example, if a student is feeling stressed, the system can temporarily slow down the pace of the class or insert relaxation activities. If the student's stress level is low, the system can also provide more difficult assignments. This allows the system to adjust the class progress according to the student's stress level and improve learning outcomes.

[0077] The educational support system can further include a goal setting unit that sets individual learning goals based on the student's learning history. The goal setting unit analyzes the student's past grades and learning history to set achievable learning goals. For example, it can set a goal of achieving a certain grade in a specific subject and provide a specific learning plan to achieve that. It can also monitor progress toward goal achievement in real time and adjust goals as necessary. This allows the system to set optimal learning goals for each student and improve learning effectiveness.

[0078] The educational support system can also use a student emotion estimation function to monitor the student's level of concentration during class. For example, if the student is concentrating, the system can continue the lesson as is, but if the student's concentration lapses, it can provide a break or add interactive activities. It can also provide more difficult assignments to highly focused students. This allows the system to adjust the lesson progress according to the student's level of concentration and improve learning effectiveness.

[0079] The education support system can further include a progress visualization unit that visualizes the student's learning progress based on their learning history. The progress visualization unit displays the student's learning history and grades in graphs and charts, allowing the student to check their learning progress at a glance. For example, it displays graphs showing the progress of grades in a specific subject and changes in study time. It also suggests what content to study next based on the student's progress. This makes it easier for students to grasp their learning progress and increases their motivation to learn.

[0080] The educational support system can also use a student emotion estimation function to monitor emotional changes during class and provide appropriate feedback. For example, if a student feels anxious, the system can provide encouraging messages or activities to help them relax. If the student's emotions remain stable, the system can continue the class as usual. This provides appropriate feedback in response to the student's emotional changes, improving learning effectiveness.

[0081] The educational support system can further include a progress prediction unit that predicts the student's learning progress based on their learning history. The progress prediction unit analyzes the student's past learning history and grades to predict their future learning progress. For example, it predicts the likelihood of grade improvement in a specific subject or an increase or decrease in study time. It also proposes appropriate study plans and supplementary lessons based on the prediction results. This makes it possible to predict the student's learning progress and provide appropriate support.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The question analyzer analyzes questions from teachers or students. For example, the question analyzer can analyze text questions using natural language processing technology and voice questions using speech recognition technology. It can also classify questions related to specific academic fields. Step 2: The answer generation unit generates an answer to the question analyzed by the question analysis unit. For example, the generation AI can be used to generate a text answer, an audio answer, or an answer including visual materials. Step 3: The feedback unit provides the answer generated by the answer generation unit. For example, the feedback unit may provide answers in real time, or may provide periodic or individual feedback. Step 4: The data storage unit stores the questions and answers. For example, a database can be used to store the questions and answers, and a storage period can be set. The stored data can also be analyzed to improve the accuracy of the generation AI.

[0084] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 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 the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question analysis unit that analyzes questions from teachers or students; an answer generation unit that generates an answer to the question analyzed by the question analysis unit; a feedback unit that provides the answer generated by the answer generation unit; a data storage unit that stores the questions and the answers; A system characterized by:

2. The question analysis unit Automatically add historical context and scientific evidence relevant to the question and provide detailed explanations 2. The system of claim 1.

3. The question analysis unit Demonstrate connections with other academic fields and promote understanding from an interdisciplinary perspective 2. The system of claim 1.

4. The answer generation unit When clarifying questions, we provide examples and case studies to facilitate concrete understanding.

2. The system of claim 1.

5. The feedback unit Provide individualized feedback based on the student's response history 2. The system of claim 1.

6. The question analysis unit Inferring the student's interests and customizing lesson content accordingly 2. The system of claim 1.

7. The answer generation unit Analyze the student's emotional response to the question and choose the most appropriate answer method 2. The system of claim 1.

8. The feedback unit Customize interactive activities based on the student's emotional state.

2. The system of claim 1.

Images