System

Abstract

An object of the system according to the embodiment is to automatically generate a question from a learning material and efficiently check the degree of understanding.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives a learning material. The analysis unit analyzes the learning material input by the reception unit and extracts an important point. The generation unit generates a problem based on the important point extracted by the analysis unit. The providing unit provides the problem generated by the generating unit.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 the drawback of requiring a lot of effort to create questions from learning materials, making it difficult to efficiently check students' understanding.

[0005] The system according to the embodiment aims to automatically generate questions from learning materials and efficiently check the level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs learning materials. The analyzing unit analyzes the learning materials input by the receiving unit and extracts important points. The generating unit generates questions based on the important points extracted by the analyzing unit. The providing unit provides the questions generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate questions from learning materials and efficiently check the level of understanding. [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) An automatic question generation system according to an embodiment of the present invention automatically analyzes learning materials, and a generation AI creates and provides questions. The automatic question generation system inputs learning materials, analyzes them, extracts important points, and generates and provides questions. For example, the automatic question generation system inputs learning materials such as e-learning materials or business procedure manuals in text format. The generation AI then analyzes the input learning materials and extracts important points. For example, particularly important procedures and important points from the business procedure manuals are extracted. The generation AI then creates questions based on the extracted important points. Questions are generated in various formats, such as multiple-choice and written questions. For example, multiple-choice questions are created to require students to select the correct procedure or select important points. Writing questions are created to require students to explain procedures or describe important points. This allows the automatic question generation system to deepen students' understanding and reduce the effort required for question creation. This allows the automatic question generation system to deepen students' understanding and reduce the effort required for question creation. For example, by inputting e-learning materials into the AI ​​generator and having the learner solve the generated problems, it is possible to check the learner's level of understanding. Also, by inputting work procedure manuals into the AI ​​generator and having employees solve the generated problems, it is possible to check the learner's level of understanding of the work procedures.

[0029] An automatic question generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit inputs learning materials. Examples of learning materials include, but are not limited to, e-learning materials and work procedure manuals. The receiving unit inputs the learning materials, for example, in text format. The analysis unit uses a generation AI to analyze the learning materials input by the receiving unit and extract important points. For example, the generation AI uses natural language processing technology to understand the content of the learning materials and extract important points. The generation unit uses the generation AI to generate questions based on the important points extracted by the analysis unit. The questions are generated in various formats, for example, multiple-choice and written format. For example, when generating multiple-choice questions, the generation AI creates questions that require the learner to select the correct procedure or select important points. Furthermore, when generating written questions, the generation AI creates questions that require the learner to explain the procedure or describe important points. The providing unit provides the questions generated by the generation unit to the learner. For example, the providing unit provides the generated questions to the learner via a web application or a mobile application. As a result, the automatic question generation system according to the embodiment can deepen the learner's understanding and reduce the effort required to create questions.

[0030] The reception unit can input learning materials for e-learning learning materials or business procedure manuals in text format. Examples of text formats include, but are not limited to, plain text, rich text, and Markdown format. For example, the reception unit inputs e-learning learning materials in plain text format. The reception unit can also input business procedure manuals in rich text format. Furthermore, the reception unit can also accept learning materials input in Markdown format. Thus, inputting learning materials in text format makes it easier for the generation AI to analyze.

[0031] The analysis unit can understand the content of the learning material and extract important points. The analysis unit, for example, uses a generative AI to understand the content of the learning material. For example, the generative AI analyzes the content of the learning material using natural language processing technology and extracts important points. The analysis unit can also understand the content of the learning material using semantic analysis technology. For example, the generative AI evaluates the importance of keywords and concepts that appear frequently in the learning material and extracts important points. The analysis unit can also understand the content of the learning material using context analysis technology. For example, the generative AI analyzes the context of the learning material and extracts important points. This allows for the generation of effective questions by extracting important points from the learning material.

[0032] The generation unit can generate multiple-choice or written questions based on the extracted important points. The generation unit generates questions based on the extracted important points, for example, using a generation AI. For example, when generating multiple-choice questions, the generation AI creates questions that require the learner to select the correct procedure or select a point to note. Furthermore, when generating written questions, the generation AI creates questions that require the learner to explain a procedure or describe a point to note. Multiple-choice formats include, but are not limited to, single-choice and multiple-choice questions. Writing formats include, but are not limited to, short answer and long answer questions. This allows the generation of questions in various formats to check the learner's understanding from multiple angles.

[0033] The providing unit can provide the generated questions to the learner. The providing unit, for example, provides the generated questions to the learner through a web application or a mobile application. For example, the providing unit displays the generated questions on a web browser so that the learner can solve the questions. The providing unit can also provide the generated questions to the learner through a mobile application. For example, the providing unit enables the learner to solve the questions using a smartphone or tablet device. Furthermore, the providing unit can print the generated questions and provide them on paper. For example, the providing unit prints the generated questions on a printer and distributes them to the learner. In this way, by providing the generated questions to the learner, the learner's level of understanding can be checked.

[0034] The generation unit can generate questions that require the learner to select the correct procedure and questions that require the learner to select important points. The generation unit, for example, uses a generation AI to generate questions that require the learner to select the correct procedure. For example, the generation AI extracts particularly important procedures from a business procedure manual and creates questions that require the learner to select those procedures. The generation unit can also use the generation AI to generate questions that require the learner to select important points. For example, the generation AI extracts particularly important important points from a business procedure manual and creates questions that require the learner to select those important points. Examples of correct procedures include, but are not limited to, business procedure manuals and operation manuals. Examples of important points include, but are not limited to, safety precautions and operational precautions. This allows the learner to understand the correct procedures and important points.

[0035] The generation unit can generate questions that require a learner to explain procedures and questions that require a learner to describe important points. The generation unit, for example, uses a generation AI to generate questions that require a learner to explain procedures. For example, the generation AI extracts particularly important procedures from a business procedure manual and creates questions that require a learner to explain those procedures. The generation unit can also use the generation AI to generate questions that require a learner to describe important points. For example, the generation AI extracts particularly important points of caution from a business procedure manual and creates questions that require a learner to describe those important points. Examples of methods for explaining procedures include, but are not limited to, step-by-step explanations and flowcharts. Examples of methods for describing important points include, but are not limited to, bullet points and detailed explanations. This allows a learner to gain a deeper understanding of procedures and important points.

[0036] When inputting learning materials, the reception unit can analyze the user's past learning history and select the optimal input method. The reception unit, for example, uses a generation AI to analyze the user's past learning history. For example, the generation AI analyzes the user's past study time, learning content, grades, etc. The reception unit also selects the optimal input method based on the user's past learning history. For example, if the user has previously input learning materials in text format, the reception unit can preferentially suggest text format. Furthermore, if the user has previously used voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user has previously studied using images, the reception unit can preferentially suggest image input. This makes it possible to provide the optimal input method based on the user's past learning history.

[0037] When inputting learning materials, the reception unit can filter the learning materials based on the user's current learning progress and areas of interest. The reception unit, for example, uses a generation AI to analyze the user's current learning progress. For example, the generation AI analyzes the number of assignments the user has completed and the results of comprehension tests. The reception unit also identifies the user's areas of interest. For example, the generation AI analyzes the subjects selected by the user and their browsing history. The reception unit filters the learning materials based on the user's current learning progress and areas of interest. For example, the reception unit prioritizes input of materials related to the area the user is currently studying. The reception unit can also suggest the next material to learn based on the user's learning progress. Furthermore, the reception unit can filter and provide related learning materials based on the user's areas of interest. This makes it possible to provide optimal learning materials based on the user's learning progress and areas of interest.

[0038] The reception unit can select the optimal input means according to the user's input method when inputting learning materials. The reception unit, for example, uses a generation AI to analyze the user's input method. For example, the generation AI analyzes input methods (voice, text, image, etc.) that the user has used in the past. The reception unit selects the optimal input means according to the user's input method. For example, if the user desires voice input, the reception unit can provide voice input with priority. Also, if the user desires text input, the reception unit can provide text input with priority. Furthermore, if the user desires image input, the reception unit can provide image input with priority. This makes it possible to provide the optimal input means according to the user's desired input method.

[0039] When inputting learning materials, the reception unit can prioritize inputting highly relevant materials based on the user's geographical location information. The reception unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI acquires the user's location information using GPS data or an IP address. The reception unit prioritizes inputting highly relevant learning materials based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize providing learning materials related to that area. Furthermore, if the user is traveling, the reception unit can prioritize providing learning materials related to the travel destination. Furthermore, if the user is in a specific facility, the reception unit can prioritize providing learning materials related to that facility. This makes it possible to provide optimal learning materials based on the user's geographical location information.

[0040] When inputting learning materials, the reception unit can analyze the user's social media activity and input highly relevant materials. The reception unit, for example, uses a generation AI to analyze the user's social media activity. For example, the generation AI analyzes the user's posted content, the number of likes, the number of followers, etc. The reception unit inputs highly relevant learning materials based on the user's social media activity. For example, the reception unit provides learning materials related to content shared by the user on social media. The reception unit can also provide learning materials that are likely to be of interest to the user based on their social media activity. Furthermore, the reception unit can provide related learning materials by taking into account the activities of the user's friends on social media. This makes it possible to provide optimal learning materials based on the user's social media activity.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting learning materials. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI analyzes the user's survey results, comments, etc. The reception unit customizes the input method based on the user's past feedback. For example, it can provide input methods that the user has preferred in the past with priority. It can also avoid input methods that the user has been dissatisfied with in the past. Furthermore, it can suggest the optimal input method based on the user's past feedback. This makes it possible to provide the optimal input method based on the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the learning material. The analysis unit, for example, uses a generation AI to evaluate the importance of the learning material. For example, the generation AI evaluates the frequency and impact of the learning material. The analysis unit adjusts the level of detail of the analysis based on the importance of the learning material. For example, a detailed analysis can be performed on important learning material. On the other hand, a brief analysis can be performed on less important learning material. Furthermore, the depth of the analysis can be adjusted according to the importance of the learning material. This makes it possible to provide optimal analysis results according to the importance of the learning material.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the learning material. The analysis unit, for example, uses a generation AI to identify the category of the learning material. For example, the generation AI identifies the subject, topic, theme, etc. of the learning material. The analysis unit applies different analysis algorithms depending on the category of the learning material. For example, a specialized analysis algorithm can be applied to technical learning material. A simple analysis algorithm can be applied to general learning material. Furthermore, the optimal analysis algorithm can be selected depending on the category of the learning material. This makes it possible to apply the optimal analysis algorithm depending on the category of the learning material.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the generation AI analyzes the user's past analysis reports and evaluation results. The analysis unit improves the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. In addition, the analysis trends can be grasped from the user's past analysis results to improve accuracy. Furthermore, the user's past analysis results can be used as feedback to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0045] During analysis, the analysis unit can determine the analysis priority based on the submission time of the learning material. The analysis unit, for example, uses a generation AI to evaluate the submission time of the learning material. For example, the generation AI evaluates the submission deadline or submission date of the learning material. The analysis unit determines the analysis priority based on the submission time of the learning material. For example, learning material with an approaching deadline can be analyzed first. Also, learning material with a more distant submission time can be postponed. Furthermore, the analysis priority can be adjusted depending on the submission time. This makes it possible to provide optimal analysis results depending on the submission time of the learning material.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the learning materials. The analysis unit, for example, uses a generation AI to evaluate the relevance of the learning materials. For example, the generation AI evaluates the similarity of the topics of the learning materials and the relevance of the content. The analysis unit adjusts the order of analysis based on the relevance of the learning materials. For example, it prioritizes analysis of highly relevant learning materials. It can also postpone analysis of less relevant learning materials. Furthermore, the order of analysis can be adjusted according to the relevance of the learning materials. This makes it possible to provide optimal analysis results according to the relevance of the learning materials.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI evaluates the user's past learning history, test results, etc. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, a user with high level of expertise can be provided with an analysis that makes heavy use of technical terms. Furthermore, a user with low level of expertise can be provided with an analysis that uses simple terms. Furthermore, the use of technical terms in the analysis can be adjusted according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0048] The generation unit can adjust the level of detail of the questions based on the importance of the extracted important points during generation. The generation unit, for example, uses a generation AI to evaluate the importance of the extracted important points. For example, the generation AI evaluates the frequency and impact of the important points. The generation unit adjusts the level of detail of the questions based on the importance of the extracted important points. For example, detailed questions can be generated for important points. Concise questions can also be generated for points that are not so important. Furthermore, the level of detail of the questions can be adjusted according to the importance of the extracted points. This makes it possible to provide optimal questions based on the important points.

[0049] The generation unit can apply different generation algorithms depending on the problem category during generation. The generation unit, for example, uses a generation AI to identify the problem category. For example, the generation AI identifies the subject, topic, theme, etc. of the problem. The generation unit applies different generation algorithms depending on the problem category. For example, a specialized generation algorithm can be applied to technical problems. Also, a simple generation algorithm can be applied to general problems. Furthermore, the optimal generation algorithm can be selected depending on the problem category. This makes it possible to apply the optimal generation algorithm depending on the problem category.

[0050] During generation, the generation unit can improve the accuracy of generation by referring to the user's past question answer results. The generation unit, for example, uses a generation AI to analyze the user's past question answer results. For example, the generation AI analyzes the user's past correct answer rate and answer time. The generation unit improves the accuracy of generation based on the user's past question answer results. For example, the generation unit improves the accuracy of generation based on the user's past question answer results. In addition, the user's past answer results can be used to understand question trends and improve accuracy. Furthermore, the user's past answer results can be used as feedback to improve the accuracy of generation. This makes it possible to improve the accuracy of generation based on the user's past answer results.

[0051] At the time of generation, the generation unit can determine the priority of questions based on the submission time of the learning materials. The generation unit, for example, uses a generation AI to evaluate the submission time of the learning materials. For example, the generation AI evaluates the submission deadline or submission date of the learning materials. The generation unit determines the priority of questions based on the submission time of the learning materials. For example, questions based on learning materials with an upcoming deadline can be generated preferentially. Also, questions based on learning materials with a more distant submission time can be postponed. Furthermore, the generation priority of questions can be adjusted depending on the submission time. This makes it possible to provide optimal questions depending on the submission time of the learning materials.

[0052] The generation unit can adjust the order of questions based on the relevance of the learning materials when generating them. The generation unit, for example, uses a generation AI to evaluate the relevance of the learning materials. For example, the generation AI evaluates the similarity of the topics of the learning materials or the relevance of the contents. The generation unit adjusts the order of questions based on the relevance of the learning materials. For example, questions based on highly relevant learning materials are generated preferentially. Also, questions based on less relevant learning materials can be postponed. Furthermore, the generation order of questions can be adjusted according to the relevance of the learning materials. This makes it possible to provide optimal questions according to the relevance of the learning materials.

[0053] The generation unit can adjust the use of technical terminology in the questions during generation according to the user's level of expertise. The generation unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI evaluates the user's past learning history, test results, etc. The generation unit adjusts the use of technical terminology in the questions according to the user's level of expertise. For example, for a user with high expertise, questions that use a lot of technical terminology can be generated. Also, for a user with low expertise, questions that use simple terminology can be generated. Furthermore, the use of technical terminology in the questions can be adjusted according to the user's level of expertise. This makes it possible to provide the most suitable questions according to the user's level of expertise.

[0054] When providing the question, the providing unit can select the optimal providing method by referring to the user's past question answer history. The providing unit, for example, uses a generation AI to analyze the user's past question answer history. For example, the generation AI analyzes the user's past correct answer rate, answer time, etc. The providing unit selects the optimal providing method based on the user's past question answer history. For example, the providing unit preferentially provides a providing method that the user has previously preferred. It can also avoid a providing method that the user has previously been dissatisfied with. Furthermore, it can suggest the optimal providing method based on the user's past answer history. This makes it possible to provide the optimal providing method based on the user's past answer history.

[0055] The providing unit can customize the content to be provided based on the user's current learning situation when providing the content. The providing unit, for example, uses a generation AI to analyze the user's current learning situation. For example, the generation AI analyzes the user's progress and level of understanding. The providing unit customizes the content to be provided based on the user's current learning situation. For example, the providing unit can provide the next problem to be learned based on the user's current learning progress. In addition, the providing unit can provide problems of an appropriate difficulty level based on the user's current level of understanding. Furthermore, the providing unit can provide related problems based on the user's current areas of interest. This makes it possible to provide the most appropriate problems based on the user's current learning situation.

[0056] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. The providing unit, for example, uses a generation AI to analyze user feedback. For example, the generation AI analyzes user survey results and comments, etc. The providing unit improves the delivery method based on user feedback. For example, the delivery method is improved based on user feedback. Furthermore, problems with the delivery method can be identified and improved from user feedback. Furthermore, the delivery method can be customized by referring to user feedback. This makes it possible to provide the optimal delivery method based on user feedback.

[0057] The providing unit can select the optimal provision method by taking into consideration the user's geographical location information when providing the questions. The providing unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI acquires the user's location information using GPS data or an IP address. The providing unit selects the optimal provision method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide questions related to that area. Also, if the user is traveling, the providing unit can provide questions related to the travel destination. Furthermore, if the user is in a specific facility, the providing unit can provide questions related to that facility. This makes it possible to provide optimal questions based on the user's geographical location information.

[0058] The providing unit can analyze the user's social media activity and customize the content to be provided when providing the content. The providing unit, for example, uses a generation AI to analyze the user's social media activity. For example, the generation AI analyzes the user's posted content, the number of likes, the number of followers, etc. The providing unit customizes the content to be provided based on the user's social media activity. For example, the providing unit can provide questions related to content shared by the user on social media. It can also provide questions that are likely to interest the user based on their social media activity. Furthermore, it can provide related questions based on the activity of the user's friends on social media. This makes it possible to provide optimal questions based on the user's social media activity.

[0059] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI analyzes the user's survey results, comments, etc. The providing unit customizes the delivery method based on the user's past feedback. For example, the delivery method is customized based on the user's past feedback. In addition, the delivery unit can identify and customize problems with the delivery method from the user's past feedback. Furthermore, the delivery method can be optimized by referring to the user's past feedback. This makes it possible to provide the optimal delivery method based on the user's past feedback.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When understanding the content of learning materials, the analysis unit can refer to the user's past learning history and perform an analysis that is individually optimized. For example, if the user previously had a deep understanding of a particular topic, the analysis unit can extract important points related to that topic in more detail. Also, for topics that the user previously struggled with, the analysis unit can extract more concise and easy-to-understand points. Furthermore, based on the user's learning history, the analysis algorithm can be adjusted to perform an analysis that is optimized for each individual learning style. This allows for more effective questions to be generated based on the user's past learning history.

[0062] When inputting learning materials, the reception unit can monitor the user's current learning progress in real time and prompt the user to input learning materials at an appropriate time. For example, the reception unit can prompt the user to input new learning materials immediately after completing a specific task. Also, if the user has suspended learning for a certain period of time, the reception unit can send a reminder to prompt the user to input learning materials. Furthermore, the difficulty level of the learning materials can be adjusted according to the user's learning progress, and materials of an appropriate level can be provided. This allows the user to input learning materials at the optimal time according to their learning progress.

[0063] When generating questions based on the extracted important points, the generator can select a question format that suits the user's learning style. For example, questions using diagrams and graphs can be generated for visual learners. Questions using audio can be generated for auditory learners. Furthermore, interactive questions can be generated for tactile learners. This makes it possible to provide the most suitable questions for the user's learning style.

[0064] When generating questions, the generator can refer to the user's past answer history and generate individually optimized questions. For example, questions with more detailed explanations can be generated for topics on which the user has a low rate of correct answers in the past. Also, questions with a higher level of difficulty can be generated for topics in which the user excels. Furthermore, the length and format of the questions can be adjusted based on the user's answer time. This makes it possible to provide more effective questions based on the user's past answer history.

[0065] When providing the generated questions to the learner, the providing unit can select the optimal method of providing them by taking into account the user's geographical location information. For example, if the user is in a specific area, questions related to that area can be provided. If the user is traveling, questions related to the travel destination can be provided. Furthermore, if the user is in a specific facility, questions related to that facility can be provided. In this way, optimal questions can be provided based on the user's geographical location information.

[0066] When understanding the content of the learning materials, the analysis unit can analyze the user's social media activities and extract highly relevant points. For example, it can extract points related to the content the user has shared on social media. It can also extract points that are likely to be of interest from the user's social media activities. It can also extract related points by referring to the activities of the user's friends on social media. This makes it possible to extract the most appropriate points based on the user's social media activities.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit inputs learning materials. The learning materials include, but are not limited to, e-learning materials and work procedure manuals. The reception unit inputs the learning materials in text format, for example. Step 2: The analysis unit uses the generation AI to analyze the learning materials entered by the reception unit and extract important points. For example, the generation AI uses natural language processing technology to understand the content of the learning materials and extract important points. Step 3: The generation unit uses the generation AI to generate questions based on the important points extracted by the analysis unit. Questions are generated in various formats, such as multiple-choice and written questions. For example, when generating multiple-choice questions, the generation AI creates questions that require the user to select the correct procedure or select important points. When generating written questions, the generation AI also creates questions that require the user to explain the procedure or describe important points. Step 4: The providing unit provides the questions generated by the generating unit to the learner. For example, the providing unit provides the generated questions to the learner through a web application or a mobile application.

[0069] (Example 2) An automatic question generation system according to an embodiment of the present invention automatically analyzes learning materials, and a generation AI creates and provides questions. The automatic question generation system inputs learning materials, analyzes them, extracts important points, and generates and provides questions. For example, the automatic question generation system inputs learning materials such as e-learning materials or business procedure manuals in text format. The generation AI then analyzes the input learning materials and extracts important points. For example, particularly important procedures and important points from the business procedure manuals are extracted. The generation AI then creates questions based on the extracted important points. Questions are generated in various formats, such as multiple-choice and written questions. For example, multiple-choice questions are created to require students to select the correct procedure or select important points. Writing questions are created to require students to explain procedures or describe important points. This allows the automatic question generation system to deepen students' understanding and reduce the effort required for question creation. This allows the automatic question generation system to deepen students' understanding and reduce the effort required for question creation. For example, by inputting e-learning materials into the AI ​​generator and having the learner solve the generated problems, it is possible to check the learner's level of understanding. Also, by inputting work procedure manuals into the AI ​​generator and having employees solve the generated problems, it is possible to check the learner's level of understanding of the work procedures.

[0070] An automatic question generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit inputs learning materials. Examples of learning materials include, but are not limited to, e-learning materials and work procedure manuals. The receiving unit inputs the learning materials, for example, in text format. The analysis unit uses a generation AI to analyze the learning materials input by the receiving unit and extract important points. For example, the generation AI uses natural language processing technology to understand the content of the learning materials and extract important points. The generation unit uses the generation AI to generate questions based on the important points extracted by the analysis unit. The questions are generated in various formats, for example, multiple-choice and written format. For example, when generating multiple-choice questions, the generation AI creates questions that require the learner to select the correct procedure or select important points. Furthermore, when generating written questions, the generation AI creates questions that require the learner to explain the procedure or describe important points. The providing unit provides the questions generated by the generation unit to the learner. For example, the providing unit provides the generated questions to the learner via a web application or a mobile application. As a result, the automatic question generation system according to the embodiment can deepen the learner's understanding and reduce the effort required to create questions.

[0071] The reception unit can input learning materials for e-learning learning materials or business procedure manuals in text format. Examples of text formats include, but are not limited to, plain text, rich text, and Markdown format. For example, the reception unit inputs e-learning learning materials in plain text format. The reception unit can also input business procedure manuals in rich text format. Furthermore, the reception unit can also accept learning materials input in Markdown format. Thus, inputting learning materials in text format makes it easier for the generation AI to analyze.

[0072] The analysis unit can understand the content of the learning material and extract important points. The analysis unit, for example, uses a generative AI to understand the content of the learning material. For example, the generative AI analyzes the content of the learning material using natural language processing technology and extracts important points. The analysis unit can also understand the content of the learning material using semantic analysis technology. For example, the generative AI evaluates the importance of keywords and concepts that appear frequently in the learning material and extracts important points. The analysis unit can also understand the content of the learning material using context analysis technology. For example, the generative AI analyzes the context of the learning material and extracts important points. This allows for the generation of effective questions by extracting important points from the learning material.

[0073] The generation unit can generate multiple-choice or written questions based on the extracted important points. The generation unit generates questions based on the extracted important points, for example, using a generation AI. For example, when generating multiple-choice questions, the generation AI creates questions that require the learner to select the correct procedure or select a point to note. Furthermore, when generating written questions, the generation AI creates questions that require the learner to explain a procedure or describe a point to note. Multiple-choice formats include, but are not limited to, single-choice and multiple-choice questions. Writing formats include, but are not limited to, short answer and long answer questions. This allows the generation of questions in various formats to check the learner's understanding from multiple angles.

[0074] The providing unit can provide the generated questions to the learner. The providing unit, for example, provides the generated questions to the learner through a web application or a mobile application. For example, the providing unit displays the generated questions on a web browser so that the learner can solve the questions. The providing unit can also provide the generated questions to the learner through a mobile application. For example, the providing unit enables the learner to solve the questions using a smartphone or tablet device. Furthermore, the providing unit can print the generated questions and provide them on paper. For example, the providing unit prints the generated questions on a printer and distributes them to the learner. In this way, by providing the generated questions to the learner, the learner's level of understanding can be checked.

[0075] The generation unit can generate questions that require the learner to select the correct procedure and questions that require the learner to select important points. The generation unit, for example, uses a generation AI to generate questions that require the learner to select the correct procedure. For example, the generation AI extracts particularly important procedures from a business procedure manual and creates questions that require the learner to select those procedures. The generation unit can also use the generation AI to generate questions that require the learner to select important points. For example, the generation AI extracts particularly important important points from a business procedure manual and creates questions that require the learner to select those important points. Examples of correct procedures include, but are not limited to, business procedure manuals and operation manuals. Examples of important points include, but are not limited to, safety precautions and operational precautions. This allows the learner to understand the correct procedures and important points.

[0076] The generation unit can generate questions that require a learner to explain procedures and questions that require a learner to describe important points. The generation unit, for example, uses a generation AI to generate questions that require a learner to explain procedures. For example, the generation AI extracts particularly important procedures from a business procedure manual and creates questions that require a learner to explain those procedures. The generation unit can also use the generation AI to generate questions that require a learner to describe important points. For example, the generation AI extracts particularly important points of caution from a business procedure manual and creates questions that require a learner to describe those important points. Examples of methods for explaining procedures include, but are not limited to, step-by-step explanations and flowcharts. Examples of methods for describing important points include, but are not limited to, bullet points and detailed explanations. This allows a learner to gain a deeper understanding of procedures and important points.

[0077] The automatic question generation system further includes a reception unit that estimates the user's emotions and adjusts the timing of inputting the learning materials based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI estimates the user's emotions using facial expression recognition technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI analyzes the tone and speed of the user's voice to estimate the emotions. The reception unit can also estimate the user's emotions using text analysis technology. For example, the generation AI analyzes the user's text input to estimate the emotions. The reception unit adjusts the timing of inputting the learning materials based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input the learning materials during a time when the user can relax. If the user is concentrating, the reception unit can prompt the user to input the learning materials at that time. If the user is tired, the reception unit can prompt the user to input the learning materials after a break. This allows the user to input the learning materials at the optimal time depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] When inputting learning materials, the reception unit can analyze the user's past learning history and select the optimal input method. The reception unit, for example, uses a generation AI to analyze the user's past learning history. For example, the generation AI analyzes the user's past study time, learning content, grades, etc. The reception unit also selects the optimal input method based on the user's past learning history. For example, if the user has previously input learning materials in text format, the reception unit can preferentially suggest text format. Furthermore, if the user has previously used voice input, the reception unit can preferentially suggest voice input. Furthermore, if the user has previously studied using images, the reception unit can preferentially suggest image input. This makes it possible to provide the optimal input method based on the user's past learning history.

[0079] When inputting learning materials, the reception unit can filter the learning materials based on the user's current learning progress and areas of interest. The reception unit, for example, uses a generation AI to analyze the user's current learning progress. For example, the generation AI analyzes the number of assignments the user has completed and the results of comprehension tests. The reception unit also identifies the user's areas of interest. For example, the generation AI analyzes the subjects selected by the user and their browsing history. The reception unit filters the learning materials based on the user's current learning progress and areas of interest. For example, the reception unit prioritizes input of materials related to the area the user is currently studying. The reception unit can also suggest the next material to learn based on the user's learning progress. Furthermore, the reception unit can filter and provide related learning materials based on the user's areas of interest. This makes it possible to provide optimal learning materials based on the user's learning progress and areas of interest.

[0080] The reception unit can select the optimal input means according to the user's input method when inputting learning materials. The reception unit, for example, uses a generation AI to analyze the user's input method. For example, the generation AI analyzes input methods (voice, text, image, etc.) that the user has used in the past. The reception unit selects the optimal input means according to the user's input method. For example, if the user desires voice input, the reception unit can provide voice input with priority. Also, if the user desires text input, the reception unit can provide text input with priority. Furthermore, if the user desires image input, the reception unit can provide image input with priority. This makes it possible to provide the optimal input means according to the user's desired input method.

[0081] The reception unit can estimate the user's emotions and prioritize the learning materials to be input based on the estimated user's emotions. The reception unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI estimates the user's emotions using facial expression recognition technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotions using text analysis technology. For example, the generation AI analyzes the user's text input to estimate the emotion. The reception unit prioritizes the learning materials to be input based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can prioritize providing easy learning materials. Also, if the user is concentrating, the reception unit can prioritize providing more difficult learning materials. Furthermore, if the user is relaxed, the reception unit can prioritize providing interesting learning materials. This makes it possible to prioritize providing the most appropriate learning materials according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] When inputting learning materials, the reception unit can prioritize inputting highly relevant materials based on the user's geographical location information. The reception unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI acquires the user's location information using GPS data or an IP address. The reception unit prioritizes inputting highly relevant learning materials based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize providing learning materials related to that area. Furthermore, if the user is traveling, the reception unit can prioritize providing learning materials related to the travel destination. Furthermore, if the user is in a specific facility, the reception unit can prioritize providing learning materials related to that facility. This makes it possible to provide optimal learning materials based on the user's geographical location information.

[0083] When inputting learning materials, the reception unit can analyze the user's social media activity and input highly relevant materials. The reception unit, for example, uses a generation AI to analyze the user's social media activity. For example, the generation AI analyzes the user's posted content, the number of likes, the number of followers, etc. The reception unit inputs highly relevant learning materials based on the user's social media activity. For example, the reception unit provides learning materials related to content shared by the user on social media. The reception unit can also provide learning materials that are likely to be of interest to the user based on their social media activity. Furthermore, the reception unit can provide related learning materials by taking into account the activities of the user's friends on social media. This makes it possible to provide optimal learning materials based on the user's social media activity.

[0084] The reception unit can customize the input method by reflecting the user's past feedback when inputting learning materials. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI analyzes the user's survey results, comments, etc. The reception unit customizes the input method based on the user's past feedback. For example, it can provide input methods that the user has preferred in the past with priority. It can also avoid input methods that the user has been dissatisfied with in the past. Furthermore, it can suggest the optimal input method based on the user's past feedback. This makes it possible to provide the optimal input method based on the user's past feedback.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI can analyze the tone and speed of the user's voice to estimate emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the generation AI can analyze the user's text input to estimate emotions. The analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can provide visually stimulating analysis results. This allows the optimal analysis results to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the learning material. The analysis unit, for example, uses a generation AI to evaluate the importance of the learning material. For example, the generation AI evaluates the frequency and impact of the learning material. The analysis unit adjusts the level of detail of the analysis based on the importance of the learning material. For example, a detailed analysis can be performed on important learning material. On the other hand, a brief analysis can be performed on less important learning material. Furthermore, the depth of the analysis can be adjusted according to the importance of the learning material. This makes it possible to provide optimal analysis results according to the importance of the learning material.

[0087] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the learning material. The analysis unit, for example, uses a generation AI to identify the category of the learning material. For example, the generation AI identifies the subject, topic, theme, etc. of the learning material. The analysis unit applies different analysis algorithms depending on the category of the learning material. For example, a specialized analysis algorithm can be applied to technical learning material. A simple analysis algorithm can be applied to general learning material. Furthermore, the optimal analysis algorithm can be selected depending on the category of the learning material. This makes it possible to apply the optimal analysis algorithm depending on the category of the learning material.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the generation AI analyzes the user's past analysis reports and evaluation results. The analysis unit improves the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. In addition, the analysis trends can be grasped from the user's past analysis results to improve accuracy. Furthermore, the user's past analysis results can be used as feedback to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI can analyze the user's tone and speed of voice to estimate emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the generation AI can analyze the user's text input to estimate emotions. The analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide a visually stimulating analysis. This allows the optimal analysis result to be provided depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] During analysis, the analysis unit can determine the analysis priority based on the submission time of the learning material. The analysis unit, for example, uses a generation AI to evaluate the submission time of the learning material. For example, the generation AI evaluates the submission deadline or submission date of the learning material. The analysis unit determines the analysis priority based on the submission time of the learning material. For example, learning material with an approaching deadline can be analyzed first. Also, learning material with a more distant submission time can be postponed. Furthermore, the analysis priority can be adjusted depending on the submission time. This makes it possible to provide optimal analysis results depending on the submission time of the learning material.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the learning materials. The analysis unit, for example, uses a generation AI to evaluate the relevance of the learning materials. For example, the generation AI evaluates the similarity of the topics of the learning materials and the relevance of the content. The analysis unit adjusts the order of analysis based on the relevance of the learning materials. For example, it prioritizes analysis of highly relevant learning materials. It can also postpone analysis of less relevant learning materials. Furthermore, the order of analysis can be adjusted according to the relevance of the learning materials. This makes it possible to provide optimal analysis results according to the relevance of the learning materials.

[0092] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI evaluates the user's past learning history, test results, etc. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, a user with high level of expertise can be provided with an analysis that makes heavy use of technical terms. Furthermore, a user with low level of expertise can be provided with an analysis that uses simple terms. Furthermore, the use of technical terms in the analysis can be adjusted according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0093] The generation unit can estimate the user's emotions and adjust the way the questions are presented based on the estimated user's emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotions using text analysis technology. For example, the generation AI can analyze the user's text input to estimate the emotion. The generation unit adjusts the way the questions are presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate questions with detailed explanations. If the user is in a hurry, the generation unit can generate concise questions. If the user is excited, the generation unit can generate visually stimulating questions. This makes it possible to provide optimal questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The generation unit can adjust the level of detail of the questions based on the importance of the extracted important points during generation. The generation unit, for example, uses a generation AI to evaluate the importance of the extracted important points. For example, the generation AI evaluates the frequency and impact of the important points. The generation unit adjusts the level of detail of the questions based on the importance of the extracted important points. For example, detailed questions can be generated for important points. Concise questions can also be generated for points that are not so important. Furthermore, the level of detail of the questions can be adjusted according to the importance of the extracted points. This makes it possible to provide optimal questions based on the important points.

[0095] The generation unit can apply different generation algorithms depending on the problem category during generation. The generation unit, for example, uses a generation AI to identify the problem category. For example, the generation AI identifies the subject, topic, theme, etc. of the problem. The generation unit applies different generation algorithms depending on the problem category. For example, a specialized generation algorithm can be applied to technical problems. Also, a simple generation algorithm can be applied to general problems. Furthermore, the optimal generation algorithm can be selected depending on the problem category. This makes it possible to apply the optimal generation algorithm depending on the problem category.

[0096] During generation, the generation unit can improve the accuracy of generation by referring to the user's past question answer results. The generation unit, for example, uses a generation AI to analyze the user's past question answer results. For example, the generation AI analyzes the user's past correct answer rate and answer time. The generation unit improves the accuracy of generation based on the user's past question answer results. For example, the generation unit improves the accuracy of generation based on the user's past question answer results. In addition, the user's past answer results can be used to understand question trends and improve accuracy. Furthermore, the user's past answer results can be used as feedback to improve the accuracy of generation. This makes it possible to improve the accuracy of generation based on the user's past answer results.

[0097] The generation unit can estimate the user's emotions and adjust the length of the questions to be generated based on the estimated user emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI can analyze the user's voice tone and speed to estimate emotions. Furthermore, the generation unit can estimate the user's emotions using text analysis technology. For example, the generation AI can analyze the user's text input to estimate emotions. The generation unit adjusts the length of the questions to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point questions. On the other hand, if the user is relaxed, the generation unit can generate longer questions with detailed explanations. Furthermore, if the user is excited, the generation unit can generate questions with visually stimulating effects. This allows the system to provide optimal questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] At the time of generation, the generation unit can determine the priority of questions based on the submission time of the learning materials. The generation unit, for example, uses a generation AI to evaluate the submission time of the learning materials. For example, the generation AI evaluates the submission deadline or submission date of the learning materials. The generation unit determines the priority of questions based on the submission time of the learning materials. For example, questions based on learning materials with an upcoming deadline can be generated preferentially. Also, questions based on learning materials with a more distant submission time can be postponed. Furthermore, the generation priority of questions can be adjusted depending on the submission time. This makes it possible to provide optimal questions depending on the submission time of the learning materials.

[0099] The generation unit can adjust the order of questions based on the relevance of the learning materials when generating them. The generation unit, for example, uses a generation AI to evaluate the relevance of the learning materials. For example, the generation AI evaluates the similarity of the topics of the learning materials or the relevance of the contents. The generation unit adjusts the order of questions based on the relevance of the learning materials. For example, questions based on highly relevant learning materials are generated preferentially. Also, questions based on less relevant learning materials can be postponed. Furthermore, the generation order of questions can be adjusted according to the relevance of the learning materials. This makes it possible to provide optimal questions according to the relevance of the learning materials.

[0100] The generation unit can adjust the use of technical terminology in the questions during generation according to the user's level of expertise. The generation unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI evaluates the user's past learning history, test results, etc. The generation unit adjusts the use of technical terminology in the questions according to the user's level of expertise. For example, for a user with high expertise, questions that use a lot of technical terminology can be generated. Also, for a user with low expertise, questions that use simple terminology can be generated. Furthermore, the use of technical terminology in the questions can be adjusted according to the user's level of expertise. This makes it possible to provide the most suitable questions according to the user's level of expertise.

[0101] The providing unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user's emotions. The providing unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI can analyze the user's tone and speed of voice to estimate emotions. The providing unit can also estimate the user's emotions using text analysis technology. For example, the generation AI can analyze the user's text input to estimate emotions. The providing unit adjusts the way questions are presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide questions with detailed explanations. If the user is in a hurry, the providing unit can provide concise questions. If the user is excited, the providing unit can provide visually stimulating questions. This allows the most appropriate questions to be presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] When providing the question, the providing unit can select the optimal providing method by referring to the user's past question answer history. The providing unit, for example, uses a generation AI to analyze the user's past question answer history. For example, the generation AI analyzes the user's past correct answer rate, answer time, etc. The providing unit selects the optimal providing method based on the user's past question answer history. For example, the providing unit preferentially provides a providing method that the user has previously preferred. It can also avoid a providing method that the user has previously been dissatisfied with. Furthermore, it can suggest the optimal providing method based on the user's past answer history. This makes it possible to provide the optimal providing method based on the user's past answer history.

[0103] The providing unit can customize the content to be provided based on the user's current learning situation when providing the content. The providing unit, for example, uses a generation AI to analyze the user's current learning situation. For example, the generation AI analyzes the user's progress and level of understanding. The providing unit customizes the content to be provided based on the user's current learning situation. For example, the providing unit can provide the next problem to be learned based on the user's current learning progress. In addition, the providing unit can provide problems of an appropriate difficulty level based on the user's current level of understanding. Furthermore, the providing unit can provide related problems based on the user's current areas of interest. This makes it possible to provide the most appropriate problems based on the user's current learning situation.

[0104] The providing unit can improve the delivery method by reflecting user feedback at the time of delivery. The providing unit, for example, uses a generation AI to analyze user feedback. For example, the generation AI analyzes user survey results and comments, etc. The providing unit improves the delivery method based on user feedback. For example, the delivery method is improved based on user feedback. Furthermore, problems with the delivery method can be identified and improved from user feedback. Furthermore, the delivery method can be customized by referring to user feedback. This makes it possible to provide the optimal delivery method based on user feedback.

[0105] The providing unit can estimate the user's emotions and determine the order in which questions are presented based on the estimated user's emotions. The providing unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation AI can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the generation AI can analyze the user's voice tone and speed to estimate the user's emotions. Furthermore, the providing unit can also estimate the user's emotions using text analysis technology. For example, the generation AI can analyze the user's text input to estimate the user's emotions. The providing unit determines the order in which questions are presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing questions with detailed explanations. Also, if the user is in a hurry, the providing unit can prioritize providing concise questions. Furthermore, if the user is excited, the providing unit can prioritize providing visually stimulating questions. This allows the most appropriate questions to be presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The providing unit can select the optimal provision method by taking into consideration the user's geographical location information when providing the questions. The providing unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI acquires the user's location information using GPS data or an IP address. The providing unit selects the optimal provision method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide questions related to that area. Also, if the user is traveling, the providing unit can provide questions related to the travel destination. Furthermore, if the user is in a specific facility, the providing unit can provide questions related to that facility. This makes it possible to provide optimal questions based on the user's geographical location information.

[0107] The providing unit can analyze the user's social media activity and customize the content to be provided when providing the content. The providing unit, for example, uses a generation AI to analyze the user's social media activity. For example, the generation AI analyzes the user's posted content, the number of likes, the number of followers, etc. The providing unit customizes the content to be provided based on the user's social media activity. For example, the providing unit can provide questions related to content shared by the user on social media. It can also provide questions that are likely to interest the user based on their social media activity. Furthermore, it can provide related questions based on the activity of the user's friends on social media. This makes it possible to provide optimal questions based on the user's social media activity.

[0108] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI analyzes the user's survey results, comments, etc. The providing unit customizes the delivery method based on the user's past feedback. For example, the delivery method is customized based on the user's past feedback. In addition, the delivery unit can identify and customize problems with the delivery method from the user's past feedback. Furthermore, the delivery method can be optimized by referring to the user's past feedback. This makes it possible to provide the optimal delivery method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input learning materials via the control unit 46A of the smart device 14. The analysis unit analyzes the learning materials using a generation AI via the specific processing unit 290 of the data processing device 12 and extracts important points. The generation unit generates questions using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit provides the questions generated by the control unit 46A of the smart device 14 to the learner. Furthermore, the reception unit estimates the user's emotions using the generation AI and adjusts the input timing of the learning materials based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input learning materials via the control unit 46A of the smart glasses 214. The analysis unit analyzes the learning materials using a generation AI via the specific processing unit 290 of the data processing device 12 and extracts important points. The generation unit generates questions using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit provides the questions generated by the control unit 46A of the smart glasses 214 to the learner. Furthermore, the reception unit estimates the user's emotions using the generation AI and adjusts the timing of inputting the learning materials based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input learning materials via the control unit 46A of the headset-type terminal 314. The analysis unit analyzes the learning materials using a generation AI via the specific processing unit 290 of the data processing device 12 and extracts important points. The generation unit generates questions using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit provides the questions generated by the control unit 46A of the headset-type terminal 314 to the learner. Furthermore, the reception unit estimates the user's emotions using the generation AI and adjusts the timing of input of the learning materials based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input learning materials via the control unit 46A of the robot 414. The analysis unit analyzes the learning materials using a generation AI via the specific processing unit 290 of the data processing device 12 and extracts important points. The generation unit generates questions using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit provides the questions generated by the control unit 46A of the robot 414 to the learner. Furthermore, the reception unit estimates the user's emotions using the generation AI and adjusts the timing of inputting the learning materials based on the estimated emotions.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When understanding the content of learning materials, the analysis unit can refer to the user's past learning history and perform an analysis that is individually optimized. For example, if the user previously had a deep understanding of a particular topic, the analysis unit can extract important points related to that topic in more detail. Also, for topics that the user previously struggled with, the analysis unit can extract more concise and easy-to-understand points. Furthermore, based on the user's learning history, the analysis algorithm can be adjusted to perform an analysis that is optimized for each individual learning style. This allows for more effective questions to be generated based on the user's past learning history.

[0111] When inputting learning materials, the reception unit can monitor the user's current learning progress in real time and prompt the user to input learning materials at an appropriate time. For example, the reception unit can prompt the user to input new learning materials immediately after completing a specific task. Also, if the user has suspended learning for a certain period of time, the reception unit can send a reminder to prompt the user to input learning materials. Furthermore, the difficulty level of the learning materials can be adjusted according to the user's learning progress, and materials of an appropriate level can be provided. This allows the user to input learning materials at the optimal time according to their learning progress.

[0112] When understanding the content of the learning material, the analysis unit can estimate the user's emotions and adjust the analysis approach based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide a concise and to-the-point analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the analysis unit to provide optimal analysis results according to the user's emotions.

[0113] When generating questions based on the extracted important points, the generator can select a question format that suits the user's learning style. For example, questions using diagrams and graphs can be generated for visual learners. Questions using audio can be generated for auditory learners. Furthermore, interactive questions can be generated for tactile learners. This makes it possible to provide the most suitable questions for the user's learning style.

[0114] When providing the generated questions to the learner, the providing unit can estimate the user's emotions and adjust the method of providing the questions based on the estimated emotions. For example, if the user is relaxed, questions with detailed explanations can be provided. If the user is in a hurry, simple questions can be provided. Furthermore, if the user is excited, visually stimulating questions can be provided. In this way, optimal questions can be provided according to the user's emotions.

[0115] When generating questions, the generator can refer to the user's past answer history and generate individually optimized questions. For example, questions with more detailed explanations can be generated for topics on which the user has a low rate of correct answers in the past. Also, questions with a higher level of difficulty can be generated for topics in which the user excels. Furthermore, the length and format of the questions can be adjusted based on the user's answer time. This makes it possible to provide more effective questions based on the user's past answer history.

[0116] When providing the generated questions to the learner, the providing unit can select the optimal method of providing them by taking into account the user's geographical location information. For example, if the user is in a specific area, questions related to that area can be provided. If the user is traveling, questions related to the travel destination can be provided. Furthermore, if the user is in a specific facility, questions related to that facility can be provided. In this way, optimal questions can be provided based on the user's geographical location information.

[0117] When understanding the content of the learning materials, the analysis unit can analyze the user's social media activities and extract highly relevant points. For example, it can extract points related to the content the user has shared on social media. It can also extract points that are likely to be of interest from the user's social media activities. It can also extract related points by referring to the activities of the user's friends on social media. This makes it possible to extract the most appropriate points based on the user's social media activities.

[0118] When providing the generated questions to the learner, the providing unit can estimate the user's emotions and determine the order in which the questions are provided based on the estimated emotions. For example, if the user is relaxed, questions with detailed explanations can be provided preferentially. If the user is in a hurry, simple questions can be provided preferentially. Furthermore, if the user is excited, visually stimulating questions can be provided preferentially. In this way, optimal questions can be provided according to the user's emotions.

[0119] When generating questions, the generator can estimate the user's emotions and adjust the length of the questions based on the estimated emotions. For example, if the user is in a hurry, it can generate short questions that are to the point. If the user is relaxed, it can generate longer questions that include detailed explanations. Furthermore, if the user is excited, it can generate questions with visually stimulating effects. This makes it possible to provide optimal questions according to the user's emotions.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit inputs learning materials. The learning materials include, but are not limited to, e-learning materials and work procedure manuals. The reception unit inputs the learning materials in text format, for example. Step 2: The analysis unit uses the generation AI to analyze the learning materials entered by the reception unit and extract important points. For example, the generation AI uses natural language processing technology to understand the content of the learning materials and extract important points. Step 3: The generation unit uses the generation AI to generate questions based on the important points extracted by the analysis unit. Questions are generated in various formats, such as multiple-choice and written questions. For example, when generating multiple-choice questions, the generation AI creates questions that require the user to select the correct procedure or select important points. When generating written questions, the generation AI also creates questions that require the user to explain the procedure or describe important points. Step 4: The providing unit provides the questions generated by the generating unit to the learner. For example, the providing unit provides the generated questions to the learner through a web application or a mobile application.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 reception section for inputting learning materials; an analysis unit that analyzes the learning materials input by the reception unit and extracts important points; a generation unit that generates questions based on the important points extracted by the analysis unit; a providing unit that provides the questions generated by the generating unit; Equipped with A system characterized by:

2. The reception unit Enter e-learning materials or work procedure manuals in text format 2. The system of claim 1.

3. The analysis unit Understand the content of the learning material and extract the key points 2. The system of claim 1.

4. The generation unit Generate multiple choice or short answer questions based on extracted key points 2. The system of claim 1.

5. The providing unit Provide generated questions to learners 2. The system of claim 1.

6. The generation unit Generate questions that require you to choose the correct procedure or points to pay attention to 2. The system of claim 1.

7. The generation unit Generate questions that require you to explain procedures or describe important points 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the timing of inputting learning materials based on the estimated user emotions.

2. The system of claim 1.

Images