System

Abstract

An object of the system according to the embodiment is to quickly and appropriately answer questions asked by students in a recorded class video.SOLUTION: A system includes a question input unit, an analysis unit, an answer generation unit, and a provision unit. The question input part inputs a question from the student. The analysis unit analyzes the question input by the question input unit. The answer generation unit generates an answer based on the question analyzed by the analysis unit. The providing unit provides the answer generated by the answer generating unit to the student.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and appropriately answer students' questions about recorded video lectures.

[0005] The system according to the embodiment aims to quickly and appropriately answer questions from students about recorded video of a lesson. [Means for solving the problem]

[0006] The system according to the embodiment includes a question input unit, an analysis unit, an answer generation unit, and a providing unit. The question input unit inputs a question from a student. The analysis unit analyzes the question input by the question input unit. The answer generation unit generates an answer based on the question analyzed by the analysis unit. The providing unit provides the answer generated by the answer generation unit to the student. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and appropriately answer questions from students about recorded video of a lesson. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the question-answering system according to the embodiment of the present invention, when a student has a question while watching a video lecture, the student inputs the question into an AI, which then provides an appropriate answer. This allows the question-answering system to quickly resolve the student's question and improve learning effectiveness.

[0029] A question answering system according to an embodiment includes a question input unit, an analysis unit, an answer generation unit, and a providing unit. The question input unit inputs a question from a student. For example, the student inputs the question in text format. The question input unit can also input the question using voice input. For example, the student inputs the question by voice using a microphone. The analysis unit analyzes the question input by the question input unit. For example, the generation AI analyzes the content of the question using natural language processing technology. The generation AI can also analyze the intent of the question using a machine learning algorithm. For example, the generation AI analyzes the intent of the question by referring to past question history and the student's learning history. The answer generation unit generates an answer based on the question analyzed by the analysis unit. For example, the generation AI generates an appropriate answer by referring to a database. The generation AI can also generate an answer using AI generation technology. For example, the generation AI understands the content of the lesson and generates an appropriate answer. The providing unit provides the answer generated by the answer generation unit to the student. For example, the providing unit provides the answer in text format. The providing unit can also provide the answer in audio format or video format. For example, the providing unit plays back the answer generated by the generation AI as audio. The providing unit can also display the answer generated by the generation AI as a video. This allows the question-answering system according to the embodiment to provide an appropriate answer to a student's question.

[0030] The analysis unit can analyze the intent of a question by referencing the question history and the student's learning history. For example, when a question is entered by a student, the generation AI refers to past question history and analyzes the intent based on similar questions and their answers. For example, if a similar question has been asked in the past, the generation AI can refer to those answers to generate a new answer. The analysis unit also refers to the student's learning history and allows the generation AI to understand where the student is likely to have difficulty. For example, if a question related to a topic that was difficult to understand in the past is entered, the generation AI can provide an answer specific to that topic. In addition, to understand the intent of the question, the analysis unit allows the generation AI to analyze the student's learning progress data and generate an answer based on the student's current level of understanding. For example, if basic understanding is lacking, the generation AI can provide an answer that explains in detail starting from the basics. This allows for more accurate analysis of the intent of the question.

[0031] The question input unit can detect ambiguity in the question and provide a prompt that encourages the student to ask a more specific question. For example, if a student inputs an ambiguous question, the question input unit detects the ambiguity and displays a prompt that encourages the student to ask a more specific question. For example, a prompt such as, "Please tell me specifically which part you don't understand" is provided. In addition, when a student inputs a question, the question input unit detects ambiguous expressions and displays an interactive prompt that asks the student for specific examples or details. For example, a prompt such as, "Please tell me specifically which part of this mathematical formula you don't understand" is provided. In addition, the question input unit detects ambiguity in the question and provides options that encourage the student to ask a more specific question. For example, options such as, "Would you like to know more about this topic? Or would you like a specific example?" are displayed. This eliminates the ambiguity in the question and encourages the student to ask a more specific question.

[0032] The question input unit can use voice recognition technology to enable students to input questions by voice. The question input unit, for example, uses voice recognition technology to build a system that allows students to input questions by voice. For example, a question is input by voice using a microphone, and the generation AI converts the voice into text and analyzes it. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice, and the generation AI generates an appropriate answer. For example, a question input by voice, such as "Please tell me the meaning of this mathematical formula," is converted into text and analyzed. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice in real time, and a system is built in which the generation AI provides an answer instantly. For example, a text-format answer is instantly provided in response to a question input by voice. This allows students to input questions by voice.

[0033] The question input unit can automatically suggest questions and answers if other students have asked similar questions. For example, the question input unit constructs a system that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, it displays a suggestion such as, "Other students have asked the same question. Please see this answer." The question input unit also adds a function that, when a student inputs a question, references a database of past questions and automatically suggests similar questions and their answers. For example, it displays a suggestion such as, "Past answers to this question are here." The question input unit also develops an algorithm that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, it analyzes the content of the question and suggests similar questions and their answers. This makes it possible to automatically suggest similar questions and answers.

[0034] When understanding the content of a lesson, the answer generation unit can analyze at least one non-verbal piece of information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, when the generation AI understands the content of a lesson, the answer generation unit analyzes non-verbal information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, an answer is generated based on the parts emphasized by the instructor or the important points of the slides. Furthermore, by analyzing non-verbal information, the generation AI can gain a deeper understanding of the content of the lesson and provide a more detailed answer. For example, the answer generation unit analyzes the instructor's gestures and facial expressions to generate an answer that reflects their intentions. Furthermore, when the generation AI understands the content of a lesson, the answer generation unit analyzes the slide design and the instructor's speaking style to extract important information and reflect it in the answer. For example, the answer generation unit analyzes the slide layout and color usage to provide an answer that emphasizes important points. In this way, by analyzing non-verbal information, a more detailed answer can be generated.

[0035] The answer generation unit can improve the quality of answers by referring to related academic papers and reference books when understanding the content of a class. For example, when the generation AI understands the content of a class, the answer generation unit can improve the quality of answers by referring to related external academic papers and reference books. For example, an answer can be generated by referring to the latest research papers related to the topic covered in the class. The answer generation unit can also provide more detailed and accurate answers by having the generation AI refer to external academic papers and reference books when understanding the content of a class. For example, an answer can be generated by citing specific examples and explanations from reference books. The answer generation unit can also automatically collect related external academic papers and reference books when the generation AI understands the content of a class, and generate answers based on that information. For example, an answer can be provided by referring to literature related to the topic covered in the class. This can improve the quality of answers by referring to external academic papers and reference books.

[0036] The answer generation unit can combine knowledge from different fields to provide answers from a new perspective when understanding the lesson content and generating answers. For example, when the generation AI understands the lesson content, the answer generation unit combines knowledge from different fields to provide answers from a new perspective. For example, in a mathematics class, knowledge of physics is used to explain application examples of mathematical formulas. The answer generation unit also combines knowledge from different fields, allowing the generation AI to provide answers to the lesson content from a new perspective. For example, in an economics class, knowledge of psychology is used to explain market behavior. The answer generation unit also builds a system in which the generation AI combines knowledge from different fields to understand the lesson content and provide answers from a new perspective. For example, in a biology class, knowledge of chemistry is used to explain the function of cells. This makes it possible to combine knowledge from different fields to provide answers from a new perspective.

[0037] The answer generation unit can refer to lesson videos from other instructors when understanding the lesson content and generate answers from multiple perspectives. For example, the answer generation unit builds a system in which the generation AI refers to lesson videos from other instructors when understanding the lesson content and generates answers from multiple perspectives. For example, answers are generated based on explanations from different instructors on the same topic. The answer generation unit also refers to lesson videos from other instructors so that the generation AI provides answers to the lesson content from multiple perspectives. For example, the answer generation unit compares explanations from different instructors and provides the most understandable explanation. The answer generation unit also develops a system in which the generation AI refers to lesson videos from other instructors and generates answers from multiple perspectives. For example, the lesson content from different instructors is integrated to provide a comprehensive answer. This makes it possible to provide answers from multiple perspectives by referring to lesson videos from other instructors.

[0038] The providing unit can add an interactive chat function that allows students to ask follow-up questions in real time about answers provided by the generating AI. For example, the providing unit builds a system that adds an interactive chat function that allows students to ask follow-up questions in real time about answers provided by the generating AI. For example, a student can request a more detailed explanation about an answer. The providing unit also uses the interactive chat function to allow students to provide real-time feedback on answers from the generating AI and ask follow-up questions. For example, a student can input a question such as "Can you give me a more specific example?" in response to an answer. The providing unit also develops an interactive chat function that allows students to ask follow-up questions in real time about answers provided by the generating AI. For example, a student can input a question such as "I still don't understand this part," in response to an answer, and the generating AI can provide an additional answer in response. This makes it possible to provide an interactive chat function that allows students to ask follow-up questions in real time.

[0039] The provision unit can enable the generation AI to provide background information and related topics of the answer as links when providing an answer, thereby enabling students to learn more deeply. For example, the provision unit builds a system that provides background information and related topics as links for answers provided by the generation AI. For example, it displays links to reference materials and websites related to the answer. The provision unit also enables the generation AI to provide background information and related topics of the answer as links when providing an answer, thereby enabling students to learn more deeply. For example, it displays links to academic papers and research results related to the answer. The provision unit also develops a system that enables students to learn more deeply by providing background information and related topics of the answer as links. For example, it displays links to videos and presentations related to the answer. This makes it possible to provide links that enable students to learn more deeply.

[0040] The providing unit provides answers not only in text format but also in audio or video format, thereby realizing answers in a variety of formats that suit the learning style of the student. For example, the providing unit builds a system that provides answers provided by the generation AI not only in text format but also in audio or video format. For example, audio explanations and video demonstrations are provided. Furthermore, by providing answers not only in text format but also in audio or video format, answers in a variety of formats that suit the learning style of the student are realized. For example, audio explanations and video demonstrations are provided. Furthermore, the providing unit develops a system that provides answers in a variety of formats that suit the learning style of the student by providing answers provided by the generation AI not only in text format but also in audio or video format. For example, audio narration and video presentations are provided. This makes it possible to provide answers in a variety of formats that suit the learning style of the student.

[0041] The provision unit collects feedback and evaluations from other students on the answers provided by the generation AI, and can continuously improve the quality of the answers. For example, the provision unit builds a system that collects feedback and evaluations from other students on the answers provided by the generation AI, and continuously improves the quality of the answers. For example, a student evaluates an answer and improves the answer based on the evaluation results. The provision unit also collects feedback and evaluations from other students, and continuously improves the quality of the answers provided by the generation AI. For example, a student leaves a comment on an answer, and the answer is improved based on that feedback. The provision unit also develops a system that continuously improves the quality of the answers by collecting feedback and evaluations from other students on the answers provided by the generation AI. For example, a student provides feedback on an answer such as "It was easy to understand" or "I'd like to know more." This allows the system to collect feedback and evaluations from other students and continuously improve the quality of the answers.

[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 question input unit can have a function to automatically categorize questions when students input them. For example, questions are automatically categorized into categories such as mathematics, physics, and chemistry. The question input unit can also suggest related reference materials and past questions and answers based on the question category. For example, for a mathematics question, it can provide related formulas and example problems. The question input unit can also use an algorithm to generate specialized answers based on the question category. For example, for a physics question, an AI with specialized physics knowledge can generate an answer. This makes it possible to provide an appropriate answer according to the question category.

[0044] The analysis unit can analyze the student's learning style and, based on that, more accurately analyze the intent of the question. For example, if the student has a visual learning style, it can generate an answer using diagrams or graphs. The analysis unit can also select an appropriate answer format depending on the student's learning style. For example, if a student has an auditory learning style, it can provide an answer in audio format. The analysis unit can also analyze the student's learning style and, based on that, use an algorithm to more deeply understand the intent of the question. For example, if a student has a hands-on learning style, it can provide an answer related to experiments or practical training. This makes it possible to provide an appropriate answer according to the student's learning style.

[0045] The question input unit may have a function to automatically evaluate the difficulty of a question when a student inputs a question. For example, the question may be classified as beginner, intermediate, or advanced. The question input unit may also provide an appropriate answer based on the difficulty of the question. For example, a basic explanation may be provided for a beginner question, and a more detailed explanation may be provided for an intermediate or advanced question. The question input unit may also use an algorithm to evaluate the difficulty of the question. For example, the content and context of the question may be analyzed to evaluate the difficulty. This allows an appropriate answer to be provided according to the difficulty of the question.

[0046] The question input unit can use voice recognition technology to enable students to input questions by voice. For example, a question can be input by voice using a microphone, and the generation AI converts the voice into text and analyzes it. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice, and the generation AI generates an appropriate answer. For example, a question input by voice, such as "Please tell me the meaning of this mathematical formula," can be converted into text and analyzed. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice in real time, and a generation AI can build a system that provides an answer instantly. For example, a text-format answer can be provided instantly in response to a question input by voice. This allows students to input questions by voice.

[0047] The question input unit can automatically suggest questions and answers if other students have asked similar questions. For example, a system can be built that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, a suggestion such as, "Other students have asked the same question. Please see the answer here." is displayed. The question input unit also adds a function that, when a student inputs a question, references a database of past questions and automatically suggests similar questions and their answers. For example, a suggestion such as, "Past answers to this question are here." is displayed. The question input unit also develops an algorithm that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, the system analyzes the content of the question and suggests similar questions and their answers. This makes it possible to automatically suggest similar questions and answers.

[0048] When understanding the content of a lesson, the answer generation unit can analyze at least one non-verbal piece of information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, when the generation AI understands the content of a lesson, it analyzes non-verbal information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, it generates an answer based on the parts emphasized by the instructor or the important points of the slides. Furthermore, by analyzing non-verbal information, the generation AI can gain a deeper understanding of the content of the lesson and provide a more detailed answer. For example, it can analyze the instructor's gestures and facial expressions to generate an answer that reflects their intentions. Furthermore, when the generation AI understands the content of a lesson, the answer generation unit can analyze the slide design and the instructor's speaking style to extract important information and reflect it in the answer. For example, it can analyze the slide layout and color usage to provide an answer that emphasizes important points. In this way, by analyzing non-verbal information, a more detailed answer can be generated.

[0049] The answer generation unit can improve the quality of answers by referring to related academic papers and reference books when understanding the content of a class. For example, when the generation AI understands the content of a class, it can refer to related external academic papers and reference books to improve the quality of answers. For example, it can generate answers by referring to the latest research papers related to the topics covered in the class. The answer generation unit also provides more detailed and accurate answers by having the generation AI refer to external academic papers and reference books when understanding the content of a class. For example, it can generate answers by citing specific examples and explanations from reference books. The answer generation unit also automatically collects related external academic papers and reference books when the generation AI understands the content of a class and generates answers based on that information. For example, it can provide answers by referring to literature related to the topics covered in the class. This allows the quality of answers to be improved by referring to external academic papers and reference books.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The question input unit inputs a question from a student. For example, the student inputs the question in text format. The question input unit can also input a question using voice input. For example, the student inputs the question by voice using a microphone. Step 2: The analysis unit analyzes the question input by the question input unit. For example, the generation AI analyzes the content of the question using natural language processing technology. The generation AI can also analyze the intent of the question using a machine learning algorithm. For example, the generation AI analyzes the intent of the question by referring to past question history and the student's learning history. Step 3: The answer generation unit generates an answer based on the question analyzed by the analysis unit. For example, the generation AI references a database to generate an appropriate answer. The generation AI can also generate answers using AI generation technology. For example, the generation AI understands the content of the lesson and generates an appropriate answer. Step 4: The providing unit provides the answer generated by the answer generating unit to the student. For example, the providing unit provides the answer in text format. The providing unit can also provide the answer in audio or video format. For example, the providing unit plays the answer generated by the generation AI as audio. The providing unit can also display the answer generated by the generation AI as a video.

[0052] (Example 2) In the question-answering system according to the embodiment of the present invention, when a student has a question while watching a video lecture, the student inputs the question into an AI, which then provides an appropriate answer. This allows the question-answering system to quickly resolve the student's question and improve learning effectiveness.

[0053] A question answering system according to an embodiment includes a question input unit, an analysis unit, an answer generation unit, and a providing unit. The question input unit inputs a question from a student. For example, the student inputs the question in text format. The question input unit can also input the question using voice input. For example, the student inputs the question by voice using a microphone. The analysis unit analyzes the question input by the question input unit. For example, the generation AI analyzes the content of the question using natural language processing technology. The generation AI can also analyze the intent of the question using a machine learning algorithm. For example, the generation AI analyzes the intent of the question by referring to past question history and the student's learning history. The answer generation unit generates an answer based on the question analyzed by the analysis unit. For example, the generation AI generates an appropriate answer by referring to a database. The generation AI can also generate an answer using AI generation technology. For example, the generation AI understands the content of the lesson and generates an appropriate answer. The providing unit provides the answer generated by the answer generation unit to the student. For example, the providing unit provides the answer in text format. The providing unit can also provide the answer in audio format or video format. For example, the providing unit plays back the answer generated by the generation AI as audio. The providing unit can also display the answer generated by the generation AI as a video. This allows the question-answering system according to the embodiment to provide an appropriate answer to a student's question.

[0054] The analysis unit can analyze the intent of a question by referencing the question history and the student's learning history. For example, when a question is entered by a student, the generation AI refers to past question history and analyzes the intent based on similar questions and their answers. For example, if a similar question has been asked in the past, the generation AI can refer to those answers to generate a new answer. The analysis unit also refers to the student's learning history and allows the generation AI to understand where the student is likely to have difficulty. For example, if a question related to a topic that was difficult to understand in the past is entered, the generation AI can provide an answer specific to that topic. In addition, to understand the intent of the question, the analysis unit allows the generation AI to analyze the student's learning progress data and generate an answer based on the student's current level of understanding. For example, if basic understanding is lacking, the generation AI can provide an answer that explains in detail starting from the basics. This allows for more accurate analysis of the intent of the question.

[0055] The question input unit can detect ambiguity in the question and provide a prompt that encourages the student to ask a more specific question. For example, if a student inputs an ambiguous question, the question input unit detects the ambiguity and displays a prompt that encourages the student to ask a more specific question. For example, a prompt such as, "Please tell me specifically which part you don't understand" is provided. In addition, when a student inputs a question, the question input unit detects ambiguous expressions and displays an interactive prompt that asks the student for specific examples or details. For example, a prompt such as, "Please tell me specifically which part of this mathematical formula you don't understand" is provided. In addition, the question input unit detects ambiguity in the question and provides options that encourage the student to ask a more specific question. For example, options such as, "Would you like to know more about this topic? Or would you like a specific example?" are displayed. This eliminates the ambiguity in the question and encourages the student to ask a more specific question.

[0056] The question input unit can use an emotion estimation function to analyze the emotion of a student when asking a question and provide additional information to help the student relax if the student is feeling stressed or anxious. For example, the question input unit can analyze the emotion of a student when entering a question and, if the student is feeling stressed or anxious, display a message to help the student relax. For example, the message can be, "Please relax and ask your question. Any question is fine." The question input unit can also use the emotion estimation function to analyze the emotion of a student when entering a question in real time and suggest relaxation techniques to reduce stress and anxiety. For example, the message can be, "Try taking a deep breath and entering your question again." The question input unit can also analyze the emotion of a student and, if the student is feeling stressed or anxious, provide additional information to help the student relax. For example, the message can be, "This topic is difficult, but don't rush, let's learn slowly." This can reduce the student's stress and anxiety and help them relax.

[0057] The question input unit can use voice recognition technology to enable students to input questions by voice. The question input unit, for example, uses voice recognition technology to build a system that allows students to input questions by voice. For example, a question is input by voice using a microphone, and the generation AI converts the voice into text and analyzes it. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice, and the generation AI generates an appropriate answer. For example, a question input by voice, such as "Please tell me the meaning of this mathematical formula," is converted into text and analyzed. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice in real time, and a system is built in which the generation AI provides an answer instantly. For example, a text-format answer is instantly provided in response to a question input by voice. This allows students to input questions by voice.

[0058] The question input unit can automatically suggest questions and answers if other students have asked similar questions. For example, the question input unit constructs a system that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, it displays a suggestion such as, "Other students have asked the same question. Please see this answer." The question input unit also adds a function that, when a student inputs a question, references a database of past questions and automatically suggests similar questions and their answers. For example, it displays a suggestion such as, "Past answers to this question are here." The question input unit also develops an algorithm that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, it analyzes the content of the question and suggests similar questions and their answers. This makes it possible to automatically suggest similar questions and answers.

[0059] The question input unit uses the emotion estimation function to analyze the emotions of students when they input questions in real time, and can improve the quality of questions by providing positive feedback. For example, the question input unit uses the emotion estimation function to build a system that analyzes the emotions of students when they input questions in real time and provides positive feedback. For example, a positive message such as "That's a great question!" is displayed. The question input unit also analyzes the emotions of students in real time and improves the quality of questions by providing positive feedback. For example, a message such as "This question is very important. I'll explain in more detail" is displayed. The question input unit also uses the emotion estimation function to analyze the emotions of students when they input questions, and can develop a system that improves the quality of questions by providing positive feedback. For example, a message such as "Your question will be helpful to other students." is displayed. This can improve the quality of questions.

[0060] When understanding the content of a lesson, the answer generation unit can analyze at least one non-verbal piece of information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, when the generation AI understands the content of a lesson, the answer generation unit analyzes non-verbal information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, an answer is generated based on the parts emphasized by the instructor or the important points of the slides. Furthermore, by analyzing non-verbal information, the generation AI can gain a deeper understanding of the content of the lesson and provide a more detailed answer. For example, the answer generation unit analyzes the instructor's gestures and facial expressions to generate an answer that reflects their intentions. Furthermore, when the generation AI understands the content of a lesson, the answer generation unit analyzes the slide design and the instructor's speaking style to extract important information and reflect it in the answer. For example, the answer generation unit analyzes the slide layout and color usage to provide an answer that emphasizes important points. In this way, by analyzing non-verbal information, a more detailed answer can be generated.

[0061] The answer generation unit can improve the quality of answers by referring to related academic papers and reference books when understanding the content of a class. For example, when the generation AI understands the content of a class, the answer generation unit can improve the quality of answers by referring to related external academic papers and reference books. For example, an answer can be generated by referring to the latest research papers related to the topic covered in the class. The answer generation unit can also provide more detailed and accurate answers by having the generation AI refer to external academic papers and reference books when understanding the content of a class. For example, an answer can be generated by citing specific examples and explanations from reference books. The answer generation unit can also automatically collect related external academic papers and reference books when the generation AI understands the content of a class, and generate answers based on that information. For example, an answer can be provided by referring to literature related to the topic covered in the class. This can improve the quality of answers by referring to external academic papers and reference books.

[0062] The answer generation unit uses the emotion estimation function to estimate the student's level of understanding and can provide more detailed explanations for parts where the student's understanding is shallow. The answer generation unit, for example, uses the emotion estimation function to analyze the student's level of understanding in real time and build a system that provides more detailed explanations for parts where the student's understanding is shallow. For example, if the student is confused, the answer generation unit provides detailed explanations starting from the basics. The answer generation unit also analyzes the student's level of understanding with the emotion estimation function and provides additional explanations for parts where the student's understanding is shallow. For example, if the student is feeling anxious, the answer generation unit supplements the explanation with specific examples. The answer generation unit also uses the emotion estimation function to develop a system that estimates the student's level of understanding and provides more detailed explanations for parts where the student's understanding is shallow. For example, a step-by-step explanation is provided for a topic that the student is struggling to understand. This makes it possible to provide more detailed explanations according to the student's level of understanding.

[0063] The answer generation unit can combine knowledge from different fields to provide answers from a new perspective when understanding the lesson content and generating answers. For example, when the generation AI understands the lesson content, the answer generation unit combines knowledge from different fields to provide answers from a new perspective. For example, in a mathematics class, knowledge of physics is used to explain application examples of mathematical formulas. The answer generation unit also combines knowledge from different fields, allowing the generation AI to provide answers to the lesson content from a new perspective. For example, in an economics class, knowledge of psychology is used to explain market behavior. The answer generation unit also builds a system in which the generation AI combines knowledge from different fields to understand the lesson content and provide answers from a new perspective. For example, in a biology class, knowledge of chemistry is used to explain the function of cells. This makes it possible to combine knowledge from different fields to provide answers from a new perspective.

[0064] The answer generation unit can refer to lesson videos from other instructors when understanding the lesson content and generate answers from multiple perspectives. For example, the answer generation unit builds a system in which the generation AI refers to lesson videos from other instructors when understanding the lesson content and generates answers from multiple perspectives. For example, answers are generated based on explanations from different instructors on the same topic. The answer generation unit also refers to lesson videos from other instructors so that the generation AI provides answers to the lesson content from multiple perspectives. For example, the answer generation unit compares explanations from different instructors and provides the most understandable explanation. The answer generation unit also develops a system in which the generation AI refers to lesson videos from other instructors and generates answers from multiple perspectives. For example, the lesson content from different instructors is integrated to provide a comprehensive answer. This makes it possible to provide answers from multiple perspectives by referring to lesson videos from other instructors.

[0065] The answer generation unit can use the emotion estimation function to identify topics that students are most interested in and provide additional information related to those topics. For example, the answer generation unit uses the emotion estimation function to identify topics that students are most interested in during class and build a system that provides additional information related to those topics. For example, related reference materials and videos are provided for topics that students have shown interest in. The answer generation unit also analyzes students' emotional responses in real time to identify topics that students are most interested in. For example, more detailed explanations and application examples are provided for parts that students have shown interest in. The answer generation unit also uses the emotion estimation function to develop a system that identifies topics that students are most interested in and provides additional information related to those topics. For example, related academic papers and research results are provided for topics that students have shown interest in. This makes it possible to provide additional information related to topics that students are most interested in.

[0066] The providing unit can add an interactive chat function that allows students to ask follow-up questions in real time about answers provided by the generating AI. For example, the providing unit builds a system that adds an interactive chat function that allows students to ask follow-up questions in real time about answers provided by the generating AI. For example, a student can request a more detailed explanation about an answer. The providing unit also uses the interactive chat function to allow students to provide real-time feedback on answers from the generating AI and ask follow-up questions. For example, a student can input a question such as "Can you give me a more specific example?" in response to an answer. The providing unit also develops an interactive chat function that allows students to ask follow-up questions in real time about answers provided by the generating AI. For example, a student can input a question such as "I still don't understand this part," in response to an answer, and the generating AI can provide an additional answer in response. This makes it possible to provide an interactive chat function that allows students to ask follow-up questions in real time.

[0067] The provision unit can enable the generation AI to provide background information and related topics of the answer as links when providing an answer, thereby enabling students to learn more deeply. For example, the provision unit builds a system that provides background information and related topics as links for answers provided by the generation AI. For example, it displays links to reference materials and websites related to the answer. The provision unit also enables the generation AI to provide background information and related topics of the answer as links when providing an answer, thereby enabling students to learn more deeply. For example, it displays links to academic papers and research results related to the answer. The provision unit also develops a system that enables students to learn more deeply by providing background information and related topics of the answer as links. For example, it displays links to videos and presentations related to the answer. This makes it possible to provide links that enable students to learn more deeply.

[0068] The providing unit uses the emotion estimation function to analyze the emotions of students when they read answers, and can provide additional explanations if their understanding is insufficient. The providing unit, for example, uses the emotion estimation function to analyze the emotions of students when they read answers in real time, and builds a system that provides additional explanations if their understanding is insufficient. For example, if the student is confused, the providing unit provides more detailed explanations. The providing unit also analyzes the student's emotions and provides additional explanations if their understanding is insufficient when they read answers. For example, if the student is feeling anxious, the providing unit supplements the explanation with specific examples. The providing unit also uses the emotion estimation function to develop a system that analyzes the emotions of students when they read answers, and provides additional explanations if their understanding is insufficient. For example, the system provides step-by-step explanations for topics that students are struggling to understand. This makes it possible to provide additional explanations if the student's understanding is insufficient.

[0069] The providing unit provides answers not only in text format but also in audio or video format, thereby realizing answers in a variety of formats that suit the learning style of the student. For example, the providing unit builds a system that provides answers provided by the generation AI not only in text format but also in audio or video format. For example, audio explanations and video demonstrations are provided. Furthermore, by providing answers not only in text format but also in audio or video format, answers in a variety of formats that suit the learning style of the student are realized. For example, audio explanations and video demonstrations are provided. Furthermore, the providing unit develops a system that provides answers in a variety of formats that suit the learning style of the student by providing answers provided by the generation AI not only in text format but also in audio or video format. For example, audio narration and video presentations are provided. This makes it possible to provide answers in a variety of formats that suit the learning style of the student.

[0070] The provision unit collects feedback and evaluations from other students on the answers provided by the generation AI, and can continuously improve the quality of the answers. For example, the provision unit builds a system that collects feedback and evaluations from other students on the answers provided by the generation AI, and continuously improves the quality of the answers. For example, a student evaluates an answer and improves the answer based on the evaluation results. The provision unit also collects feedback and evaluations from other students, and continuously improves the quality of the answers provided by the generation AI. For example, a student leaves a comment on an answer, and the answer is improved based on that feedback. The provision unit also develops a system that continuously improves the quality of the answers by collecting feedback and evaluations from other students on the answers provided by the generation AI. For example, a student provides feedback on an answer such as "It was easy to understand" or "I'd like to know more." This allows the system to collect feedback and evaluations from other students and continuously improve the quality of the answers.

[0071] The providing unit can use the emotion estimation function to monitor the emotions of students when they receive answers in real time and provide additional information to elicit positive emotions. For example, the providing unit uses the emotion estimation function to build a system that monitors the emotions of students when they receive answers in real time and provides additional information to elicit positive emotions. For example, if the student feels happy, further related information is provided. The providing unit also monitors the emotions of students in real time and provides additional information to elicit positive emotions. For example, if the student shows interest, more detailed explanations and application examples are provided. The providing unit also uses the emotion estimation function to develop a system that monitors the emotions of students when they receive answers and provides additional information to elicit positive emotions. For example, if the student feels satisfied, related academic papers and research results are provided. This makes it possible to provide additional information to elicit positive emotions in students.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The question input unit can have a function to automatically categorize questions when students input them. For example, questions are automatically categorized into categories such as mathematics, physics, and chemistry. The question input unit can also suggest related reference materials and past questions and answers based on the question category. For example, for a mathematics question, it can provide related formulas and example problems. The question input unit can also use an algorithm to generate specialized answers based on the question category. For example, for a physics question, an AI with specialized physics knowledge can generate an answer. This makes it possible to provide an appropriate answer according to the question category.

[0074] The analysis unit can analyze the student's learning style and, based on that, more accurately analyze the intent of the question. For example, if the student has a visual learning style, it can generate an answer using diagrams or graphs. The analysis unit can also select an appropriate answer format depending on the student's learning style. For example, if a student has an auditory learning style, it can provide an answer in audio format. The analysis unit can also analyze the student's learning style and, based on that, use an algorithm to more deeply understand the intent of the question. For example, if a student has a hands-on learning style, it can provide an answer related to experiments or practical training. This makes it possible to provide an appropriate answer according to the student's learning style.

[0075] The question input unit may have a function to automatically evaluate the difficulty of a question when a student inputs a question. For example, the question may be classified as beginner, intermediate, or advanced. The question input unit may also provide an appropriate answer based on the difficulty of the question. For example, a basic explanation may be provided for a beginner question, and a more detailed explanation may be provided for an intermediate or advanced question. The question input unit may also use an algorithm to evaluate the difficulty of the question. For example, the content and context of the question may be analyzed to evaluate the difficulty. This allows an appropriate answer to be provided according to the difficulty of the question.

[0076] The question input unit can use the emotion estimation function to analyze the emotion of a student when asking a question and provide feedback to elicit positive emotions. For example, when a student inputs a question, the emotion estimation function analyzes the student's emotion and provides positive feedback. For example, a message such as "That's a great question!" is displayed. The question input unit also uses the emotion estimation function to analyze the emotion of a student when inputting a question in real time and provide advice to elicit positive emotions. For example, a message such as "This question is very important. I'll explain in more detail" is displayed. The question input unit also uses the emotion estimation function to analyze the student's emotion and provide interactive prompts to elicit positive emotions. For example, a message such as "Your question will be helpful to other students." is displayed. This can elicit positive emotions from students and improve the quality of questions.

[0077] The question input unit can use voice recognition technology to enable students to input questions by voice. For example, a question can be input by voice using a microphone, and the generation AI converts the voice into text and analyzes it. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice, and the generation AI generates an appropriate answer. For example, a question input by voice, such as "Please tell me the meaning of this mathematical formula," can be converted into text and analyzed. The question input unit also uses voice recognition technology to analyze the voice data when a student inputs a question by voice in real time, and a generation AI can build a system that provides an answer instantly. For example, a text-format answer can be provided instantly in response to a question input by voice. This allows students to input questions by voice.

[0078] The question input unit can automatically suggest questions and answers if other students have asked similar questions. For example, a system can be built that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, a suggestion such as, "Other students have asked the same question. Please see the answer here." is displayed. The question input unit also adds a function that, when a student inputs a question, references a database of past questions and automatically suggests similar questions and their answers. For example, a suggestion such as, "Past answers to this question are here." is displayed. The question input unit also develops an algorithm that automatically suggests questions and answers if other students have asked similar questions when a question is being input. For example, the system analyzes the content of the question and suggests similar questions and their answers. This makes it possible to automatically suggest similar questions and answers.

[0079] The question input unit uses the emotion estimation function to analyze the emotions of students when they input questions in real time, and can improve the quality of questions by providing positive feedback. For example, a system is constructed using the emotion estimation function to analyze the emotions of students when they input questions in real time and provide positive feedback. For example, a positive message such as "That's a great question!" is displayed. The question input unit also analyzes the emotions of students in real time and improves the quality of questions by providing positive feedback. For example, a message such as "This question is very important. I'll explain in more detail" is displayed. The question input unit also uses the emotion estimation function to analyze the emotions of students when they input questions, and can improve the quality of questions by providing positive feedback. For example, a message such as "Your question will be helpful to other students" is displayed. This makes it possible to improve the quality of questions.

[0080] When understanding the content of a lesson, the answer generation unit can analyze at least one non-verbal piece of information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, when the generation AI understands the content of a lesson, it analyzes non-verbal information, such as the instructor's speaking style or the slide design, to generate a more detailed answer. For example, it generates an answer based on the parts emphasized by the instructor or the important points of the slides. Furthermore, by analyzing non-verbal information, the generation AI can gain a deeper understanding of the content of the lesson and provide a more detailed answer. For example, it can analyze the instructor's gestures and facial expressions to generate an answer that reflects their intentions. Furthermore, when the generation AI understands the content of a lesson, the answer generation unit can analyze the slide design and the instructor's speaking style to extract important information and reflect it in the answer. For example, it can analyze the slide layout and color usage to provide an answer that emphasizes important points. In this way, by analyzing non-verbal information, a more detailed answer can be generated.

[0081] The answer generation unit can improve the quality of answers by referring to related academic papers and reference books when understanding the content of a class. For example, when the generation AI understands the content of a class, it can refer to related external academic papers and reference books to improve the quality of answers. For example, it can generate answers by referring to the latest research papers related to the topics covered in the class. The answer generation unit also provides more detailed and accurate answers by having the generation AI refer to external academic papers and reference books when understanding the content of a class. For example, it can generate answers by citing specific examples and explanations from reference books. The answer generation unit also automatically collects related external academic papers and reference books when the generation AI understands the content of a class and generates answers based on that information. For example, it can provide answers by referring to literature related to the topics covered in the class. This allows the quality of answers to be improved by referring to external academic papers and reference books.

[0082] The answer generation unit uses the emotion estimation function to estimate the student's level of understanding and can provide more detailed explanations for parts where the student's understanding is shallow. For example, a system is constructed that uses the emotion estimation function to analyze the student's level of understanding in real time and provide more detailed explanations for parts where the student's understanding is shallow. For example, if the student is confused, a detailed explanation is provided starting from the basics. The answer generation unit also analyzes the student's level of understanding using the emotion estimation function and provides additional explanations for parts where the student's understanding is shallow. For example, if the student is feeling anxious, the explanation is supplemented using specific examples. The answer generation unit also uses the emotion estimation function to develop a system that estimates the student's level of understanding and provides more detailed explanations for parts where the student's understanding is shallow. For example, a step-by-step explanation is provided for a topic that the student is struggling to understand. This makes it possible to provide more detailed explanations according to the student's level of understanding.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The question input unit inputs a question from a student. For example, the student inputs the question in text format. The question input unit can also input a question using voice input. For example, the student inputs the question by voice using a microphone. Step 2: The analysis unit analyzes the question input by the question input unit. For example, the generation AI analyzes the content of the question using natural language processing technology. The generation AI can also analyze the intent of the question using a machine learning algorithm. For example, the generation AI analyzes the intent of the question by referring to past question history and the student's learning history. Step 3: The answer generation unit generates an answer based on the question analyzed by the analysis unit. For example, the generation AI references a database to generate an appropriate answer. The generation AI can also generate answers using AI generation technology. For example, the generation AI understands the content of the lesson and generates an appropriate answer. Step 4: The providing unit provides the answer generated by the answer generating unit to the student. For example, the providing unit provides the answer in text format. The providing unit can also provide the answer in audio or video format. For example, the providing unit plays the answer generated by the generation AI as audio. The providing unit can also display the answer generated by the generation AI as a video.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question input section for inputting questions from students; an analysis unit that analyzes the question input by the question input unit; an answer generation unit that generates an answer based on the question analyzed by the analysis unit; a providing unit that provides the answer generated by the answer generating unit to the student. A system characterized by:

2. The analysis unit The intent of the question is analyzed by referring to the question history and the learning history of the student.

2. The system of claim 1.

3. The question input unit Detecting ambiguity in the question and prompting the student to ask a specific question.

2. The system of claim 1.

4. The question input unit Analyze the student's emotions when asking questions and provide additional information to help them relax if they are feeling stressed or anxious 2. The system of claim 1.

5. The question input unit Using voice recognition technology to allow the student to enter the question by voice 2. The system of claim 1.

Images