System

Abstract

An object of the system according to the embodiment is to support learning by efficiently summarizing the contents of a book and extracting important points.SOLUTION: A system includes an analysis part, a summary generation part, an important point extraction part, and a quiz generation part. The analysis unit analyzes the content of the book. The summary generation unit summarizes the content analyzed by the analysis unit. The important point extraction unit extracts important points from the summary generated by the summary generation unit. The quiz generation unit generates a quiz based on the important points extracted by the important point extraction 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 had the problem of making it difficult to efficiently summarize the contents of a book and extract and study its important points.

[0005] The system according to the embodiment aims to efficiently summarize the contents of a book and extract important points to support learning. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a summary generation unit, an important point extraction unit, and a quiz generation unit. The analysis unit analyzes the content of a book. The summary generation unit summarizes the content analyzed by the analysis unit. The important point extraction unit extracts important points from the summary generated by the summary generation unit. The quiz generation unit generates a quiz based on the important points extracted by the important point extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently summarize the contents of a book and extract important points to support learning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to an embodiment of the present invention is a system that supports users in efficiently inputting books they want to learn from. In this system, a generative AI analyzes the contents of the book, extracts summaries and important points, and provides reviews in the form of quizzes. This allows the AI ​​system to efficiently input the contents of books that users want to learn from and deepen their understanding.

[0029] The AI ​​system according to the embodiment includes an analysis unit, a summary generation unit, an important point extraction unit, and a quiz generation unit. The analysis unit analyzes the content of a book. For example, the analysis unit may analyze the content of a book using text mining technology. The analysis unit may also analyze the content of a book using natural language processing technology. The analysis unit may also analyze the content of a book using a machine learning algorithm. The summary generation unit summarizes the content analyzed by the analysis unit. For example, the summary generation unit may summarize the content of a book using a generation AI. The summary generation unit may also summarize the content of a book using a text generation AI (e.g., LLM). The summary generation unit may also summarize the content of a book using a multimodal generation AI. The important point extraction unit extracts important points from the summary generated by the summary generation unit. For example, the important point extraction unit may extract important points based on frequently occurring keywords. The important point extraction unit may also extract important points based on central concepts of a theme. The important point extraction unit may also extract important points based on definitions and theories. The quiz generation unit generates a quiz based on the important points extracted by the important point extraction unit. For example, the quiz generation unit generates multiple-choice quizzes. The quiz generation unit can also generate descriptive quizzes. The quiz generation unit can also generate quizzes with adjusted difficulty levels. This allows the AI ​​system according to the embodiment to efficiently input the contents of books that users want to learn and deepen their understanding.

[0030] The analysis unit evaluates the user's level of understanding in real time and can focus on summarizing parts where understanding is shallow. For example, the analysis unit uses a generation AI to analyze the contents of a book and evaluate the level of understanding based on the user's answers and reactions. For parts where understanding is shallow, a detailed summary is generated and provided to the user. Furthermore, as the user reads the contents of the book, the analysis unit uses the generation AI to monitor the level of understanding in real time and identify parts where understanding is shallow. For those parts, additional summaries and explanations are provided. Furthermore, the analysis unit uses the generation AI to analyze the user's learning history and re-summarize parts where understanding was shallow in the past. This enables efficient learning by providing summaries that correspond to the user's level of understanding.

[0031] The analysis unit can personalize summaries based on the user's learning history and interests, and link them to related past learning content. For example, the analysis unit allows the generation AI to analyze the user's learning history and include parts related to previously learned content in the summary. This allows the user to understand new knowledge by linking it to existing knowledge. The analysis unit also allows the generation AI to customize the summary content based on the user's interests and concerns. For example, a user who is interested in a specific topic will be given priority in receiving summaries related to that topic. The analysis unit also allows the generation AI to link previously learned content with new content based on the user's learning history. This allows the user to have a consistent learning experience. This enables efficient learning by providing summaries based on the user's learning history and interests.

[0032] The analysis unit summarizes the analyzed content in audio or video format, supporting learning through sight and hearing. For example, the analysis unit uses a generation AI to analyze the contents of a book and generate a summary in audio format. By listening to the audio, users can advance their learning in a way other than visually. The analysis unit also uses a generation AI to analyze the contents of a book and generate a summary in video format. For example, it provides a video that explains important points visually. The analysis unit also uses a generation AI to provide a summary in audio or video format so that users can learn through sight and hearing. This accommodates different learning styles. This makes it possible to support learning through sight and hearing.

[0033] The analysis unit can cross-reference the contents of books from different genres and provide summaries that integrate related knowledge. For example, the analysis unit allows the generation AI to analyze the contents of books from different genres and generate summaries that integrate related knowledge. For example, a summary that combines scientific and historical knowledge is provided. The analysis unit can also cross-reference the contents of books from different genres, find common themes and topics, and generate summaries based on those. The analysis unit allows the generation AI to analyze the contents of books from different genres and provide summaries that integrate related knowledge. This allows users to learn from a broad range of perspectives. By providing summaries that integrate knowledge from different genres, learning from a broad range of perspectives is possible.

[0034] The important point extraction unit can reevaluate important points based on the user's learning goals and present the most appropriate points. For example, the important point extraction unit reevaluates important points extracted from the book's contents by the generation AI based on the user's learning goals and presents the most appropriate points. For example, it provides points necessary for exam preparation with priority. The important point extraction unit also reevaluates important points based on the user's learning goals and presents the most appropriate points. For example, it emphasizes points necessary for acquiring specific skills. The important point extraction unit also analyzes the user's learning goals and reevaluates important points based on them. This allows the user to study efficiently. As a result, efficient learning is possible by providing important points according to the user's learning goals.

[0035] The important point extraction unit can compare the important points extracted by the generation AI with other related literature and databases to increase reliability. For example, the important point extraction unit compares the important points extracted by the generation AI with other related literature and databases to confirm reliability. For example, it refers to databases of academic papers and specialized books. The important point extraction unit also compares the extracted important points with other related literature to build a system to increase reliability. For example, it evaluates reliability based on citation sources and references. The important point extraction unit also compares the important points extracted by the generation AI with other databases to increase reliability. This allows users to obtain highly reliable information. As a result, providing highly reliable information can improve the user's learning effectiveness.

[0036] The important point extraction unit can present the important points in an interactive mind map format, making it easier to understand visually. For example, the important point extraction unit presents the important points extracted by the generation AI in an interactive mind map format. The user can visually understand the important points while operating the mind map. The important point extraction unit also displays the extracted important points in mind map format, allowing the user to access detailed information by clicking and zooming. The important point extraction unit also presents the important points extracted by the generation AI as an interactive mind map. This allows the user to intuitively understand the important points. This makes it possible to improve learning effectiveness by providing important points in a format that is easy to understand visually.

[0037] The important point extraction unit incorporates the opinions of experts from different fields and can provide multifaceted perspectives on important points. For example, the important point extraction unit incorporates the opinions of experts from different fields on important points extracted by the generation AI. For example, it integrates technical and economic perspectives. The important point extraction unit also builds a system that evaluates important points from multiple perspectives based on the opinions of experts from different fields. This allows users to learn from a wide range of perspectives. The important point extraction unit also incorporates the opinions of experts from different fields on important points extracted by the generation AI. This allows users to understand important points from multiple perspectives. This makes it possible to learn from a wide range of perspectives.

[0038] The quiz generation unit can dynamically adjust the difficulty of the quiz according to the user's learning progress. For example, the generation AI of the quiz generation unit analyzes the user's learning progress and dynamically adjusts the difficulty of the quiz. For example, it presents easier questions in areas where the user is weak and more difficult questions in areas where the user is strong. In addition, the generation AI of the quiz generation unit adjusts the difficulty of the quiz based on the user's learning history. For example, it changes the difficulty of questions according to the past rate of correct answers. In addition, the generation AI of the quiz generation unit monitors the user's learning progress in real time and dynamically adjusts the difficulty of the quiz. This allows the user to challenge questions of an appropriate difficulty. This allows efficient review by providing quizzes according to the user's learning progress.

[0039] The quiz generation unit reflects the user's past answer history and can present questions that focus on weak areas. In the quiz generation unit, for example, the generation AI analyzes the user's past answer history and generates quizzes that focus on weak areas. For example, questions that the user got wrong in the past are presented again. In addition, the quiz generation unit identifies weak areas based on the user's answer history and generates quizzes related to those areas. This allows the user to review efficiently. In addition, the quiz generation unit reflects the user's past answer history and generates quizzes that focus on weak areas. This allows the user to overcome their weak areas. This improves learning effectiveness by focusing on reviewing the user's weak areas.

[0040] The quiz generation unit can turn the quiz into a game format to incorporate a competitive element and improve motivation to learn. For example, the quiz generation unit uses a generation AI to convert the quiz into a game format, allowing users to learn while competing. For example, it adds a function to display scores and rankings. The quiz generation unit can also incorporate a competitive element into the quiz, allowing users to compete with other users and improving motivation to learn. For example, it can provide an online battle function. The quiz generation unit can also convert the quiz into a game format using the generation AI to incorporate a competitive element. This allows users to learn while having fun. This can improve motivation to learn by providing a quiz format that incorporates a competitive element.

[0041] The quiz generation unit can provide quiz formats (e.g., visual, auditory, tactile) that correspond to different learning styles. In the quiz generation unit, for example, the generation AI provides quiz formats that correspond to different learning styles. For example, it provides a quiz using images and diagrams to visual learners. It also provides a quiz using audio to auditory learners and an interactive quiz to tactile learners. This enables review that matches the user's learning style. In addition, the quiz generation unit uses the generation AI to analyze the user's learning style and provide the optimal quiz format based on that. For example, it generates a quiz that combines visual, auditory, and tactile elements. This provides quiz formats that correspond to different learning styles, thereby improving the user's learning effectiveness.

[0042] The learning progress management unit can monitor the user's learning progress in real time and provide feedback at the appropriate time. For example, the generation AI in the learning progress management unit monitors the user's learning progress in real time and provides feedback at the appropriate time. For example, sending an encouraging message if learning stagnates. The learning progress management unit also builds a system that analyzes the user's learning progress and provides feedback at the appropriate time. For example, sending a praising message if a certain level of progress is made. The learning progress management unit also builds a system in which the generation AI monitors the user's learning progress in real time and provides feedback at the appropriate time. This allows the user to continue learning while maintaining motivation. As a result, providing feedback in real time can improve the user's learning effectiveness.

[0043] The learning progress management unit can compare the user's learning progress with other users and present the relative progress status. In the learning progress management unit, for example, the generation AI compares the user's learning progress with other users and presents the relative progress status. For example, it compares the progress with that of other users who are studying the same book. The learning progress management unit also builds a system that analyzes the user's learning progress and compares it with that of other users. This allows the user to objectively grasp their own progress status. In addition, the learning progress management unit also compares the user's learning progress with that of other users and presents the relative progress status. This allows the user to study while developing a competitive spirit. In this way, presenting the relative progress status can increase the user's motivation to study.

[0044] The learning progress management unit visually displays the user's learning progress on a visual dashboard, allowing the user to intuitively understand. In the learning progress management unit, for example, the generation AI visually displays the user's learning progress on a visual dashboard. For example, the progress status is shown using graphs and charts. The learning progress management unit also builds a visual dashboard that visually displays the user's learning progress. This allows the user to intuitively grasp the progress status. In the learning progress management unit, the generation AI visually displays the user's learning progress on a visual dashboard. This allows the user to understand the learning progress at a glance. In this way, visually displaying the learning progress can help the user understand.

[0045] The learning progress management unit provides progress management methods according to different learning goals, making it possible to meet individual needs. In the learning progress management unit, for example, the generation AI provides a progress management method according to the user's learning goals. For example, progress management is performed according to short-term goals and long-term goals. In addition, the learning progress management unit customizes the progress management method based on the user's learning goals. This makes it possible to manage progress according to individual needs. In addition, the learning progress management unit provides progress management methods according to different learning goals. For example, progress management is performed according to exam preparation or skill acquisition. In this way, progress management according to individual learning goals can be provided, thereby improving the user's learning effectiveness.

[0046] The study plan providing unit can analyze the user's study history and dynamically update the optimal study plan. In the study plan providing unit, for example, a generation AI analyzes the user's study history and dynamically updates the optimal study plan. For example, the plan is adjusted according to the progress of the study. The study plan providing unit also builds a system in which the generation AI proposes the optimal study plan based on the user's study history. This allows the user to study efficiently. In addition, the study plan providing unit also analyzes the user's study history using the generation AI and dynamically updates the optimal study plan. This allows the user to always study based on the latest study plan. This makes it possible to improve learning effectiveness by providing the optimal study plan based on the user's study history.

[0047] The study plan providing unit can flexibly adjust the content of the plan to suit the user's learning style and pace. For example, the generation AI of the study plan providing unit analyzes the user's learning style and pace and flexibly adjusts the content of the study plan based on that. For example, a visual learner is provided with a plan that includes a lot of visual content. The generation AI of the study plan providing unit also adjusts the study plan to suit the user's learning pace. For example, if progress is fast, new topics are added, and if progress is slow, review is suggested. The generation AI of the study plan providing unit also analyzes the user's learning style and pace and flexibly adjusts the content of the study plan based on that. This allows the user to study at a pace that suits them. This makes it possible to improve learning effectiveness by providing a study plan that suits the user's learning style and pace.

[0048] The learning plan providing unit can provide a plan that combines different learning resources (e.g., online courses, videos, articles). In the learning plan providing unit, for example, the generation AI provides a learning plan that combines different learning resources. For example, the optimal learning plan is created by combining online courses, videos, and articles. In addition, the learning plan providing unit selects the optimal learning resources according to the user's learning goals and provides a plan that combines them. For example, it aggregates resources necessary to acquire specific skills. In addition, the generation AI analyzes different learning resources and suggests the optimal combination to the user. This allows the user to advance their studies by utilizing a variety of resources. As a result, by providing a plan that combines different learning resources, the user's learning effectiveness can be improved.

[0049] The study plan providing unit can incorporate opinions from experts in different fields to provide a study plan with multiple perspectives. For example, the generation AI of the study plan providing unit provides a study plan that incorporates opinions from experts in different fields. For example, the generation AI creates a plan that integrates a technical perspective and an economic perspective. The study plan providing unit also provides a study plan with multiple perspectives from the generation AI based on the opinions of experts in different fields. This allows the user to study from a wide range of perspectives. The study plan providing unit also provides a study plan with multiple perspectives from the generation AI. This allows the user to study from a multifaceted perspective. This makes it possible to improve the user's learning effectiveness by providing a study plan with multiple perspectives.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can link related past learning content based on the user's learning history. For example, the generation AI analyzes the user's learning history and includes parts related to previously learned content in the summary. This allows the user to understand new knowledge by linking it to existing knowledge. The analysis unit also allows the generation AI to customize the summary content based on the user's interests and concerns. For example, a user who is interested in a particular topic will be given priority in receiving summaries related to that topic. The analysis unit also allows the generation AI to link previously learned content with new content based on the user's learning history. This allows the user to have a consistent learning experience. This enables efficient learning by providing summaries based on the user's learning history and interests.

[0052] The analysis unit evaluates the user's level of comprehension in real time and can focus on summarizing parts where understanding is shallow. For example, the generation AI analyzes the contents of a book and evaluates the user's level of comprehension based on their answers and reactions. For parts where understanding is shallow, a detailed summary is generated and provided to the user. The analysis unit also monitors the user's level of comprehension in real time as they read the book, identifying parts where understanding is shallow. For those parts, additional summaries and explanations are provided. The analysis unit also allows the generation AI to analyze the user's learning history and re-summarize parts where understanding was shallow in the past. This enables efficient learning by providing summaries that match the user's level of comprehension.

[0053] The analysis unit summarizes the analyzed content in audio or video format, supporting learning through sight and hearing. For example, the generation AI analyzes the contents of a book and generates a summary in audio format. By listening to the audio, users can advance their learning in a way other than visually. The analysis unit also allows the generation AI to analyze the contents of a book and generate a summary in video format. For example, it provides a video that explains important points visually. The analysis unit also allows the generation AI to provide a summary in audio or video format so that users can learn through sight and hearing. This accommodates different learning styles. This makes it possible to support learning through sight and hearing.

[0054] The analysis unit can cross-reference the contents of books from different genres and provide summaries that integrate related knowledge. For example, the generation AI can analyze the contents of books from different genres and generate summaries that integrate related knowledge. For example, a summary that combines scientific and historical knowledge can be provided. The analysis unit can also cross-reference the contents of books from different genres, find common themes and topics, and generate summaries based on those. The generation AI can also analyze the contents of books from different genres and provide summaries that integrate related knowledge. This allows users to learn from a broad range of perspectives. By providing summaries that integrate knowledge from different genres, learning from a broad range of perspectives is possible.

[0055] The important point extraction unit can reevaluate the important points based on the user's learning goals and present the most appropriate points. For example, the generation AI reevaluates the important points extracted from the book's contents based on the user's learning goals and presents the most appropriate points. For example, it prioritizes points necessary for exam preparation. The important point extraction unit also reevaluates the important points based on the user's learning goals and presents the most appropriate points. For example, it emphasizes points necessary for acquiring specific skills. The important point extraction unit also analyzes the user's learning goals and reevaluates the important points based on them. This allows the user to study efficiently. By providing important points according to the user's learning goals, efficient learning is possible.

[0056] The key point extraction unit can compare the key points extracted by the generation AI with other related literature and databases to increase reliability. For example, the key points extracted by the generation AI can be compared with other related literature and databases to confirm reliability. For example, databases of academic papers and specialized books can be referenced. The key point extraction unit can also compare the extracted key points with other related literature to build a system to increase reliability. For example, it can evaluate reliability based on citation sources and references. The key point extraction unit can also compare the key points extracted by the generation AI with other databases to increase reliability. This allows users to obtain highly reliable information. By providing highly reliable information, the user's learning effectiveness can be improved.

[0057] The important point extraction unit can present the important points in an interactive mind map format, making them easier to understand visually. For example, the important points extracted by the generation AI can be presented in an interactive mind map format. The user can visually understand the important points while operating the mind map. The important point extraction unit also displays the extracted important points in mind map format, allowing the user to access detailed information by clicking and zooming. The important point extraction unit also presents the important points extracted by the generation AI as an interactive mind map. This allows the user to intuitively understand the important points. This improves learning effectiveness by providing important points in a format that is easy to understand visually.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The analysis unit analyzes the content of the book. For example, the analysis unit analyzes the content of the book using text mining technology, natural language processing technology, or a machine learning algorithm. Step 2: The summary generator summarizes the content analyzed by the analyzer. For example, the summary generator may use a generative AI, a text generation AI (e.g., LLM), or a multimodal generation AI to summarize the book's contents. Step 3: The key point extractor extracts key points from the summary generated by the summary generator. For example, the key point extractor extracts key points based on frequently occurring keywords, central thematic concepts, or definitions and theories. Step 4: The quiz generation unit generates a quiz based on the important points extracted by the important point extraction unit. For example, the quiz generation unit generates a multiple-choice quiz, a written quiz, or a quiz with adjusted difficulty.

[0060] (Example 2) The AI ​​system according to an embodiment of the present invention is a system that supports users in efficiently inputting books they want to learn from. In this system, a generative AI analyzes the contents of the book, extracts summaries and important points, and provides reviews in the form of quizzes. This allows the AI ​​system to efficiently input the contents of books that users want to learn from and deepen their understanding.

[0061] The AI ​​system according to the embodiment includes an analysis unit, a summary generation unit, an important point extraction unit, and a quiz generation unit. The analysis unit analyzes the content of a book. For example, the analysis unit may analyze the content of a book using text mining technology. The analysis unit may also analyze the content of a book using natural language processing technology. The analysis unit may also analyze the content of a book using a machine learning algorithm. The summary generation unit summarizes the content analyzed by the analysis unit. For example, the summary generation unit may summarize the content of a book using a generation AI. The summary generation unit may also summarize the content of a book using a text generation AI (e.g., LLM). The summary generation unit may also summarize the content of a book using a multimodal generation AI. The important point extraction unit extracts important points from the summary generated by the summary generation unit. For example, the important point extraction unit may extract important points based on frequently occurring keywords. The important point extraction unit may also extract important points based on central concepts of a theme. The important point extraction unit may also extract important points based on definitions and theories. The quiz generation unit generates a quiz based on the important points extracted by the important point extraction unit. For example, the quiz generation unit generates multiple-choice quizzes. The quiz generation unit can also generate descriptive quizzes. The quiz generation unit can also generate quizzes with adjusted difficulty levels. This allows the AI ​​system according to the embodiment to efficiently input the contents of books that users want to learn and deepen their understanding.

[0062] The analysis unit evaluates the user's level of understanding in real time and can focus on summarizing parts where understanding is shallow. For example, the analysis unit uses a generation AI to analyze the contents of a book and evaluate the level of understanding based on the user's answers and reactions. For parts where understanding is shallow, a detailed summary is generated and provided to the user. Furthermore, as the user reads the contents of the book, the analysis unit uses the generation AI to monitor the level of understanding in real time and identify parts where understanding is shallow. For those parts, additional summaries and explanations are provided. Furthermore, the analysis unit uses the generation AI to analyze the user's learning history and re-summarize parts where understanding was shallow in the past. This enables efficient learning by providing summaries that correspond to the user's level of understanding.

[0063] The analysis unit can personalize summaries based on the user's learning history and interests, and link them to related past learning content. For example, the analysis unit allows the generation AI to analyze the user's learning history and include parts related to previously learned content in the summary. This allows the user to understand new knowledge by linking it to existing knowledge. The analysis unit also allows the generation AI to customize the summary content based on the user's interests and concerns. For example, a user who is interested in a specific topic will be given priority in receiving summaries related to that topic. The analysis unit also allows the generation AI to link previously learned content with new content based on the user's learning history. This allows the user to have a consistent learning experience. This enables efficient learning by providing summaries based on the user's learning history and interests.

[0064] The analysis unit uses the emotion estimation function to generate summaries that are likely to interest the user, thereby increasing their motivation to learn. For example, the analysis unit uses a generation AI to analyze the user's emotions and generate summaries that are likely to interest the user. For example, parts with strong positive emotions are emphasized and included in the summary. The analysis unit also uses the emotion estimation function to identify topics that are likely to interest the user and provide summaries related to those topics. This increases the user's motivation to learn. The analysis unit also uses a generation AI to generate summaries that are likely to interest the user based on the user's emotion data. For example, parts that the user found interesting are summarized in detail and provided. This increases the user's motivation to learn by providing summaries based on the user's interests.

[0065] The analysis unit summarizes the analyzed content in audio or video format, supporting learning through sight and hearing. For example, the analysis unit uses a generation AI to analyze the contents of a book and generate a summary in audio format. By listening to the audio, users can advance their learning in a way other than visually. The analysis unit also uses a generation AI to analyze the contents of a book and generate a summary in video format. For example, it provides a video that explains important points visually. The analysis unit also uses a generation AI to provide a summary in audio or video format so that users can learn through sight and hearing. This accommodates different learning styles. This makes it possible to support learning through sight and hearing.

[0066] The analysis unit can cross-reference the contents of books from different genres and provide summaries that integrate related knowledge. For example, the analysis unit allows the generation AI to analyze the contents of books from different genres and generate summaries that integrate related knowledge. For example, a summary that combines scientific and historical knowledge is provided. The analysis unit can also cross-reference the contents of books from different genres, find common themes and topics, and generate summaries based on those. The analysis unit allows the generation AI to analyze the contents of books from different genres and provide summaries that integrate related knowledge. This allows users to learn from a broad range of perspectives. By providing summaries that integrate knowledge from different genres, learning from a broad range of perspectives is possible.

[0067] The analysis unit can use the emotion estimation function to identify topics in which the user is most interested and prioritize providing summaries related to those topics. The analysis unit, for example, uses the emotion estimation function to identify topics in which the user is most interested and generate summaries related to those topics. For example, it prioritizes summaries that have a high user emotion score. The analysis unit also uses the generation AI to analyze the user's emotion data and identify topics that are likely to interest the user. It prioritizes providing summaries related to those topics. The analysis unit also uses the emotion estimation function to identify topics in which the user is most interested and generate summaries related to those topics. This increases the user's motivation to learn. By providing summaries based on the user's interests, it is possible to increase the user's motivation to learn.

[0068] The important point extraction unit can reevaluate important points based on the user's learning goals and present the most appropriate points. For example, the important point extraction unit reevaluates important points extracted from the book's contents by the generation AI based on the user's learning goals and presents the most appropriate points. For example, it provides points necessary for exam preparation with priority. The important point extraction unit also reevaluates important points based on the user's learning goals and presents the most appropriate points. For example, it emphasizes points necessary for acquiring specific skills. The important point extraction unit also analyzes the user's learning goals and reevaluates important points based on them. This allows the user to study efficiently. As a result, efficient learning is possible by providing important points according to the user's learning goals.

[0069] The important point extraction unit can compare the important points extracted by the generation AI with other related literature and databases to increase reliability. For example, the important point extraction unit compares the important points extracted by the generation AI with other related literature and databases to confirm reliability. For example, it refers to databases of academic papers and specialized books. The important point extraction unit also compares the extracted important points with other related literature to build a system to increase reliability. For example, it evaluates reliability based on citation sources and references. The important point extraction unit also compares the important points extracted by the generation AI with other databases to increase reliability. This allows users to obtain highly reliable information. As a result, providing highly reliable information can improve the user's learning effectiveness.

[0070] The important point extraction unit can use the emotion estimation function to identify the important points that the user is most interested in and emphasize those points. For example, the important point extraction unit can use the emotion estimation function to identify the important points that the user is most interested in and emphasize those points. For example, it can prioritize presenting parts with high emotion scores. In addition, the important point extraction unit uses the generation AI to analyze the user's emotion data and identify important points that are likely to interest the user. It then emphasizes and provides those points. In addition, the important point extraction unit can use the emotion estimation function to identify the important points that the user is most interested in and emphasize those points. This allows the user to study efficiently. As a result, the learning effect can be improved by providing important points based on the user's interests.

[0071] The important point extraction unit can present the important points in an interactive mind map format, making it easier to understand visually. For example, the important point extraction unit presents the important points extracted by the generation AI in an interactive mind map format. The user can visually understand the important points while operating the mind map. The important point extraction unit also displays the extracted important points in mind map format, allowing the user to access detailed information by clicking and zooming. The important point extraction unit also presents the important points extracted by the generation AI as an interactive mind map. This allows the user to intuitively understand the important points. This makes it possible to improve learning effectiveness by providing important points in a format that is easy to understand visually.

[0072] The important point extraction unit incorporates the opinions of experts from different fields and can provide multifaceted perspectives on important points. For example, the important point extraction unit incorporates the opinions of experts from different fields on important points extracted by the generation AI. For example, it integrates technical and economic perspectives. The important point extraction unit also builds a system that evaluates important points from multiple perspectives based on the opinions of experts from different fields. This allows users to learn from a wide range of perspectives. The important point extraction unit also incorporates the opinions of experts from different fields on important points extracted by the generation AI. This allows users to understand important points from multiple perspectives. This makes it possible to learn from a wide range of perspectives.

[0073] The important point extraction unit can use the emotion estimation function to identify the important points that interest the user most and provide additional information related to those points. For example, the important point extraction unit can use the emotion estimation function to identify the important points that interest the user most and provide additional information related to those points. For example, it can present materials related to parts with high emotion scores. The important point extraction unit also uses the generation AI to analyze the user's emotion data and identify important points that are likely to interest the user. It then provides additional information related to those points. The important point extraction unit also uses the emotion estimation function to identify the important points that interest the user most and provides additional information related to those points. This allows the user to learn more deeply. This makes it possible to improve learning effectiveness by providing important points and additional information based on the user's interests.

[0074] The quiz generation unit can dynamically adjust the difficulty of the quiz according to the user's learning progress. For example, the generation AI of the quiz generation unit analyzes the user's learning progress and dynamically adjusts the difficulty of the quiz. For example, it presents easier questions in areas where the user is weak and more difficult questions in areas where the user is strong. In addition, the generation AI of the quiz generation unit adjusts the difficulty of the quiz based on the user's learning history. For example, it changes the difficulty of questions according to the past rate of correct answers. In addition, the generation AI of the quiz generation unit monitors the user's learning progress in real time and dynamically adjusts the difficulty of the quiz. This allows the user to challenge questions of an appropriate difficulty. This allows efficient review by providing quizzes according to the user's learning progress.

[0075] The quiz generation unit reflects the user's past answer history and can present questions that focus on weak areas. In the quiz generation unit, for example, the generation AI analyzes the user's past answer history and generates quizzes that focus on weak areas. For example, questions that the user got wrong in the past are presented again. In addition, the quiz generation unit identifies weak areas based on the user's answer history and generates quizzes related to those areas. This allows the user to review efficiently. In addition, the quiz generation unit reflects the user's past answer history and generates quizzes that focus on weak areas. This allows the user to overcome their weak areas. This improves learning effectiveness by focusing on reviewing the user's weak areas.

[0076] The quiz generation unit uses the emotion estimation function to provide a quiz format that allows users to learn while having fun, thereby increasing their motivation to learn. The quiz generation unit, for example, uses the emotion estimation function to provide a quiz format that allows users to learn while having fun. For example, it generates a quiz that elicits positive emotions based on the user's emotional response. The quiz generation unit also uses a generation AI to analyze the user's emotional data and provide a quiz format that allows users to learn while having fun. For example, it generates a quiz that incorporates game elements. The quiz generation unit also uses the emotion estimation function to provide a quiz format that allows users to learn while having fun. This increases the user's motivation to learn. By providing a quiz format that allows users to learn while having fun, it is possible to increase the user's motivation to learn.

[0077] The quiz generation unit can turn the quiz into a game format to incorporate a competitive element and improve motivation to learn. For example, the quiz generation unit uses a generation AI to convert the quiz into a game format, allowing users to learn while competing. For example, it adds a function to display scores and rankings. The quiz generation unit can also incorporate a competitive element into the quiz, allowing users to compete with other users and improving motivation to learn. For example, it can provide an online battle function. The quiz generation unit can also convert the quiz into a game format using the generation AI to incorporate a competitive element. This allows users to learn while having fun. This can improve motivation to learn by providing a quiz format that incorporates a competitive element.

[0078] The quiz generation unit can provide quiz formats (e.g., visual, auditory, tactile) that correspond to different learning styles. In the quiz generation unit, for example, the generation AI provides quiz formats that correspond to different learning styles. For example, it provides a quiz using images and diagrams to visual learners. It also provides a quiz using audio to auditory learners and an interactive quiz to tactile learners. This enables review that matches the user's learning style. In addition, the quiz generation unit uses the generation AI to analyze the user's learning style and provide the optimal quiz format based on that. For example, it generates a quiz that combines visual, auditory, and tactile elements. This provides quiz formats that correspond to different learning styles, thereby improving the user's learning effectiveness.

[0079] The quiz generation unit can use the emotion estimation function to prioritize quizzes related to topics that interest the user most. The quiz generation unit, for example, uses the emotion estimation function to identify topics that interest the user most and prioritize quizzes related to those topics. For example, questions related to parts with high emotion scores are presented. Furthermore, the quiz generation unit uses a generation AI to analyze the user's emotion data and generate quizzes related to topics that are likely to interest the user. This allows the user to continue learning while maintaining their interest. Furthermore, the quiz generation unit uses the emotion estimation function to identify topics that interest the user most and prioritize quizzes related to those topics. This increases the user's motivation to learn. As a result, by providing quizzes based on the user's interests, it is possible to increase the user's motivation to learn.

[0080] The learning progress management unit can monitor the user's learning progress in real time and provide feedback at the appropriate time. For example, the generation AI in the learning progress management unit monitors the user's learning progress in real time and provides feedback at the appropriate time. For example, sending an encouraging message if learning stagnates. The learning progress management unit also builds a system that analyzes the user's learning progress and provides feedback at the appropriate time. For example, sending a praising message if a certain level of progress is made. The learning progress management unit also builds a system in which the generation AI monitors the user's learning progress in real time and provides feedback at the appropriate time. This allows the user to continue learning while maintaining motivation. As a result, providing feedback in real time can improve the user's learning effectiveness.

[0081] The learning progress management unit can compare the user's learning progress with other users and present the relative progress status. In the learning progress management unit, for example, the generation AI compares the user's learning progress with other users and presents the relative progress status. For example, it compares the progress with that of other users who are studying the same book. The learning progress management unit also builds a system that analyzes the user's learning progress and compares it with that of other users. This allows the user to objectively grasp their own progress status. In addition, the learning progress management unit also compares the user's learning progress with that of other users and presents the relative progress status. This allows the user to study while developing a competitive spirit. In this way, presenting the relative progress status can increase the user's motivation to study.

[0082] The learning progress management unit can use the emotion estimation function to perform motivation management to maintain the user's motivation to learn. The learning progress management unit, for example, uses the emotion estimation function to perform motivation management to maintain the user's motivation to learn. For example, it sends messages that elicit positive emotions. The learning progress management unit also performs motivation management to maintain the user's motivation to learn by having the generation AI analyze the user's emotion data. For example, it sends an encouraging message if the emotion score is low. The learning progress management unit also uses the emotion estimation function to perform motivation management to maintain the user's motivation to learn. This allows the user to continue learning while maintaining their motivation. This makes it possible to improve learning effectiveness by performing motivation management to maintain the user's motivation to learn.

[0083] The learning progress management unit visually displays the user's learning progress on a visual dashboard, allowing the user to intuitively understand. In the learning progress management unit, for example, the generation AI visually displays the user's learning progress on a visual dashboard. For example, the progress status is shown using graphs and charts. The learning progress management unit also builds a visual dashboard that visually displays the user's learning progress. This allows the user to intuitively grasp the progress status. In the learning progress management unit, the generation AI visually displays the user's learning progress on a visual dashboard. This allows the user to understand the learning progress at a glance. In this way, visually displaying the learning progress can help the user understand.

[0084] The learning progress management unit provides progress management methods according to different learning goals, making it possible to meet individual needs. In the learning progress management unit, for example, the generation AI provides a progress management method according to the user's learning goals. For example, progress management is performed according to short-term goals and long-term goals. In addition, the learning progress management unit customizes the progress management method based on the user's learning goals. This makes it possible to manage progress according to individual needs. In addition, the learning progress management unit provides progress management methods according to different learning goals. For example, progress management is performed according to exam preparation or skill acquisition. In this way, progress management according to individual learning goals can be provided, thereby improving the user's learning effectiveness.

[0085] The learning progress management unit can use the emotion estimation function to identify the progress management method that most motivates the user and provide that method preferentially. The learning progress management unit, for example, uses the emotion estimation function to identify the progress management method that most motivates the user and provide that method preferentially. For example, the learning progress management unit selects a progress management method with a high emotion score. The learning progress management unit also uses the generation AI to analyze the user's emotion data and identify the progress management method that most motivates the user and provide that method preferentially. The learning progress management unit also uses the emotion estimation function to identify the progress management method that most motivates the user and provide that method preferentially. This allows the user to continue learning while maintaining motivation. By providing the progress management method that most motivates the user, the learning effect can be improved.

[0086] The study plan providing unit can analyze the user's study history and dynamically update the optimal study plan. In the study plan providing unit, for example, a generation AI analyzes the user's study history and dynamically updates the optimal study plan. For example, the plan is adjusted according to the progress of the study. The study plan providing unit also builds a system in which the generation AI proposes the optimal study plan based on the user's study history. This allows the user to study efficiently. In addition, the study plan providing unit also analyzes the user's study history using the generation AI and dynamically updates the optimal study plan. This allows the user to always study based on the latest study plan. This makes it possible to improve learning effectiveness by providing the optimal study plan based on the user's study history.

[0087] The study plan providing unit can flexibly adjust the content of the plan to suit the user's learning style and pace. For example, the generation AI of the study plan providing unit analyzes the user's learning style and pace and flexibly adjusts the content of the study plan based on that. For example, a visual learner is provided with a plan that includes a lot of visual content. The generation AI of the study plan providing unit also adjusts the study plan to suit the user's learning pace. For example, if progress is fast, new topics are added, and if progress is slow, review is suggested. The generation AI of the study plan providing unit also analyzes the user's learning style and pace and flexibly adjusts the content of the study plan based on that. This allows the user to study at a pace that suits them. This makes it possible to improve learning effectiveness by providing a study plan that suits the user's learning style and pace.

[0088] The study plan providing unit can use the emotion estimation function to identify a plan that allows the user to study most effectively and provide that plan. The study plan providing unit, for example, uses the emotion estimation function to identify a plan that allows the user to study most effectively and provides that plan. For example, it prioritizes the adoption of study methods with high emotion scores. The study plan providing unit also uses the generation AI to analyze the user's emotion data and identify a plan that allows the user to study most effectively and provides that plan. The study plan providing unit also uses the emotion estimation function to identify a plan that allows the user to study most effectively and provides that plan. This allows the user to study efficiently. This makes it possible to improve learning effectiveness by providing a plan that allows the user to study most effectively.

[0089] The learning plan providing unit can provide a plan that combines different learning resources (e.g., online courses, videos, articles). In the learning plan providing unit, for example, the generation AI provides a learning plan that combines different learning resources. For example, the optimal learning plan is created by combining online courses, videos, and articles. In addition, the learning plan providing unit selects the optimal learning resources according to the user's learning goals and provides a plan that combines them. For example, it aggregates resources necessary to acquire specific skills. In addition, the generation AI analyzes different learning resources and suggests the optimal combination to the user. This allows the user to advance their studies by utilizing a variety of resources. As a result, by providing a plan that combines different learning resources, the user's learning effectiveness can be improved.

[0090] The study plan providing unit can incorporate opinions from experts in different fields to provide a study plan with multiple perspectives. For example, the generation AI of the study plan providing unit provides a study plan that incorporates opinions from experts in different fields. For example, the generation AI creates a plan that integrates a technical perspective and an economic perspective. The study plan providing unit also provides a study plan with multiple perspectives from the generation AI based on the opinions of experts in different fields. This allows the user to study from a wide range of perspectives. The study plan providing unit also provides a study plan with multiple perspectives from the generation AI. This allows the user to study from a multifaceted perspective. This makes it possible to improve the user's learning effectiveness by providing a study plan with multiple perspectives.

[0091] The study plan providing unit can use the emotion estimation function to prioritize providing study plans related to topics that interest the user most. The study plan providing unit, for example, uses the emotion estimation function to identify topics that interest the user most and provides study plans related to those topics. For example, topics with high emotion scores are preferentially incorporated into the study plan. The study plan providing unit also uses the generation AI to analyze the user's emotion data and provides study plans related to topics that are likely to interest the user. This allows the user to study while maintaining their interest. The study plan providing unit also uses the emotion estimation function to identify topics that interest the user most and provides study plans related to those topics. This increases the user's motivation to study. This makes it possible to improve learning effectiveness by providing study plans based on the user's interests.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can link related past learning content based on the user's learning history. For example, the generation AI analyzes the user's learning history and includes parts related to previously learned content in the summary. This allows the user to understand new knowledge by linking it to existing knowledge. The analysis unit also allows the generation AI to customize the summary content based on the user's interests and concerns. For example, a user who is interested in a particular topic will be given priority in receiving summaries related to that topic. The analysis unit also allows the generation AI to link previously learned content with new content based on the user's learning history. This allows the user to have a consistent learning experience. This enables efficient learning by providing summaries based on the user's learning history and interests.

[0094] The analysis unit evaluates the user's level of comprehension in real time and can focus on summarizing parts where understanding is shallow. For example, the generation AI analyzes the contents of a book and evaluates the user's level of comprehension based on their answers and reactions. For parts where understanding is shallow, a detailed summary is generated and provided to the user. The analysis unit also monitors the user's level of comprehension in real time as they read the book, identifying parts where understanding is shallow. For those parts, additional summaries and explanations are provided. The analysis unit also allows the generation AI to analyze the user's learning history and re-summarize parts where understanding was shallow in the past. This enables efficient learning by providing summaries that match the user's level of comprehension.

[0095] The analysis unit uses the emotion estimation function to generate summaries that are likely to interest the user, thereby increasing their motivation to learn. For example, the generation AI analyzes the user's emotions and generates summaries that are likely to interest the user. For example, parts with strong positive emotions are emphasized and included in the summary. The analysis unit also uses the emotion estimation function to identify topics that are likely to interest the user and provide summaries related to those topics. This increases the user's motivation to learn. The analysis unit also uses the generation AI to generate summaries that are likely to interest the user based on the user's emotion data. For example, it provides detailed summaries of parts that the user found interesting. This increases the user's motivation to learn by providing summaries based on the user's interests.

[0096] The analysis unit summarizes the analyzed content in audio or video format, supporting learning through sight and hearing. For example, the generation AI analyzes the contents of a book and generates a summary in audio format. By listening to the audio, users can advance their learning in a way other than visually. The analysis unit also allows the generation AI to analyze the contents of a book and generate a summary in video format. For example, it provides a video that explains important points visually. The analysis unit also allows the generation AI to provide a summary in audio or video format so that users can learn through sight and hearing. This accommodates different learning styles. This makes it possible to support learning through sight and hearing.

[0097] The analysis unit can cross-reference the contents of books from different genres and provide summaries that integrate related knowledge. For example, the generation AI can analyze the contents of books from different genres and generate summaries that integrate related knowledge. For example, a summary that combines scientific and historical knowledge can be provided. The analysis unit can also cross-reference the contents of books from different genres, find common themes and topics, and generate summaries based on those. The generation AI can also analyze the contents of books from different genres and provide summaries that integrate related knowledge. This allows users to learn from a broad range of perspectives. By providing summaries that integrate knowledge from different genres, learning from a broad range of perspectives is possible.

[0098] The analysis unit can use the emotion estimation function to identify the topic in which the user is most interested and prioritize providing summaries related to that topic. For example, the emotion estimation function can be used to identify the topic in which the user is most interested and generate summaries related to that topic. For example, parts with a high user emotion score can be prioritized for summarization. The analysis unit also uses the generation AI to analyze the user's emotion data and identify topics that are likely to interest the user. The analysis unit then prioritizes providing summaries related to that topic. The analysis unit also uses the emotion estimation function to identify the topic in which the user is most interested and generate summaries related to that topic. This increases the user's motivation to learn. By providing summaries based on the user's interests, the user's motivation to learn can be increased.

[0099] The important point extraction unit can reevaluate the important points based on the user's learning goals and present the most appropriate points. For example, the generation AI reevaluates the important points extracted from the book's contents based on the user's learning goals and presents the most appropriate points. For example, it prioritizes points necessary for exam preparation. The important point extraction unit also reevaluates the important points based on the user's learning goals and presents the most appropriate points. For example, it emphasizes points necessary for acquiring specific skills. The important point extraction unit also analyzes the user's learning goals and reevaluates the important points based on them. This allows the user to study efficiently. By providing important points according to the user's learning goals, efficient learning is possible.

[0100] The key point extraction unit can compare the key points extracted by the generation AI with other related literature and databases to increase reliability. For example, the key points extracted by the generation AI can be compared with other related literature and databases to confirm reliability. For example, databases of academic papers and specialized books can be referenced. The key point extraction unit can also compare the extracted key points with other related literature to build a system to increase reliability. For example, it can evaluate reliability based on citation sources and references. The key point extraction unit can also compare the key points extracted by the generation AI with other databases to increase reliability. This allows users to obtain highly reliable information. By providing highly reliable information, the user's learning effectiveness can be improved.

[0101] The important point extraction unit can use the emotion estimation function to identify the important points that the user is most interested in and emphasize those points. For example, the emotion estimation function can be used to identify the important points that the user is most interested in and emphasize those points. For example, parts with high emotion scores can be presented preferentially. The important point extraction unit also uses the generation AI to analyze the user's emotion data and identify important points that are likely to interest the user. Those points are emphasized and provided. The important point extraction unit also uses the emotion estimation function to identify the important points that the user is most interested in and emphasize those points. This allows the user to study efficiently. This makes it possible to improve learning effectiveness by providing important points based on the user's interests.

[0102] The important point extraction unit can present the important points in an interactive mind map format, making them easier to understand visually. For example, the important points extracted by the generation AI can be presented in an interactive mind map format. The user can visually understand the important points while operating the mind map. The important point extraction unit also displays the extracted important points in mind map format, allowing the user to access detailed information by clicking and zooming. The important point extraction unit also presents the important points extracted by the generation AI as an interactive mind map. This allows the user to intuitively understand the important points. This improves learning effectiveness by providing important points in a format that is easy to understand visually.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The analysis unit analyzes the content of the book. For example, the analysis unit analyzes the content of the book using text mining technology, natural language processing technology, or a machine learning algorithm. Step 2: The summary generator summarizes the content analyzed by the analyzer. For example, the summary generator may use a generative AI, a text generation AI (e.g., LLM), or a multimodal generation AI to summarize the book's contents. Step 3: The key point extractor extracts key points from the summary generated by the summary generator. For example, the key point extractor extracts key points based on frequently occurring keywords, central thematic concepts, or definitions and theories. Step 4: The quiz generation unit generates a quiz based on the important points extracted by the important point extraction unit. For example, the quiz generation unit generates a multiple-choice quiz, a written quiz, or a quiz with adjusted difficulty.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 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.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the 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.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 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. an analysis unit that analyzes the contents of the book; a summary generation unit that summarizes the content analyzed by the analysis unit; an important point extraction unit that extracts important points from the summary generated by the summary generation unit; a quiz generation unit that generates a quiz based on the important points extracted by the important point extraction unit. A system characterized by:

2. The analysis unit Evaluate the user's level of understanding in real time and summarize the areas where they have little understanding 2. The system of claim 1.

3. The analysis unit Personalize summaries based on your learning history and interests, linking them to relevant past learning 2. The system of claim 1.

4. The analysis unit Generate summaries that are interesting to users and increase their motivation to learn 2. The system of claim 1.

5. The analysis unit Analyzed content is summarized in audio and video formats to support learning through visual and auditory means.

2. The system of claim 1.

6. The analysis unit Cross-references the contents of books from different genres and provides summaries that integrate relevant knowledge 2. The system of claim 1.

Images