System

Abstract

An object of the system according to the embodiment is to provide a high-quality learning environment even in a rural area or a specific area.SOLUTION: A system according to an embodiment includes a AI lecturer, an image generation unit, and an interaction unit. The AI instructor is knowledgeable and capable of interactive communication. The image generation unit provides a cartoon or a moving image generated by the AI instructor. The dialogue unit performs a dialogue between the AI instructor and the student.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] With conventional technology, it was difficult to secure high-quality teaching personnel in rural areas and specific areas, and there were challenges in providing a stable learning environment.

[0005] The system according to the embodiment aims to provide a high-quality learning environment even in rural areas or specific regions. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI instructor, an image generation unit, and a dialogue unit. The AI ​​instructor has a wide range of knowledge and is capable of dialogue communication. The image generation unit provides comics and videos generated by the AI ​​instructor. The dialogue unit conducts dialogue between the AI ​​instructor and students. [Effects of the Invention]

[0007] The system according to the embodiment can provide a high-quality learning environment even in rural areas or specific regions. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 online class system according to the embodiment of the present invention is a system that provides the highest level of learning environment through AI instructors who have a wide range of knowledge and are capable of interactive communication. As a result, the online class system can solve the problem of securing human resources for private tutoring schools and provide students with a high-quality learning environment.

[0029] The online teaching system according to the embodiment includes an AI instructor, an image generation unit, and a dialogue unit. The AI ​​instructor has a wide range of knowledge and is capable of dialogue-based communication. For example, the AI ​​instructor has specialized knowledge in subjects such as mathematics, science, and history, and can accurately answer students' questions. The AI ​​instructor can provide individualized instruction based on each student's learning progress and level of understanding. Furthermore, the AI ​​instructor can automatically generate optimal teaching materials based on each student's learning situation. For example, the AI ​​instructor analyzes a student's level of understanding and automatically generates necessary supplementary materials and practice problems. The image generation unit provides comics and videos generated by the AI ​​instructor. For example, the image generation unit can explain historical events using comics and videos. The image generation unit can also enable the AI ​​instructor to analyze a student's learning style (visual, auditory, tactile) and provide customized lessons accordingly. The dialogue unit engages in dialogue between the AI ​​instructor and the student. For example, the dialogue unit can identify a student's difficulties in real time and provide appropriate explanations. In addition, the dialogue unit allows the AI ​​instructor to analyze the student's learning history and propose optimal teaching methods based on the student's past learning patterns. As a result, the online class system according to the embodiment can provide a high-quality learning environment with the AI ​​instructor and deepen students' understanding.

[0030] AI tutors can accurately answer students' questions. For example, AI tutors can analyze students' past learning data and identify their learning patterns. For example, they can understand which subjects and topics students tend to struggle with, and use that information to suggest optimal teaching methods. AI tutors can also create individual learning plans based on students' learning history. For example, they can identify topics that students should focus on, taking into account their past grades and level of understanding, and support efficient learning. Furthermore, AI tutors can analyze students' learning history in real time and dynamically adjust their teaching methods according to their learning progress. For example, they can provide additional explanations or practice questions for topics that students are not fully understanding. This can quickly resolve students' questions and improve their learning effectiveness.

[0031] The image generation unit can explain historical events using cartoons or videos. For example, the image generation unit allows an AI instructor to analyze a student's learning style and provide lessons that make extensive use of diagrams and graphs for students who prefer visual learning. For example, visual explanations are provided in mathematics classes. For students who prefer auditory learning, lessons are provided in audio commentary or podcast format. For example, audio commentary is used to explain important events in history classes. Furthermore, lessons that incorporate interactive teaching materials and experiments are provided for students who prefer tactile learning. For example, experiment kits are used in science classes to advance learning. This makes it easier for students to understand historical events visually.

[0032] The dialogue unit can identify in real time the areas where students are struggling and provide appropriate explanations. For example, the dialogue unit uses emotion estimation functionality to monitor students' emotions in real time while they study and provide appropriate breaks or encouragement when they feel stressed or fatigued. For example, the AI ​​instructor analyzes students' facial expressions and voice to detect signs of stress or fatigue. For example, it uses facial recognition technology to monitor students' facial expressions and suggest taking a break if they feel stressed. The emotion estimation function also displays encouraging messages when students feel fatigued. For example, it provides messages such as "Let's take a short break" or "You're doing great." Furthermore, the AI ​​instructor adjusts the progress of learning based on the student's emotional data. For example, if stress is increasing, it may provide questions with lower difficulty levels. This can deepen students' understanding and improve learning efficiency.

[0033] An AI instructor can analyze a student's level of comprehension and automatically generate necessary supplementary materials and practice questions. For example, an AI instructor can provide lessons in different languages, creating a multilingual learning environment. For example, an AI instructor can provide lessons in multiple languages, such as English, Japanese, and Spanish. For example, lessons can be taught in English for students whose native language is English, and in Japanese for students whose native language is Japanese. A multilingual AI instructor can also customize lessons according to a student's language preferences. For example, lessons can be taught in French for students who want to learn French. Furthermore, an AI instructor can automatically translate lessons in different languages ​​and provide them in real time. For example, a lesson taught in English can be translated and provided in Japanese. This can provide appropriate materials according to a student's level of comprehension, improving learning effectiveness.

[0034] AI tutors can analyze students' learning data and display their progress and level of understanding in graphs and charts. For example, AI tutors may use culturally appropriate examples and metaphors when teaching students from different cultural backgrounds. For example, AI tutors may take students' cultural backgrounds into consideration and use culturally appropriate examples and metaphors when teaching. For example, AI tutors may use examples related to American history and culture for American students. AI tutors may also provide culturally sensitive lessons for students from different cultural backgrounds. For example, AI tutors may include content that takes religion and traditions into consideration. Furthermore, AI tutors may understand students' cultural backgrounds and select culturally appropriate teaching materials. For example, AI tutors may use materials related to Asian history and culture for Asian students. This makes it easier to visually grasp students' learning status.

[0035] AI tutors can analyze students' learning history and suggest optimal teaching methods based on their past learning patterns. For example, AI tutors can analyze students' past learning data to identify their learning patterns. For example, they can determine which subjects or topics students tend to struggle with and suggest optimal teaching methods based on that information. AI tutors can also create individual learning plans based on students' learning history. For example, they can identify topics that students should focus on, taking into account their past grades and level of understanding, to support efficient learning. Furthermore, AI tutors can analyze students' learning history in real time and dynamically adjust teaching methods according to their learning progress. For example, they can provide additional explanations or practice questions for topics that students are not fully understanding. This makes it possible to provide optimal teaching methods based on students' learning history and improve learning effectiveness.

[0036] AI instructors can analyze students' learning styles and provide customized lessons accordingly. For example, an AI instructor could analyze a student's learning style and provide lessons that make extensive use of diagrams and graphs to students who prefer visual learning. For example, visual explanations could be provided in a math class. For students who prefer auditory learning, lessons could be provided in the form of audio commentary or podcasts. For example, audio commentary could be used to explain important events in a history class. Furthermore, for students who prefer tactile learning, lessons could be provided that incorporate interactive teaching materials and experiments. For example, experiment kits could be used to advance learning in a science class. This makes it possible to provide optimal lessons tailored to each student's learning style and improve learning effectiveness.

[0037] AI instructors can provide lessons in different languages, creating a multilingual learning environment. AI instructors provide lessons in multiple languages, such as English, Japanese, and Spanish. For example, they can provide lessons in English to students whose native language is English, and in Japanese to students whose native language is Japanese. Multilingual AI instructors can also customize lessons according to students' language preferences. For example, they can provide lessons in French to students who want to learn French. Furthermore, AI instructors can automatically translate lessons in different languages ​​and provide them in real time. For example, a lesson taught in English can be translated and provided in Japanese. This multilingual support makes it possible to accommodate students who speak different languages.

[0038] AI instructors can assess students' level of understanding in real time and focus on areas where they are lacking in understanding. For example, AI instructors can analyze students' answer data in real time to assess their level of understanding. For example, they can identify areas where students' understanding is lacking based on test and quiz results. In addition, AI instructors can provide additional teaching materials and explanatory videos to focus on areas where students are lacking in understanding. For example, if a student is struggling with a math problem, a detailed explanatory video will be displayed. Furthermore, they can assess students' level of understanding in real time and adjust the lesson content as needed. For example, they can provide additional explanation for topics where students are not fully understood. This can provide appropriate explanations according to the student's level of understanding, improving learning effectiveness.

[0039] An AI tutor can automatically generate questions with adjusted difficulty according to a student's learning progress, providing an appropriate challenge. For example, an AI tutor can analyze a student's learning progress and automatically generate questions with adjusted difficulty. For example, it can provide more difficult questions to students with a high level of understanding. In addition, the difficulty of questions can be dynamically adjusted according to the student's learning progress to provide an appropriate challenge. For example, it can provide basic questions to students with a low level of understanding. Furthermore, a system can be built that evaluates a student's learning progress in real time and automatically generates questions with adjusted difficulty. For example, the difficulty of questions can be changed according to the student's learning progress. This makes it possible to provide appropriate questions according to the student's learning progress, improving learning effectiveness.

[0040] AI instructors can select the optimal lesson format depending on the student's learning environment. For example, AI instructors can analyze the student's device and internet connection status to select the optimal lesson format. For example, if the student has a slow internet connection, they can provide lightweight teaching materials. AI instructors can also dynamically adjust the lesson format depending on the learning environment. For example, they can provide mobile-friendly lessons to students who use smartphones. Furthermore, we will build a system that evaluates the student's learning environment in real time and selects the optimal lesson format. For example, they can adjust the layout of teaching materials depending on the device's screen size. This will allow us to provide the optimal lesson format depending on the student's learning environment, improving learning effectiveness.

[0041] AI instructors can automatically incorporate reviews linked to past learning content based on students' learning history. For example, an AI instructor can analyze a student's learning history and automatically incorporate reviews linked to past learning content. For example, it can provide questions to review what was learned in the previous lesson. Furthermore, based on the learning history, the AI ​​instructor will review at an appropriate time. For example, it can review past content after a certain period of time has passed. Furthermore, a system can be built that evaluates students' learning history in real time and automatically incorporates reviews. For example, it can focus on reviewing areas where understanding is insufficient. This can provide appropriate reviews based on the student's learning history, improving learning effectiveness.

[0042] An AI tutor can analyze a student's learning data and automatically generate an individual learning plan. For example, an AI tutor can analyze a student's learning data and automatically generate an individual learning plan. For example, it can provide a learning plan customized based on the student's level of understanding and progress. Furthermore, based on the learning data, the AI ​​tutor can create the optimal learning plan for each student. For example, it can adjust the learning content based on the student's strong and weak subjects. Furthermore, a system can be built that analyzes students' learning data in real time and dynamically generates individual learning plans. For example, it can update the plan based on the student's learning progress. This makes it possible to provide an individual learning plan based on the student's learning data, improving learning effectiveness.

[0043] AI tutors can create short-, medium-, and long-term study plans and manage progress based on students' learning goals. For example, AI tutors can create short-, medium-, and long-term study plans based on students' learning goals. For example, they can create study schedules that align with exam dates. AI tutors can also manage progress and adjust plans as needed based on learning goals. For example, they can monitor progress toward goal achievement in real time. Furthermore, AI tutors can provide step-by-step study plans to help students achieve their learning goals. For example, they can create plans that cover steps from basic to advanced. This allows for plans that meet students' learning goals and improve learning effectiveness.

[0044] AI tutors can suggest group learning or pair work based on students' learning styles, promoting collaborative learning. For example, AI tutors can analyze students' learning styles and suggest group learning or pair work. For example, they can recommend group discussions for students who find collaborative learning effective. Furthermore, AI tutors can suggest collaborative learning formats based on students' learning styles. For example, they can recommend pair problem solving for students who find pair work effective. Furthermore, AI tutors can provide collaborative learning opportunities based on students' learning styles. For example, they can suggest online group projects. This allows for collaborative learning that suits students' learning styles, improving learning effectiveness.

[0045] An AI tutor can evaluate a student's compatibility with other students based on their learning history and recommend the most suitable study partner. For example, an AI tutor can analyze a student's learning history and recommend a compatible study partner. For example, it can pair students who share the same learning pace or topic interests. The AI ​​tutor can also evaluate the most suitable study partner based on the student's learning history. For example, it can determine compatibility based on the results of past collaborative learning. Furthermore, a system can be built that analyzes a student's learning history in real time and dynamically recommends the most suitable study partner. For example, it can change partners depending on the student's learning progress. This can provide the most suitable study partner based on the student's learning history and improve learning effectiveness.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The online class system can also be equipped with a health management unit that monitors students' health. For example, the health management unit can monitor students' heart rate and sleep patterns and suggest a study schedule based on their health. For example, if their heart rate is high, it can suggest a break to relax, and if it detects a lack of sleep, it can shorten study time. The health management unit can also record students' diet and exercise habits to support a balanced lifestyle. For example, it can provide nutritional advice based on dietary records and suggest light exercise if it detects a lack of exercise. This can provide a learning environment that takes students' health into consideration and improve learning effectiveness.

[0048] The online teaching system can further include a creativity promotion module to draw out students' creativity. For example, the creativity promotion module may suggest creative tasks or projects to students. For example, it may provide creative tasks in fields such as art, music, or programming. The creativity promotion module may also evaluate students' ideas and provide feedback. For example, it may suggest specific improvements or new ideas for work submitted by students. Furthermore, the creativity promotion module may encourage collaboration between students and provide opportunities for them to work on projects together. For example, it may suggest group art projects or collaborative programming tasks. This can draw out students' creativity and increase their motivation to learn.

[0049] An online teaching system can also have a social development department to foster students' social skills. For example, the social development department can provide opportunities for students to hold discussions and debates. For example, it can hold discussions on current affairs and social issues to encourage the exchange of opinions. The social development department can also provide training to improve students' communication skills. For example, it can hold workshops to improve presentation skills and leadership skills. Furthermore, the social development department can promote interaction between students and provide opportunities to build friendships. For example, it can suggest online social events and group activities. This can foster students' social skills and enrich the learning environment.

[0050] The online class system can also be equipped with a career support department that supports students' career development. For example, the career support department provides students with work experience and internship opportunities. For example, it may collaborate with companies and organizations to implement work experience programs. The career support department may also propose career plans based on students' interests and aptitudes. For example, it may suggest optimal career paths through aptitude tests and counseling. Furthermore, the career support department may support students in their job hunting activities, helping them write resumes and practice interviews. For example, it may conduct mock interviews, correct resumes, and provide practical advice. This can support students' career development and help them achieve their future goals.

[0051] The online class system can further include a progress visualization unit that analyzes students' learning data and visualizes their learning progress. For example, the progress visualization unit displays students' learning data in graphs and charts, allowing them to visually grasp their learning progress. For example, it displays their learning progress in a line graph and their level of understanding in a pie chart. The progress visualization unit also evaluates their learning progress based on their learning data. For example, it displays the degree of goal achievement as a percentage and displays the progress status in different colors. Furthermore, the progress visualization unit analyzes students' learning data in real time and dynamically updates their progress status. For example, it immediately reflects test results and updates their progress status. This makes it easier to visually grasp students' learning progress, improving learning effectiveness.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The AI ​​teacher has a wide range of knowledge and is capable of interactive communication. For example, the AI ​​teacher has specialized knowledge in subjects such as mathematics, science, and history, and can accurately answer students' questions. The AI ​​teacher can also provide individual instruction based on each student's learning progress and level of understanding. Furthermore, the AI ​​teacher can automatically generate optimal learning materials based on the student's learning situation. For example, the AI ​​teacher can analyze the student's level of understanding and automatically generate necessary supplementary materials and practice questions. Step 2: The image generator provides comics and videos generated by the AI ​​instructor. For example, the image generator can explain historical events through comics and videos. The image generator can also help the AI ​​instructor analyze the student's learning style (visual, auditory, tactile) and provide customized lessons accordingly. Step 3: The dialogue unit holds a dialogue between the AI ​​instructor and the student. For example, the dialogue unit can grasp in real time the points where the student is struggling and provide appropriate explanations. The dialogue unit also allows the AI ​​instructor to analyze the student's learning history and suggest optimal teaching methods based on past learning patterns.

[0054] (Example 2) The online class system according to the embodiment of the present invention is a system that provides the highest level of learning environment through AI instructors who have a wide range of knowledge and are capable of interactive communication. As a result, the online class system can solve the problem of securing human resources for private tutoring schools and provide students with a high-quality learning environment.

[0055] The online teaching system according to the embodiment includes an AI instructor, an image generation unit, and a dialogue unit. The AI ​​instructor has a wide range of knowledge and is capable of dialogue-based communication. For example, the AI ​​instructor has specialized knowledge in subjects such as mathematics, science, and history, and can accurately answer students' questions. The AI ​​instructor can provide individualized instruction based on each student's learning progress and level of understanding. Furthermore, the AI ​​instructor can automatically generate optimal teaching materials based on each student's learning situation. For example, the AI ​​instructor analyzes a student's level of understanding and automatically generates necessary supplementary materials and practice problems. The image generation unit provides comics and videos generated by the AI ​​instructor. For example, the image generation unit can explain historical events using comics and videos. The image generation unit can also enable the AI ​​instructor to analyze a student's learning style (visual, auditory, tactile) and provide customized lessons accordingly. The dialogue unit engages in dialogue between the AI ​​instructor and the student. For example, the dialogue unit can identify a student's difficulties in real time and provide appropriate explanations. In addition, the dialogue unit allows the AI ​​instructor to analyze the student's learning history and propose optimal teaching methods based on the student's past learning patterns. As a result, the online class system according to the embodiment can provide a high-quality learning environment with the AI ​​instructor and deepen students' understanding.

[0056] AI tutors can accurately answer students' questions. For example, AI tutors can analyze students' past learning data and identify their learning patterns. For example, they can understand which subjects and topics students tend to struggle with, and use that information to suggest optimal teaching methods. AI tutors can also create individual learning plans based on students' learning history. For example, they can identify topics that students should focus on, taking into account their past grades and level of understanding, and support efficient learning. Furthermore, AI tutors can analyze students' learning history in real time and dynamically adjust their teaching methods according to their learning progress. For example, they can provide additional explanations or practice questions for topics that students are not fully understanding. This can quickly resolve students' questions and improve their learning effectiveness.

[0057] The image generation unit can explain historical events using cartoons or videos. For example, the image generation unit allows an AI instructor to analyze a student's learning style and provide lessons that make extensive use of diagrams and graphs for students who prefer visual learning. For example, visual explanations are provided in mathematics classes. For students who prefer auditory learning, lessons are provided in audio commentary or podcast format. For example, audio commentary is used to explain important events in history classes. Furthermore, lessons that incorporate interactive teaching materials and experiments are provided for students who prefer tactile learning. For example, experiment kits are used in science classes to advance learning. This makes it easier for students to understand historical events visually.

[0058] The dialogue unit can identify in real time the areas where students are struggling and provide appropriate explanations. For example, the dialogue unit uses emotion estimation functionality to monitor students' emotions in real time while they study and provide appropriate breaks or encouragement when they feel stressed or fatigued. For example, the AI ​​instructor analyzes students' facial expressions and voice to detect signs of stress or fatigue. For example, it uses facial recognition technology to monitor students' facial expressions and suggest taking a break if they feel stressed. The emotion estimation function also displays encouraging messages when students feel fatigued. For example, it provides messages such as "Let's take a short break" or "You're doing great." Furthermore, the AI ​​instructor adjusts the progress of learning based on the student's emotional data. For example, if stress is increasing, it may provide questions with lower difficulty levels. This can deepen students' understanding and improve learning efficiency.

[0059] An AI instructor can analyze a student's level of comprehension and automatically generate necessary supplementary materials and practice questions. For example, an AI instructor can provide lessons in different languages, creating a multilingual learning environment. For example, an AI instructor can provide lessons in multiple languages, such as English, Japanese, and Spanish. For example, lessons can be taught in English for students whose native language is English, and in Japanese for students whose native language is Japanese. A multilingual AI instructor can also customize lessons according to a student's language preferences. For example, lessons can be taught in French for students who want to learn French. Furthermore, an AI instructor can automatically translate lessons in different languages ​​and provide them in real time. For example, a lesson taught in English can be translated and provided in Japanese. This can provide appropriate materials according to a student's level of comprehension, improving learning effectiveness.

[0060] AI tutors can analyze students' learning data and display their progress and level of understanding in graphs and charts. For example, AI tutors may use culturally appropriate examples and metaphors when teaching students from different cultural backgrounds. For example, AI tutors may take students' cultural backgrounds into consideration and use culturally appropriate examples and metaphors when teaching. For example, AI tutors may use examples related to American history and culture for American students. AI tutors may also provide culturally sensitive lessons for students from different cultural backgrounds. For example, AI tutors may include content that takes religion and traditions into consideration. Furthermore, AI tutors may understand students' cultural backgrounds and select culturally appropriate teaching materials. For example, AI tutors may use materials related to Asian history and culture for Asian students. This makes it easier to visually grasp students' learning status.

[0061] AI tutors can analyze students' learning history and suggest optimal teaching methods based on their past learning patterns. For example, AI tutors can analyze students' past learning data to identify their learning patterns. For example, they can determine which subjects or topics students tend to struggle with and suggest optimal teaching methods based on that information. AI tutors can also create individual learning plans based on students' learning history. For example, they can identify topics that students should focus on, taking into account their past grades and level of understanding, to support efficient learning. Furthermore, AI tutors can analyze students' learning history in real time and dynamically adjust teaching methods according to their learning progress. For example, they can provide additional explanations or practice questions for topics that students are not fully understanding. This makes it possible to provide optimal teaching methods based on students' learning history and improve learning effectiveness.

[0062] AI instructors can analyze students' learning styles and provide customized lessons accordingly. For example, an AI instructor could analyze a student's learning style and provide lessons that make extensive use of diagrams and graphs to students who prefer visual learning. For example, visual explanations could be provided in a math class. For students who prefer auditory learning, lessons could be provided in the form of audio commentary or podcasts. For example, audio commentary could be used to explain important events in a history class. Furthermore, for students who prefer tactile learning, lessons could be provided that incorporate interactive teaching materials and experiments. For example, experiment kits could be used to advance learning in a science class. This makes it possible to provide optimal lessons tailored to each student's learning style and improve learning effectiveness.

[0063] Using emotion estimation, AI tutors can monitor students' emotions in real time while studying and provide appropriate breaks or encouragement when they feel stressed or fatigued. For example, AI tutors can analyze students' facial expressions and voices to detect signs of stress or fatigue. For example, they can use facial recognition technology to monitor students' facial expressions and suggest taking a break if they feel stressed. They can also use emotion estimation to display encouraging messages when students feel fatigued. For example, they can provide messages such as "Let's take a short break" or "You're doing great." Furthermore, based on the student's emotional data, the AI ​​tutor can adjust the progress of their studies. For example, if stress levels are rising, they can provide easier questions. This reduces students' stress and fatigue and improves their learning efficiency.

[0064] AI instructors can provide lessons in different languages, creating a multilingual learning environment. AI instructors provide lessons in multiple languages, such as English, Japanese, and Spanish. For example, they can provide lessons in English to students whose native language is English, and in Japanese to students whose native language is Japanese. Multilingual AI instructors can also customize lessons according to students' language preferences. For example, they can provide lessons in French to students who want to learn French. Furthermore, AI instructors can automatically translate lessons in different languages ​​and provide them in real time. For example, a lesson taught in English can be translated and provided in Japanese. This multilingual support makes it possible to accommodate students who speak different languages.

[0065] Using the emotion estimation function, the AI ​​instructor can select topics that will pique students' interest and reflect them in the lesson content. For example, the AI ​​instructor can analyze students' emotional data and select topics that will pique their interest. For example, they can incorporate themes that students are likely to find interesting into their lessons. They can also use the emotion estimation function to identify topics that students are likely to find interesting and provide lessons based on those topics. For example, they can select science-related topics for students who are interested in science. Furthermore, the AI ​​instructor can dynamically adjust the lesson content based on the students' emotional responses. For example, they can provide lessons that focus on topics that students are likely to find interesting. This can pique students' interest and increase their motivation to learn.

[0066] AI instructors can assess students' level of understanding in real time and focus on areas where they are lacking in understanding. For example, AI instructors can analyze students' answer data in real time to assess their level of understanding. For example, they can identify areas where students' understanding is lacking based on test and quiz results. In addition, AI instructors can provide additional teaching materials and explanatory videos to focus on areas where students are lacking in understanding. For example, if a student is struggling with a math problem, a detailed explanatory video will be displayed. Furthermore, they can assess students' level of understanding in real time and adjust the lesson content as needed. For example, they can provide additional explanation for topics where students are not fully understood. This can provide appropriate explanations according to the student's level of understanding, improving learning effectiveness.

[0067] An AI tutor can automatically generate questions with adjusted difficulty according to a student's learning progress, providing an appropriate challenge. For example, an AI tutor can analyze a student's learning progress and automatically generate questions with adjusted difficulty. For example, it can provide more difficult questions to students with a high level of understanding. In addition, the difficulty of questions can be dynamically adjusted according to the student's learning progress to provide an appropriate challenge. For example, it can provide basic questions to students with a low level of understanding. Furthermore, a system can be built that evaluates a student's learning progress in real time and automatically generates questions with adjusted difficulty. For example, the difficulty of questions can be changed according to the student's learning progress. This makes it possible to provide appropriate questions according to the student's learning progress, improving learning effectiveness.

[0068] Using the emotion estimation function, an AI instructor can provide lessons in a format that is likely to interest students. For example, an AI instructor can analyze students' emotional data and provide lessons in a format that is likely to interest them. For example, the instructor could use manga-style teaching materials to teach. The emotion estimation function could also be used to identify a format that is likely to interest students and provide lessons based on that format. For example, the instructor could use video-style explanations to teach. Furthermore, the AI ​​instructor could provide interactive content based on students' emotional responses. For example, the instructor could hold lessons that incorporate quizzes and games. This can pique students' interest and increase their motivation to learn.

[0069] AI instructors can select the optimal lesson format depending on the student's learning environment. For example, AI instructors can analyze the student's device and internet connection status to select the optimal lesson format. For example, if the student has a slow internet connection, they can provide lightweight teaching materials. AI instructors can also dynamically adjust the lesson format depending on the learning environment. For example, they can provide mobile-friendly lessons to students who use smartphones. Furthermore, we will build a system that evaluates the student's learning environment in real time and selects the optimal lesson format. For example, they can adjust the layout of teaching materials depending on the device's screen size. This will allow us to provide the optimal lesson format depending on the student's learning environment, improving learning effectiveness.

[0070] AI instructors can automatically incorporate reviews linked to past learning content based on students' learning history. For example, an AI instructor can analyze a student's learning history and automatically incorporate reviews linked to past learning content. For example, it can provide questions to review what was learned in the previous lesson. Furthermore, based on the learning history, the AI ​​instructor will review at an appropriate time. For example, it can review past content after a certain period of time has passed. Furthermore, a system can be built that evaluates students' learning history in real time and automatically incorporates reviews. For example, it can focus on reviewing areas where understanding is insufficient. This can provide appropriate reviews based on the student's learning history, improving learning effectiveness.

[0071] An AI instructor can use the emotion estimation function to identify the time of day when students are most relaxed when studying and schedule lessons for that time. For example, the AI ​​instructor can analyze students' emotion data to identify the time of day when they are most relaxed when studying. For example, it can find the time of day when emotion scores are stable. It can also use the emotion estimation function to schedule lessons for times when students are most relaxed when studying. For example, it can hold lessons during times when stress is low. Furthermore, based on students' emotional responses, the AI ​​instructor can suggest the optimal study time. For example, it can focus on studying during times when students are relaxed. This allows lessons to be offered at times when students are most relaxed when studying, improving learning effectiveness.

[0072] An AI tutor can analyze a student's learning data and automatically generate an individual learning plan. For example, an AI tutor can analyze a student's learning data and automatically generate an individual learning plan. For example, it can provide a learning plan customized based on the student's level of understanding and progress. Furthermore, based on the learning data, the AI ​​tutor can create the optimal learning plan for each student. For example, it can adjust the learning content based on the student's strong and weak subjects. Furthermore, a system can be built that analyzes students' learning data in real time and dynamically generates individual learning plans. For example, it can update the plan based on the student's learning progress. This makes it possible to provide an individual learning plan based on the student's learning data, improving learning effectiveness.

[0073] AI tutors can create short-, medium-, and long-term study plans and manage progress based on students' learning goals. For example, AI tutors can create short-, medium-, and long-term study plans based on students' learning goals. For example, they can create study schedules that align with exam dates. AI tutors can also manage progress and adjust plans as needed based on learning goals. For example, they can monitor progress toward goal achievement in real time. Furthermore, AI tutors can provide step-by-step study plans to help students achieve their learning goals. For example, they can create plans that cover steps from basic to advanced. This allows for plans that meet students' learning goals and improve learning effectiveness.

[0074] Using the emotion estimation function, the AI ​​tutor can introduce an encouragement and reward system to maintain student motivation. For example, the AI ​​tutor can analyze students' emotional data and provide encouraging messages to maintain their motivation. For example, it can display a message such as, "You're doing great!". The emotion estimation function can also be used to introduce a reward system to increase students' motivation. For example, badges or points can be awarded when learning goals are achieved. Furthermore, based on the students' emotional responses, the AI ​​tutor can suggest actions to maintain their motivation. For example, it can suggest taking a break when they feel tired, allowing them to refocus on their studies. This can maintain students' motivation and improve their learning effectiveness.

[0075] AI tutors can suggest group learning or pair work based on students' learning styles, promoting collaborative learning. For example, AI tutors can analyze students' learning styles and suggest group learning or pair work. For example, they can recommend group discussions for students who find collaborative learning effective. Furthermore, AI tutors can suggest collaborative learning formats based on students' learning styles. For example, they can recommend pair problem solving for students who find pair work effective. Furthermore, AI tutors can provide collaborative learning opportunities based on students' learning styles. For example, they can suggest online group projects. This allows for collaborative learning that suits students' learning styles, improving learning effectiveness.

[0076] An AI tutor can evaluate a student's compatibility with other students based on their learning history and recommend the most suitable study partner. For example, an AI tutor can analyze a student's learning history and recommend a compatible study partner. For example, it can pair students who share the same learning pace or topic interests. The AI ​​tutor can also evaluate the most suitable study partner based on the student's learning history. For example, it can determine compatibility based on the results of past collaborative learning. Furthermore, a system can be built that analyzes a student's learning history in real time and dynamically recommends the most suitable study partner. For example, it can change partners depending on the student's learning progress. This can provide the most suitable study partner based on the student's learning history and improve learning effectiveness.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The online class system can also be equipped with a health management unit that monitors students' health. For example, the health management unit can monitor students' heart rate and sleep patterns and suggest a study schedule based on their health. For example, if their heart rate is high, it can suggest a break to relax, and if it detects a lack of sleep, it can shorten study time. The health management unit can also record students' diet and exercise habits to support a balanced lifestyle. For example, it can provide nutritional advice based on dietary records and suggest light exercise if it detects a lack of exercise. This can provide a learning environment that takes students' health into consideration and improve learning effectiveness.

[0079] The online teaching system can further include a creativity promotion module to draw out students' creativity. For example, the creativity promotion module may suggest creative tasks or projects to students. For example, it may provide creative tasks in fields such as art, music, or programming. The creativity promotion module may also evaluate students' ideas and provide feedback. For example, it may suggest specific improvements or new ideas for work submitted by students. Furthermore, the creativity promotion module may encourage collaboration between students and provide opportunities for them to work on projects together. For example, it may suggest group art projects or collaborative programming tasks. This can draw out students' creativity and increase their motivation to learn.

[0080] An online teaching system can also have a social development department to foster students' social skills. For example, the social development department can provide opportunities for students to hold discussions and debates. For example, it can hold discussions on current affairs and social issues to encourage the exchange of opinions. The social development department can also provide training to improve students' communication skills. For example, it can hold workshops to improve presentation skills and leadership skills. Furthermore, the social development department can promote interaction between students and provide opportunities to build friendships. For example, it can suggest online social events and group activities. This can foster students' social skills and enrich the learning environment.

[0081] The online class system can also be equipped with a career support department that supports students' career development. For example, the career support department provides students with work experience and internship opportunities. For example, it may collaborate with companies and organizations to implement work experience programs. The career support department may also propose career plans based on students' interests and aptitudes. For example, it may suggest optimal career paths through aptitude tests and counseling. Furthermore, the career support department may support students in their job hunting activities, helping them write resumes and practice interviews. For example, it may conduct mock interviews, correct resumes, and provide practical advice. This can support students' career development and help them achieve their future goals.

[0082] The online teaching system can further include an emotion adaptation unit that estimates a student's emotions and provides a learning environment based on the student's emotions. For example, the emotion adaptation unit analyzes the student's emotion data and adjusts the learning environment. For example, it provides relaxing music to a student who is feeling stressed, and creates an environment that helps a student who is losing concentration to improve their concentration. The emotion adaptation unit also provides encouragement and advice according to the student's emotions. For example, it displays encouraging messages to a student who is losing motivation, thereby motivating them. Furthermore, the emotion adaptation unit dynamically adjusts the learning content based on the student's emotion data. For example, it provides easier questions if the student is feeling stressed, and provides more challenging tasks if the student is relaxed. This allows the system to provide an optimal learning environment based on the student's emotions and improve learning effectiveness.

[0083] The online teaching system can further include an emotional content unit that estimates students' emotions and provides learning content based on their emotions. For example, the emotional content unit analyzes students' emotional data and provides content that is likely to attract their interest. For example, it provides entertaining content to students who are emotionally excited, and relaxing content to students who are relaxed. The emotional content unit also suggests learning styles according to students' emotions. For example, it provides difficult tasks to students who are highly focused, and light review questions to students who are tired. Furthermore, the emotional content unit dynamically adjusts learning progress based on students' emotional data. For example, it introduces a new topic when emotions are stable, and reviews when emotions are unstable. This makes it possible to provide optimal learning content according to students' emotions and improve learning effectiveness.

[0084] The online teaching system may further include an emotion feedback unit that estimates a student's emotions and provides feedback based on the emotions. For example, the emotion feedback unit analyzes the student's emotion data and provides appropriate feedback. For example, it provides positive feedback to a student who is emotionally excited and encouraging feedback to a student who is emotionally depressed. The emotion feedback unit also provides learning advice according to the student's emotions. For example, it provides relaxation advice to a student who is feeling stressed and advice to improve concentration to a student who is losing concentration. Furthermore, the emotion feedback unit dynamically adjusts the learning progress based on the student's emotion data. For example, it introduces a new topic when the student's emotions are stable and reviews the material when the student's emotions are unstable. This allows the provision of optimal feedback according to the student's emotions and improves learning effectiveness.

[0085] The online class system can further include an emotion scheduling unit that estimates a student's emotions and proposes a study schedule based on the emotions. For example, the emotion scheduling unit analyzes the student's emotion data and proposes an optimal study schedule. For example, intensive study is conducted during times when the student's emotions are stable, and light review is conducted during times when the student's emotions are unstable. The emotion scheduling unit also proposes break times based on the student's emotions. For example, a break to relax is suggested when stress is high, and a short break is suggested when the student's concentration is low. Furthermore, the emotion scheduling unit dynamically adjusts the progress of study based on the student's emotion data. For example, a new topic is introduced when the student's emotions are stable, and review is conducted when the student's emotions are unstable. This allows the system to provide an optimal study schedule based on the student's emotions and improve learning effectiveness.

[0086] The online teaching system can further include an emotional goal unit that estimates a student's emotions and sets learning goals based on their emotions. For example, the emotional goal unit analyzes the student's emotional data and sets optimal learning goals. For example, it sets challenging goals for a student who is emotionally excited, and sets easy-to-achieve goals for a student who is emotionally depressed. The emotional goal unit also proposes learning plans based on the student's emotions. For example, it proposes a relaxation plan for a student who is feeling stressed, and a plan to improve concentration for a student who is experiencing high levels of concentration. Furthermore, the emotional goal unit dynamically adjusts learning progress based on the student's emotional data. For example, it introduces a new topic when the student's emotions are stable, and reviews the material when the student's emotions are unstable. This makes it possible to set optimal learning goals based on the student's emotions and improve learning effectiveness.

[0087] The online class system can further include a progress visualization unit that analyzes students' learning data and visualizes their learning progress. For example, the progress visualization unit displays students' learning data in graphs and charts, allowing them to visually grasp their learning progress. For example, it displays their learning progress in a line graph and their level of understanding in a pie chart. The progress visualization unit also evaluates their learning progress based on their learning data. For example, it displays the degree of goal achievement as a percentage and displays the progress status in different colors. Furthermore, the progress visualization unit analyzes students' learning data in real time and dynamically updates their progress status. For example, it immediately reflects test results and updates their progress status. This makes it easier to visually grasp students' learning progress, improving learning effectiveness.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The AI ​​teacher has a wide range of knowledge and is capable of interactive communication. For example, the AI ​​teacher has specialized knowledge in subjects such as mathematics, science, and history, and can accurately answer students' questions. The AI ​​teacher can also provide individual instruction based on each student's learning progress and level of understanding. Furthermore, the AI ​​teacher can automatically generate optimal learning materials based on the student's learning situation. For example, the AI ​​teacher can analyze the student's level of understanding and automatically generate necessary supplementary materials and practice questions. Step 2: The image generator provides comics and videos generated by the AI ​​instructor. For example, the image generator can explain historical events through comics and videos. The image generator can also help the AI ​​instructor analyze the student's learning style (visual, auditory, tactile) and provide customized lessons accordingly. Step 3: The dialogue unit holds a dialogue between the AI ​​instructor and the student. For example, the dialogue unit can grasp in real time the points where the student is struggling and provide appropriate explanations. The dialogue unit also allows the AI ​​instructor to analyze the student's learning history and suggest optimal teaching methods based on past learning patterns.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] 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 AI 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 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. AI instructors with a wide range of knowledge and the ability to communicate interactively an image generation unit that provides comics and videos generated by the AI ​​instructor; A dialogue unit that conducts dialogue between the AI ​​instructor and students. A system characterized by:

2. The AI ​​instructor said, Answer the student's question appropriately 2. The system of claim 1.

3. The image generation unit Explaining historical events through comics and videos 2. The system of claim 1.

4. The dialogue unit Understand the points where the student is struggling in real time and provide appropriate explanations 2. The system of claim 1.

5. The AI ​​instructor said, Analyze the student's level of understanding and automatically generate necessary supplementary materials and practice questions.

2. The system of claim 1.

6. The AI ​​instructor said, Analyze the student's learning data and display their progress and understanding in graphs and charts 2. The system of claim 1.

Images