System

Abstract

An object of a system according to an embodiment is to reduce a burden on an education site and provide individualized learning.SOLUTION: A system according to an embodiment includes a class progression unit, a learning analysis unit, a voice providing unit, and a video providing unit. The lesson progress unit progresses a lesson. A learning analysis part analyzes the learning progress of the student on the basis of the lesson contents progressed by the lesson progress part. The speech providing unit provides a native-level speech based on the learning progress analyzed by the learning analysis unit. The video image providing unit provides a video image corresponding to the lesson content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has room for improvement, as it does not adequately address the shortage of staff in educational settings and provide personalized learning.

[0005] The system according to the embodiment aims to reduce the burden on educational institutions and provide personalized learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a lesson proceeding unit, a learning analysis unit, an audio providing unit, and a video providing unit. The lesson proceeding unit proceeds with the lesson. The learning analysis unit analyzes the students' learning progress based on the lesson content proceeded by the lesson proceeding unit. The audio providing unit provides native-level audio based on the learning progress analyzed by the learning analysis unit. The video providing unit provides video according to the lesson content. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on educational institutions and provide personalized learning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The education support system according to an embodiment of the present invention is a system that reduces the burden on educational institutions, alleviates labor shortages, and improves the quality of education. This system utilizes a robot equipped with generative AI to create an environment in which teachers can devote more time to "caring for children." This allows the education support system to reduce the burden on educational institutions, alleviate labor shortages, and improve the quality of education.

[0029] An educational support system according to an embodiment includes a lesson progress unit, a learning analysis unit, an audio provision unit, and a video provision unit. The lesson progress unit progresses a lesson. For example, a generation AI understands the content of the lesson and progresses the lesson using appropriate teaching materials. The lesson progress unit can also conduct a lesson based on prompts including a method for progressing the lesson provided by the generation AI. For example, the generation AI receives information necessary for progressing the lesson as prompts and progresses the lesson based on the prompts. The learning analysis unit analyzes a student's learning progress based on the lesson content progressed by the lesson progress unit. For example, the generation AI analyzes a student's answers and provides feedback according to the student's level of understanding. The learning analysis unit can also analyze a student's learning status in real time and provide an individualized learning plan. For example, the generation AI receives a student's answers and learning status as prompts and generates an individualized learning plan based on the prompts. The audio provision unit provides native-level audio based on the learning progress analyzed by the learning analysis unit. For example, the generation AI accurately reproduces English pronunciation and intonation and provides pronunciation practice for the student. The audio providing unit can also allow the generation AI to receive prompts regarding the content and method of pronunciation practice and generate audio based on the prompts. For example, the generation AI receives a prompt for pronunciation practice and generates native-level audio based on the prompts. The video providing unit can provide video according to the lesson content. For example, the generation AI generates video according to the lesson content to provide visual assistance to students. The video providing unit can also allow the generation AI to receive prompts regarding the content of the lesson and video requirements and generate video based on the prompts. For example, the generation AI receives a video prompt and generates appropriate video based on the prompts. This allows the education support system according to the embodiment to reduce the burden on educational institutions, alleviate labor shortages, and improve the quality of education. For example, teachers can reduce the time spent preparing and conducting lessons and spend more time on "childcare." Furthermore, providing an optimal learning environment for each student can improve learning effectiveness. Furthermore, providing native-level audio and appropriate video can promote language learning and visual comprehension.

[0030] The lesson progress unit can learn the teacher's teaching style and teaching methods, and conduct lessons that are tailored to the teacher's characteristics. For example, the lesson progress unit's generative AI can learn the teacher's teaching style and conduct lessons that are tailored to the teacher's characteristics. For example, the generative AI can learn the analogies and explanation methods that the teacher often uses and incorporate them into the lesson. The lesson progress unit can also learn the teacher's teaching methods and conduct lessons based on that. For example, the generative AI can learn the teacher's explanation style and how they ask questions, and reflect that in the lesson. This allows lessons to be tailored to the teacher's characteristics, making the lesson more approachable for students.

[0031] The learning analysis unit can provide learning content based on students' interests and concerns. For example, the generation AI in the learning analysis unit analyzes students' interests and concerns and provides learning content based on them. For example, the generation AI automatically selects learning materials related to topics that the student likes. The learning analysis unit can also have the generation AI receive students' interests and concerns as prompts and generate learning content based on them. For example, the generation AI receives students' interests and concerns as prompts and provides personalized learning content based on them. This can increase students' motivation to learn by providing learning content based on their interests and concerns.

[0032] The audio providing unit can provide audio with different accents and dialects, allowing students to learn a variety of pronunciations. For example, the generation AI can provide audio with different accents and dialects, allowing students to learn a variety of pronunciations. For example, the generation AI can provide accents such as American English, British English, and Australian English. The audio providing unit can also provide audio with different dialects. For example, the generation AI can learn regional dialects and provide them to students. This allows students to improve their language skills by learning different accents and dialects.

[0033] The video providing unit can customize the content of the video according to the student's level of understanding. For example, the generation AI provides video according to the lesson content, and customizes the content of the video according to the student's level of understanding. For example, the generation AI provides video with more detailed explanations to students with low levels of understanding. The video providing unit can also customize the content of the video based on the generation AI receiving the student's level of understanding as a prompt. For example, the generation AI receives the student's level of understanding as a prompt and adjusts the content of the video based on that. This can improve learning effectiveness by providing video according to the student's level of understanding.

[0034] The lesson progress section can respond to students' questions after class and check their homework. In the lesson progress section, for example, the generation AI accepts questions from students after class and answers them in real time. For example, when a student enters a question in chat format, the generation AI immediately generates an answer. The lesson progress section can also have the generation AI check homework. For example, the generation AI analyzes and evaluates students' submitted work. This reduces the burden on teachers by allowing them to respond to questions and check homework after class.

[0035] The learning analysis unit can provide feedback tailored to the student's learning style. For example, the generation AI in the learning analysis unit analyzes the student's learning style and provides feedback tailored to that style. For example, for a student who prefers visual learning, feedback that makes extensive use of diagrams and graphs can be provided. The learning analysis unit can also have the generation AI receive the student's learning style as a prompt and provide feedback based on that. For example, the generation AI receive the student's learning style as a prompt and provide personalized feedback based on that. This makes it possible to provide feedback tailored to the student's learning style, thereby improving learning effectiveness.

[0036] The video providing unit can create interactive video teaching materials, allowing students to advance their learning on their own. For example, the video providing unit allows the generation AI to create interactive video teaching materials according to the lesson content, allowing students to advance their learning on their own. For example, the generation AI provides teaching materials in a format where students answer questions within the video. The video providing unit can also allow the generation AI to receive interactive video teaching materials as prompts and create teaching materials based on them. For example, the generation AI receives prompts for interactive video teaching materials and generates teaching materials based on them. In this way, interactive video teaching materials are created, allowing students to advance their learning on their own.

[0037] The learning analysis unit can propose future learning plans based on a student's learning history. In the learning analysis unit, for example, the generation AI analyzes a student's learning history and proposes a future learning plan. For example, the generation AI suggests what to learn next based on past grades and level of understanding. The learning analysis unit can also use the generation AI to receive a student's learning history as a prompt and propose a learning plan based on that. For example, the generation AI receives a student's learning history as a prompt and proposes a future learning plan based on that. In this way, by proposing a future learning plan based on the student's learning history, it is possible to set long-term learning goals.

[0038] The audio providing unit can support online exchanges with students overseas as part of an intercultural exchange program. For example, the audio providing unit allows the generation AI to support the intercultural exchange program and facilitate online exchanges with students overseas. For example, the generation AI pairs students with students overseas to practice conversation as part of language learning. The audio providing unit can also allow the generation AI to receive prompts from the intercultural exchange program and support the online exchange based on the prompts. For example, the generation AI receives prompts from the intercultural exchange program and adjusts the schedule for the online exchange based on the prompts. This can improve the quality of language learning by supporting online exchanges with students overseas as part of the intercultural exchange program.

[0039] The video provision unit can provide quizzes and activities related to the video to enhance learning effectiveness. For example, the generation AI provides video corresponding to the lesson content, and the video provision unit provides quizzes and activities related to the video. For example, the generation AI poses a quiz to check comprehension after watching the video. The video provision unit can also allow the generation AI to receive an activity related to the video as a prompt and provide an activity based on that. For example, the generation AI receives a prompt for an activity related to the video and suggests group work or practical training based on that. In this way, learning effectiveness can be enhanced by providing quizzes and activities related to the video.

[0040] The lesson management department can support the management of schedules across the school and the planning and management of events. For example, in the lesson management department, the generative AI manages the school's schedule and automatically creates timetables for classes and events. For example, the generative AI proposes the optimal schedule taking into account teachers' free time and student schedules. The lesson management department can also support the planning and management of events using the generative AI. For example, the generative AI receives prompts for the content and management procedures of an event, and plans and manages the event based on these. This reduces the burden on teachers by supporting the management of schedules across the school and the planning and management of events.

[0041] The lesson progress section can communicate with parents on behalf of the teacher and prepare for parent-teacher meetings and individual interviews. In the lesson progress section, for example, the generation AI can communicate with parents on behalf of the teacher and arrange schedules for parent-teacher meetings and individual interviews. For example, the generation AI can automatically respond to inquiries from parents and arrange the date and time of the interview. The lesson progress section can also have the generation AI receive prompts for parent-teacher meetings and individual interviews and make preparations based on those prompts. For example, the generation AI can receive prompts for parent-teacher meetings and individual interviews and create materials based on those prompts. In this way, the burden on teachers can be reduced by communicating with parents on behalf of the teacher and preparing for parent-teacher meetings and individual interviews.

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

[0043] The lesson progress section can learn the teacher's teaching style and teaching methods, and conduct lessons that are tailored to the teacher's characteristics. For example, the generative AI can learn the teacher's teaching style and conduct lessons that are tailored to the teacher's characteristics. For example, the generative AI can learn the analogies and explanation methods that the teacher often uses and incorporate them into the lesson. The lesson progress section can also learn the teacher's teaching methods and conduct lessons based on that. For example, the generative AI can learn the teacher's explanation style and how they ask questions, and reflect that in the lesson. This makes it possible to conduct lessons that are tailored to the teacher's characteristics, and provide lessons that are easy for students to understand.

[0044] The learning analysis unit can provide learning content based on students' interests and concerns. For example, the generation AI analyzes students' interests and concerns and provides learning content based on them. For example, the generation AI automatically selects learning materials related to topics that the student likes. The learning analysis unit can also enable the generation AI to receive students' interests and concerns as prompts and generate learning content based on them. For example, the generation AI receives students' interests and concerns as prompts and provides personalized learning content based on them. This can increase students' motivation to learn by providing learning content based on their interests and concerns.

[0045] The audio providing unit can provide audio with different accents and dialects to enable students to learn diverse pronunciations. For example, the generation AI can provide audio with different accents and dialects to enable students to learn diverse pronunciations. For example, the generation AI can provide accents such as American English, British English, and Australian English. The audio providing unit can also provide audio with different dialects. For example, the generation AI can learn regional dialects and provide them to students. This allows students to improve their language skills by learning different accents and dialects.

[0046] The video provision unit can customize the content of the video according to the student's level of comprehension. For example, the generation AI provides video according to the lesson content and customizes the content of the video according to the student's level of comprehension. For example, the generation AI provides video with more detailed explanations to students with low levels of comprehension. The video provision unit can also customize the content of the video based on the generation AI receiving the student's level of comprehension as a prompt. For example, the generation AI receives the student's level of comprehension as a prompt and adjusts the content of the video based on that. This can improve learning effectiveness by providing video according to the student's level of comprehension.

[0047] The lesson management department can respond to students' questions after class and check their homework. For example, the generation AI can accept questions from students after class and answer them in real time. For example, when a student enters a question in chat format, the generation AI can instantly generate an answer. The lesson management department can also use the generation AI to check homework. For example, the generation AI can analyze and evaluate students' submitted work. This reduces the burden on teachers by allowing them to respond to questions and check homework after class.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The lesson progress unit conducts the lesson. For example, the generation AI understands the content of the lesson and conducts the lesson using appropriate teaching materials. The lesson progress unit can also conduct the lesson based on prompts that include how the generation AI should conduct the lesson. For example, the generation AI receives the information necessary to conduct the lesson as prompts and conducts the lesson based on those prompts. Step 2: The learning analysis unit analyzes the student's learning progress based on the lesson content conducted by the lesson management unit. For example, the generation AI analyzes the student's answers and provides feedback based on their level of understanding. The learning analysis unit can also analyze the student's learning status in real time and provide an individualized learning plan. For example, the generation AI receives the student's answers and learning status as prompts and generates an individualized learning plan based on them. Step 3: The audio provider provides native-level audio based on the learning progress analyzed by the learning analysis unit. For example, the generation AI accurately reproduces English pronunciation and intonation and provides pronunciation practice to the student. The audio provider can also receive prompts from the generation AI regarding the content and method of pronunciation practice, and generate audio based on those prompts. For example, the generation AI receives a prompt for pronunciation practice, and generates native-level audio based on that. Step 4: The video provision unit provides video according to the lesson content. For example, the generation AI generates video according to the lesson content to provide visual assistance to students. The video provision unit can also generate video based on the lesson content and video requirements received as prompts by the generation AI. For example, the generation AI receives a video prompt and generates appropriate video based on that.

[0050] (Example 2) The education support system according to an embodiment of the present invention is a system that reduces the burden on educational institutions, alleviates labor shortages, and improves the quality of education. This system utilizes a robot equipped with generative AI to create an environment in which teachers can devote more time to "caring for children." This allows the education support system to reduce the burden on educational institutions, alleviate labor shortages, and improve the quality of education.

[0051] An educational support system according to an embodiment includes a lesson progress unit, a learning analysis unit, an audio provision unit, and a video provision unit. The lesson progress unit progresses a lesson. For example, a generation AI understands the content of the lesson and progresses the lesson using appropriate teaching materials. The lesson progress unit can also conduct a lesson based on prompts including a method for progressing the lesson provided by the generation AI. For example, the generation AI receives information necessary for progressing the lesson as prompts and progresses the lesson based on the prompts. The learning analysis unit analyzes a student's learning progress based on the lesson content progressed by the lesson progress unit. For example, the generation AI analyzes a student's answers and provides feedback according to the student's level of understanding. The learning analysis unit can also analyze a student's learning status in real time and provide an individualized learning plan. For example, the generation AI receives a student's answers and learning status as prompts and generates an individualized learning plan based on the prompts. The audio provision unit provides native-level audio based on the learning progress analyzed by the learning analysis unit. For example, the generation AI accurately reproduces English pronunciation and intonation and provides pronunciation practice for the student. The audio providing unit can also allow the generation AI to receive prompts regarding the content and method of pronunciation practice and generate audio based on the prompts. For example, the generation AI receives a prompt for pronunciation practice and generates native-level audio based on the prompts. The video providing unit can provide video according to the lesson content. For example, the generation AI generates video according to the lesson content to provide visual assistance to students. The video providing unit can also allow the generation AI to receive prompts regarding the content of the lesson and video requirements and generate video based on the prompts. For example, the generation AI receives a video prompt and generates appropriate video based on the prompts. This allows the education support system according to the embodiment to reduce the burden on educational institutions, alleviate labor shortages, and improve the quality of education. For example, teachers can reduce the time spent preparing and conducting lessons and spend more time on "childcare." Furthermore, providing an optimal learning environment for each student can improve learning effectiveness. Furthermore, providing native-level audio and appropriate video can promote language learning and visual comprehension.

[0052] The lesson progress unit can learn the teacher's teaching style and teaching methods, and conduct lessons that are tailored to the teacher's characteristics. For example, the lesson progress unit's generative AI can learn the teacher's teaching style and conduct lessons that are tailored to the teacher's characteristics. For example, the generative AI can learn the analogies and explanation methods that the teacher often uses and incorporate them into the lesson. The lesson progress unit can also learn the teacher's teaching methods and conduct lessons based on that. For example, the generative AI can learn the teacher's explanation style and how they ask questions, and reflect that in the lesson. This allows lessons to be tailored to the teacher's characteristics, making the lesson more approachable for students.

[0053] The learning analysis unit can provide learning content based on students' interests and concerns. For example, the generation AI in the learning analysis unit analyzes students' interests and concerns and provides learning content based on them. For example, the generation AI automatically selects learning materials related to topics that the student likes. The learning analysis unit can also have the generation AI receive students' interests and concerns as prompts and generate learning content based on them. For example, the generation AI receives students' interests and concerns as prompts and provides personalized learning content based on them. This can increase students' motivation to learn by providing learning content based on their interests and concerns.

[0054] The audio providing unit can provide audio with different accents and dialects, allowing students to learn a variety of pronunciations. For example, the generation AI can provide audio with different accents and dialects, allowing students to learn a variety of pronunciations. For example, the generation AI can provide accents such as American English, British English, and Australian English. The audio providing unit can also provide audio with different dialects. For example, the generation AI can learn regional dialects and provide them to students. This allows students to improve their language skills by learning different accents and dialects.

[0055] The video providing unit can customize the content of the video according to the student's level of understanding. For example, the generation AI provides video according to the lesson content, and customizes the content of the video according to the student's level of understanding. For example, the generation AI provides video with more detailed explanations to students with low levels of understanding. The video providing unit can also customize the content of the video based on the generation AI receiving the student's level of understanding as a prompt. For example, the generation AI receives the student's level of understanding as a prompt and adjusts the content of the video based on that. This can improve learning effectiveness by providing video according to the student's level of understanding.

[0056] The lesson progress section can respond to students' questions after class and check their homework. In the lesson progress section, for example, the generation AI accepts questions from students after class and answers them in real time. For example, when a student enters a question in chat format, the generation AI immediately generates an answer. The lesson progress section can also have the generation AI check homework. For example, the generation AI analyzes and evaluates students' submitted work. This reduces the burden on teachers by allowing them to respond to questions and check homework after class.

[0057] The learning analysis unit can provide feedback tailored to the student's learning style. For example, the generation AI in the learning analysis unit analyzes the student's learning style and provides feedback tailored to that style. For example, for a student who prefers visual learning, feedback that makes extensive use of diagrams and graphs can be provided. The learning analysis unit can also have the generation AI receive the student's learning style as a prompt and provide feedback based on that. For example, the generation AI receive the student's learning style as a prompt and provide personalized feedback based on that. This makes it possible to provide feedback tailored to the student's learning style, thereby improving learning effectiveness.

[0058] The audio providing unit can analyze a student's emotions regarding language learning and provide an audio message to increase motivation. For example, the generation AI of the audio providing unit analyzes a student's facial expressions and tone of voice to analyze their emotions regarding language learning in real time. For example, if it determines that a student is tired, it provides an encouraging audio message. The audio providing unit can also receive a student's emotions as a prompt and provide an audio message to increase motivation based on that. For example, the generation AI receives a student's emotions as a prompt and generates an encouraging message based on that. In this way, by analyzing a student's emotions regarding language learning and providing an audio message to increase motivation, it is possible to improve their motivation to learn.

[0059] The video providing unit can create interactive video teaching materials, allowing students to advance their learning on their own. For example, the video providing unit allows the generation AI to create interactive video teaching materials according to the lesson content, allowing students to advance their learning on their own. For example, the generation AI provides teaching materials in a format where students answer questions within the video. The video providing unit can also allow the generation AI to receive interactive video teaching materials as prompts and create teaching materials based on them. For example, the generation AI receives prompts for interactive video teaching materials and generates teaching materials based on them. In this way, interactive video teaching materials are created, allowing students to advance their learning on their own.

[0060] The lesson management unit can use the emotion estimation function to monitor students' emotional states in real time and provide breaks or words of encouragement at appropriate times. For example, the generation AI in the lesson management unit analyzes students' facial expressions and tone of voice to monitor their emotional states in real time. For example, if it determines that a student is tired, it will suggest a break. The lesson management unit can also use the generation AI to receive students' emotions as prompts and provide breaks or words of encouragement based on those prompts. For example, the generation AI can receive students' emotions as prompts and provide breaks at appropriate times based on those prompts. This makes it possible to monitor students' emotional states in real time and provide breaks or words of encouragement at appropriate times, thereby improving learning effectiveness.

[0061] The learning analysis unit can propose future learning plans based on a student's learning history. In the learning analysis unit, for example, the generation AI analyzes a student's learning history and proposes a future learning plan. For example, the generation AI suggests what to learn next based on past grades and level of understanding. The learning analysis unit can also use the generation AI to receive a student's learning history as a prompt and propose a learning plan based on that. For example, the generation AI receives a student's learning history as a prompt and proposes a future learning plan based on that. In this way, by proposing a future learning plan based on the student's learning history, it is possible to set long-term learning goals.

[0062] The audio providing unit can support online exchanges with students overseas as part of an intercultural exchange program. For example, the audio providing unit allows the generation AI to support the intercultural exchange program and facilitate online exchanges with students overseas. For example, the generation AI pairs students with students overseas to practice conversation as part of language learning. The audio providing unit can also allow the generation AI to receive prompts from the intercultural exchange program and support the online exchange based on the prompts. For example, the generation AI receives prompts from the intercultural exchange program and adjusts the schedule for the online exchange based on the prompts. This can improve the quality of language learning by supporting online exchanges with students overseas as part of the intercultural exchange program.

[0063] The video provision unit can provide quizzes and activities related to the video to enhance learning effectiveness. For example, the generation AI provides video corresponding to the lesson content, and the video provision unit provides quizzes and activities related to the video. For example, the generation AI poses a quiz to check comprehension after watching the video. The video provision unit can also allow the generation AI to receive an activity related to the video as a prompt and provide an activity based on that. For example, the generation AI receives a prompt for an activity related to the video and suggests group work or practical training based on that. In this way, learning effectiveness can be enhanced by providing quizzes and activities related to the video.

[0064] The lesson management department can support the management of schedules across the school and the planning and management of events. For example, in the lesson management department, the generative AI manages the school's schedule and automatically creates timetables for classes and events. For example, the generative AI proposes the optimal schedule taking into account teachers' free time and student schedules. The lesson management department can also support the planning and management of events using the generative AI. For example, the generative AI receives prompts for the content and management procedures of an event, and plans and manages the event based on these. This reduces the burden on teachers by supporting the management of schedules across the school and the planning and management of events.

[0065] The learning analysis unit can analyze the compatibility between students and suggest optimal study partners. For example, the generation AI in the learning analysis unit analyzes students' emotional data and suggests compatible study partners. For example, the generation AI pairs students who share the same interests. The learning analysis unit can also receive students' compatibility as a prompt and suggest study partners based on that. For example, the generation AI receives students' compatibility as a prompt and suggests optimal study partners based on that. In this way, by analyzing the compatibility between students and suggesting optimal study partners, learning effectiveness can be improved.

[0066] The lesson progress section can communicate with parents on behalf of the teacher and prepare for parent-teacher meetings and individual interviews. In the lesson progress section, for example, the generation AI can communicate with parents on behalf of the teacher and arrange schedules for parent-teacher meetings and individual interviews. For example, the generation AI can automatically respond to inquiries from parents and arrange the date and time of the interview. The lesson progress section can also have the generation AI receive prompts for parent-teacher meetings and individual interviews and make preparations based on those prompts. For example, the generation AI can receive prompts for parent-teacher meetings and individual interviews and create materials based on those prompts. In this way, the burden on teachers can be reduced by communicating with parents on behalf of the teacher and preparing for parent-teacher meetings and individual interviews.

[0067] The video provision unit can use the emotion estimation function to analyze the emotional state of the student and provide videos and music with a relaxing effect. For example, the generation AI of the video provision unit analyzes the student's facial expressions and tone of voice to analyze the emotional state in real time. For example, if it determines that the student is feeling stressed, it provides videos and music with a relaxing effect. The video provision unit can also provide videos and music with a relaxing effect based on the generation AI receiving the student's emotions as prompts. For example, the generation AI receives the student's emotions as prompts and generates videos and music with a relaxing effect based on them. This makes it possible to optimize the learning environment by analyzing the student's emotional state and providing videos and music with a relaxing effect.

[0068] The class management unit can monitor the stress level of teachers and make suggestions for refreshment at appropriate times. For example, the generation AI in the class management unit can analyze the teacher's facial expressions and tone of voice to monitor the stress level in real time. For example, if it determines that the teacher is tired, it can suggest a break. The class management unit can also receive the teacher's stress level as a prompt and make suggestions for refreshment based on that. For example, the generation AI can receive the teacher's stress level as a prompt and make suggestions for refreshment at appropriate times based on that. In this way, by monitoring the teacher's stress level and making suggestions for refreshment at appropriate times, it is possible to maintain the health of teachers and improve the quality of education.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The lesson progress section can learn the teacher's teaching style and teaching methods, and conduct lessons that are tailored to the teacher's characteristics. For example, the generative AI can learn the teacher's teaching style and conduct lessons that are tailored to the teacher's characteristics. For example, the generative AI can learn the analogies and explanation methods that the teacher often uses and incorporate them into the lesson. The lesson progress section can also learn the teacher's teaching methods and conduct lessons based on that. For example, the generative AI can learn the teacher's explanation style and how they ask questions, and reflect that in the lesson. This makes it possible to conduct lessons that are tailored to the teacher's characteristics, and provide lessons that are easy for students to understand.

[0071] The learning analysis unit can provide learning content based on students' interests and concerns. For example, the generation AI analyzes students' interests and concerns and provides learning content based on them. For example, the generation AI automatically selects learning materials related to topics that the student likes. The learning analysis unit can also enable the generation AI to receive students' interests and concerns as prompts and generate learning content based on them. For example, the generation AI receives students' interests and concerns as prompts and provides personalized learning content based on them. This can increase students' motivation to learn by providing learning content based on their interests and concerns.

[0072] The audio providing unit can provide audio with different accents and dialects to enable students to learn diverse pronunciations. For example, the generation AI can provide audio with different accents and dialects to enable students to learn diverse pronunciations. For example, the generation AI can provide accents such as American English, British English, and Australian English. The audio providing unit can also provide audio with different dialects. For example, the generation AI can learn regional dialects and provide them to students. This allows students to improve their language skills by learning different accents and dialects.

[0073] The video provision unit can customize the content of the video according to the student's level of comprehension. For example, the generation AI provides video according to the lesson content and customizes the content of the video according to the student's level of comprehension. For example, the generation AI provides video with more detailed explanations to students with low levels of comprehension. The video provision unit can also customize the content of the video based on the generation AI receiving the student's level of comprehension as a prompt. For example, the generation AI receives the student's level of comprehension as a prompt and adjusts the content of the video based on that. This can improve learning effectiveness by providing video according to the student's level of comprehension.

[0074] The lesson management department can respond to students' questions after class and check their homework. For example, the generation AI can accept questions from students after class and answer them in real time. For example, when a student enters a question in chat format, the generation AI can instantly generate an answer. The lesson management department can also use the generation AI to check homework. For example, the generation AI can analyze and evaluate students' submitted work. This reduces the burden on teachers by allowing them to respond to questions and check homework after class.

[0075] The audio providing unit can analyze a student's emotions regarding language learning and provide an audio message to increase motivation. For example, the generation AI analyzes a student's facial expressions and tone of voice to analyze their emotions regarding language learning in real time. For example, if it determines that a student is tired, it can provide an encouraging audio message. The audio providing unit can also receive a student's emotions as a prompt, and provide an audio message to increase motivation based on that. For example, the generation AI receives a student's emotions as a prompt, and generates an encouraging message based on that. In this way, by analyzing a student's emotions regarding language learning and providing an audio message to increase motivation, it is possible to improve their motivation to learn.

[0076] The video provision unit can use the emotion estimation function to analyze the emotional state of students and provide videos and music with a relaxing effect. For example, the generation AI analyzes the student's facial expressions and tone of voice to analyze their emotional state in real time. For example, if it determines that a student is feeling stressed, it can provide videos and music with a relaxing effect. The video provision unit can also have the generation AI receive the student's emotions as prompts and provide videos and music with a relaxing effect based on those prompts. For example, the generation AI receives the student's emotions as prompts and generates videos and music with a relaxing effect based on those prompts. This allows the learning environment to be optimized by analyzing the student's emotional state and providing videos and music with a relaxing effect.

[0077] The lesson management department can use the emotion estimation function to monitor students' emotional states in real time and provide breaks or words of encouragement at appropriate times. For example, the generation AI analyzes students' facial expressions and tone of voice to monitor their emotional states in real time. For example, if it determines that a student is tired, it will suggest a break. The lesson management department can also use the generation AI to receive students' emotions as prompts and provide breaks or words of encouragement based on those prompts. For example, the generation AI can receive students' emotions as prompts and provide breaks at appropriate times based on those prompts. This allows students' emotional states to be monitored in real time and provide breaks or words of encouragement at appropriate times, thereby improving learning effectiveness.

[0078] The learning analysis unit can analyze students' emotional data and suggest compatible study partners. For example, the generation AI can analyze students' emotional data and suggest compatible study partners. For example, the generation AI can pair students who share the same interests. The learning analysis unit can also receive students' compatibility as a prompt and suggest study partners based on that. For example, the generation AI can receive students' compatibility as a prompt and suggest optimal study partners based on that. This allows the compatibility between students to be analyzed and optimal study partners to be suggested, thereby improving learning effectiveness.

[0079] The lesson management department can monitor teachers' stress levels and make suggestions for refreshment at appropriate times. For example, the generation AI can analyze teachers' facial expressions and tone of voice to monitor stress levels in real time. For example, if it determines that a teacher is tired, it can suggest a break. The lesson management department can also use the generation AI to receive teachers' stress levels as prompts and make suggestions for refreshment based on that. For example, the generation AI can receive teachers' stress levels as prompts and make suggestions for refreshment at appropriate times based on that. In this way, by monitoring teachers' stress levels and making suggestions for refreshment at appropriate times, it is possible to maintain teachers' health and improve the quality of education.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The lesson progress unit conducts the lesson. For example, the generation AI understands the content of the lesson and conducts the lesson using appropriate teaching materials. The lesson progress unit can also conduct the lesson based on prompts that include how the generation AI should conduct the lesson. For example, the generation AI receives the information necessary to conduct the lesson as prompts and conducts the lesson based on those prompts. Step 2: The learning analysis unit analyzes the student's learning progress based on the lesson content conducted by the lesson management unit. For example, the generation AI analyzes the student's answers and provides feedback based on their level of understanding. The learning analysis unit can also analyze the student's learning status in real time and provide an individualized learning plan. For example, the generation AI receives the student's answers and learning status as prompts and generates an individualized learning plan based on them. Step 3: The audio provider provides native-level audio based on the learning progress analyzed by the learning analysis unit. For example, the generation AI accurately reproduces English pronunciation and intonation and provides pronunciation practice to the student. The audio provider can also receive prompts from the generation AI regarding the content and method of pronunciation practice, and generate audio based on those prompts. For example, the generation AI receives a prompt for pronunciation practice, and generates native-level audio based on that. Step 4: The video provision unit provides video according to the lesson content. For example, the generation AI generates video according to the lesson content to provide visual assistance to students. The video provision unit can also generate video based on the lesson content and video requirements received as prompts by the generation AI. For example, the generation AI receives a video prompt and generates appropriate video based on that.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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. The class management department, which conducts the classes, a learning analysis unit that analyzes the learning progress of students based on the content of the lesson conducted by the lesson proceeding unit; a voice providing unit that provides native-level voice based on the learning progress analyzed by the learning analysis unit; a video providing unit that provides a video according to the content of the lesson A system characterized by:

2. The lesson progress department: Learn about the teaching style and methods of the teacher and tailor lessons to suit the teacher's characteristics 2. The system of claim 1.

3. The learning analysis unit Providing learning content based on the student's interests 2. The system of claim 1.

4. The voice providing unit Providing audio in different accents and dialects to help students learn a variety of pronunciations 2. The system of claim 1.

5. The video providing unit Customize the content of the video according to the student's level of understanding 2. The system of claim 1.

6. The voice providing unit Analyzing the student's feelings about language learning and providing them with a voice message to motivate them 2. The system of claim 1.

7. The lesson progress department: Monitor the student's emotional state in real time and provide appropriate breaks or words of encouragement 2. The system of claim 1.

8. The video providing unit Analyze the student's emotional state and provide relaxing images and music 2. The system of claim 1.

Images