System

Abstract

An object of a system according to an embodiment is to efficiently generate an exercise for an examination measure and provide an explanation.SOLUTION: A system includes a book content learning part, a question generation part, a correct / incorrect determination part, and an explanation output part. The book contents learning unit learns contents of a book. The question generation unit generates questions in a question-and-answer format on the basis of the contents learned by the book content learning unit. The correct / incorrect determination unit analyzes the user's answer to the problem generated by the problem generation unit and determines whether the answer is correct or incorrect. The explanation output unit outputs an explanation based on the result determined by the correct / incorrect determination 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 techniques have not been sufficient in efficiently generating practice questions for exam preparation and providing explanations, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently generate practice questions for exam preparation and provide explanations. [Means for solving the problem]

[0006] The system according to the embodiment includes a book content learning unit, a question generation unit, a correct / incorrect judgment unit, and an explanation output unit. The book content learning unit learns the content of a book. The question generation unit generates questions in a question-and-answer format based on the content learned by the book content learning unit. The correct / incorrect judgment unit analyzes the user's answers to the questions generated by the question generation unit and judges whether the answers are correct. The explanation output unit outputs an explanation based on the results judged by the correct / incorrect judgment unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate practice questions for exam preparation and provide explanations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The practice support system according to an embodiment of the present invention is a system that aims to provide practice support for popular exams in the final stages. This system uses a generation AI to study the contents of a book, pose questions in a question-and-answer format, and output correct / incorrect answers and explanations. This allows the practice support system to enable users to efficiently prepare for exams.

[0029] The exercise assistance system according to the embodiment includes a book content learning unit, a question generation unit, a correct / incorrect judgment unit, and an explanation output unit. The book content learning unit learns the content of the book. For example, the generation AI inputs text data from a book for exam preparation and understands and analyzes the content. The generation AI can also comprehensively grasp the content of the book. The question generation unit generates questions in a question-and-answer format based on the content learned by the book content learning unit. For example, the generation AI receives a prompt such as, "Please give me a question for the IT Passport exam," and generates an appropriate question. The generation AI can also appropriately select the content of the question and present it to the user. The correct / incorrect judgment unit analyzes the user's answer to the question generated by the question generation unit and judges whether it is correct or incorrect. For example, if the user answers "confidentiality, integrity, and availability" to the question, "What are the basic concepts of information security?", the generation AI judges the answer to be correct. The generation AI can also quickly and accurately analyze the user's answer. The explanation output unit outputs an explanation based on the result determined by the correct / incorrect judgment unit. For example, the system outputs an explanation such as, "Confidentiality, integrity, and availability are the three major elements of information security, respectively referring to keeping information secret, maintaining the accuracy of information, and making information available when needed." The generation AI can also provide explanations that include the background of the questions and related knowledge. This allows the practice assistance system according to the embodiment to efficiently prepare for exams. For example, in the run-up to the IT Passport Exam or Bookkeeping Exam, users can confirm and deepen their understanding through practice questions using the generation AI. Furthermore, the explanations allow users to understand the causes of incorrect answers and study effectively for the next exam.

[0030] The book content learning unit can learn not only the contents of books, but also data from past exam questions and mock exams. For example, when the book content learning unit has the generation AI learn the contents of a book, it also simultaneously inputs data from past exam questions and mock exams. For example, adding data from past questions for the IT Passport exam makes it easier to understand exam question trends. The generation AI can also analyze data from past exam questions and mock exams to identify important points. This allows it to build more comprehensive knowledge.

[0031] The book content learning unit allows the generation AI to automatically extract important keywords and concepts and highlight them when studying the contents of a book. For example, the book content learning unit allows the generation AI to analyze the contents of a book and automatically extract important keywords and concepts. For example, it highlights important terms such as "information security" and "network basics." In addition, by highlighting important keywords and concepts, the generation AI can enable the user to grasp important information at a glance. This allows the user to grasp important information at a glance.

[0032] The book content learning unit can learn not only the content of a book, but also the content of related video lectures and online courses, and integrate multimedia information. For example, the book content learning unit has the generation AI learn the content of related video lectures in addition to the content of a book. For example, by adding video lectures for the IT Passport exam, visual information is also integrated. The generation AI can also learn the content of online courses and integrate multimedia information. This enables more diversified learning.

[0033] The book content learning unit can compare the contents of different test preparation books and automatically extract similarities and differences. For example, the book content learning unit has the generation AI learn the contents of different test preparation books and automatically extract similarities and differences. For example, it compares multiple books for the IT Passport exam and identifies important points. The generation AI can also compare the contents of different test preparation books and extract similarities and differences, allowing it to build more comprehensive knowledge. This allows it to build more comprehensive knowledge.

[0034] The book content learning unit allows the generation AI to automatically search for related real-world examples and case studies when studying the content of a book and add them to the study content. For example, when the generation AI studies the content of a book, the book content learning unit automatically searches for related real-world examples and adds them to the study content. For example, adding the latest security examples related to the content of the IT Passport Examination. The generation AI can also build more practical knowledge by searching for case studies and adding them to the study content. This allows the generation AI to build more practical knowledge.

[0035] The question generation unit can analyze the user's past answer history and prioritize questions in areas where the user is weak or where the user frequently gets the questions wrong. For example, the question generation unit uses a generation AI to analyze the user's past answer history and identify areas where the user is weak. For example, it prioritizes questions where the user frequently gets the questions wrong. Furthermore, the generation AI can analyze the user's past answer history and prioritize questions in areas where the user is weak or where the user frequently gets the questions wrong, allowing the user to focus on learning their weak points. This allows the user to focus on learning their weak points.

[0036] The question generation unit can automatically adjust the difficulty of the questions and present questions of an appropriate level according to the user's learning progress. For example, the question generation unit uses a generation AI to analyze the user's learning progress and automatically present questions of an appropriate level of difficulty. For example, basic questions are presented to beginners, and applied questions are presented to advanced learners. The generation AI can also automatically adjust the difficulty of the questions and present questions of an appropriate level according to the user's learning progress. This makes it possible to provide appropriate questions according to the user's learning progress.

[0037] The question generation unit can provide a variety of question formats by generating multiple choice and written questions in addition to question-and-answer format. For example, the question generation unit generates multiple choice questions for the IT Passport Examination and provides them to the user. The generation AI can also generate written questions and provide a variety of question formats, allowing the user to deal with a variety of question formats.

[0038] The question generation unit can combine questions from different test subjects or fields to generate questions that test comprehensive knowledge. The question generation unit, for example, combines questions from different test subjects to generate questions. For example, it combines questions from the IT Passport exam and the bookkeeping exam to generate questions that test comprehensive knowledge. The generation AI can also combine questions from different fields to generate questions that test comprehensive knowledge. This makes it possible to evaluate the user's comprehensive knowledge.

[0039] The question generation unit automatically generates related diagrams and graphs, making it possible to present questions that are visually easy to understand. For example, the question generation unit uses a generation AI to automatically generate diagrams and graphs related to the question. For example, it generates related diagrams for questions about network topology in the IT Passport exam. In addition, the generation AI presents questions that are visually easy to understand, making it easier for users to understand the questions. This makes it possible to provide questions that are visually easy for users to understand.

[0040] When analyzing a user's answers, the correctness determination unit can also perform a detailed analysis of the reasons for partial correct answers and incorrect answers and provide feedback. For example, the correctness determination unit allows the generation AI to analyze a user's answers and identify partial correct answers. For example, if a user provides a partially correct answer to a question in the IT Passport exam, that part is provided as feedback. The generation AI can also perform a detailed analysis of the reasons for incorrect answers and provide feedback to the user. This makes it clear which part the user made a mistake in.

[0041] When determining whether a question is correct or incorrect, the correctness determination unit allows the generation AI to suggest related reference literature and additional learning resources. For example, the correctness determination unit allows the generation AI to suggest related reference literature based on the results of the correctness determination. For example, the generation AI may suggest related books and papers for questions on the IT Passport Examination. Furthermore, by suggesting additional learning resources, the generation AI can provide reference literature and learning resources to help the user deepen their understanding. This makes it possible to provide reference literature and learning resources to help the user deepen their understanding.

[0042] The correct / incorrect judgment unit can compare the result of the correct / incorrect judgment with other users and provide a relative evaluation. For example, the generation AI compares the result of the correct / incorrect judgment with the results of other users and provides a relative evaluation. For example, the generation AI displays the correct answer rate of other users for questions on the IT Passport exam. In addition, the generation AI compares the result of the correct / incorrect judgment with other users and provides a relative evaluation, allowing the user to understand their own position. This allows the user to understand their own position.

[0043] The correctness determination unit can provide a dashboard that visualizes the user's learning progress based on the results of the correctness determination. For example, the generation AI provides a dashboard that visualizes the user's learning progress based on the results of the correctness determination. For example, the generation AI displays the progress of the IT Passport exam in a graph. Furthermore, by providing a dashboard that visualizes the user's learning progress, the generation AI allows the user to grasp the learning progress at a glance. This allows the user to grasp the learning progress at a glance.

[0044] The correctness judgment unit allows the generation AI to automatically present similar questions to prevent the user from making the same mistake again. For example, the generation AI analyzes the user's incorrect answers and automatically presents similar questions. For example, it presents a different question that tests the same concept as a question in the IT Passport exam. The generation AI can also present similar questions to prevent the user from making the same mistake again. This prevents the user from making the same mistake again.

[0045] When outputting an explanation, the explanation output unit generates related videos and animations, making it possible to provide explanations that are visually easy to understand. For example, when the generation AI outputs an explanation, the explanation output unit automatically generates related videos. For example, related videos are displayed for explanations of the IT Passport exam. The generation AI can also generate animations to provide explanations that are visually easy to understand. This makes it possible to provide explanations that are visually easy for users to understand.

[0046] The explanation output unit can customize the content of the explanation according to the user's level of understanding and provide explanations for beginners and advanced users. For example, the generation AI analyzes the user's level of understanding and customizes the explanations for beginners and advanced users. For example, for the explanations of the IT Passport exam, basic explanations are provided to beginners and detailed explanations are provided to advanced users. Furthermore, by customizing the explanations according to the user's level of understanding, the generation AI can provide explanations that suit the user's level of understanding. This makes it possible to provide explanations that suit the user's level of understanding.

[0047] The explanation output unit can add a function to share the content of the explanation with other users and receive community-based feedback. The explanation output unit adds a function to share the explanation output by the generation AI with other users, for example, sharing explanations for the IT Passport exam and receiving feedback from other users. Furthermore, the generation AI can improve the quality of its explanations by receiving community-based feedback. This allows it to receive feedback to improve the quality of its explanations.

[0048] The explanation output unit can automatically translate the content of the explanation into different languages, making it possible to accommodate international users. For example, the explanation output unit adds a function to automatically translate the explanation output by the generation AI into different languages. For example, it translates explanations for the IT Passport exam into English and Chinese. In addition, the generation AI can automatically translate into different languages, making it possible to accommodate international users. This makes it possible to accommodate international users.

[0049] The explanation output unit allows the generating AI to automatically add relevant real-world examples and case studies to the explanation, promoting practical understanding. For example, when the generating AI outputs an explanation, the explanation output unit automatically adds relevant real-world examples. For example, it adds the latest security examples to the explanation for the IT Passport exam. In addition, the generating AI can add case studies to the explanation, allowing the user to deepen their practical understanding. This allows the user to deepen their practical understanding.

[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 practice assistance system can provide customized learning plans according to the user's learning style. For example, for users who prefer visual learning, it can provide learning materials that make extensive use of charts and graphs. For users who prefer auditory learning, it can provide learning materials in the form of audio commentary or podcasts. Furthermore, the generative AI can monitor the user's learning progress and adjust the learning plan as needed. This allows users to study efficiently in the way that best suits them.

[0052] The exercise assistance system allows users to input questions while studying, and the generative AI can provide answers in real time. For example, if a user inputs "I'd like to know more about this concept," the generative AI will provide related information and additional explanations. The generative AI can also automatically generate an FAQ section that lists frequently asked questions based on the user's question history. This allows users to quickly resolve their questions and progress smoothly through their studies.

[0053] The exercise support system can provide a function that allows users to share what they have learned with other users and learn collaboratively. For example, users can share their study notes and explanations with other users and exchange opinions and hold discussions. The generative AI can also monitor the progress of collaborative learning and provide advice to improve the learning effectiveness of the entire group. This allows users to gain a deeper understanding while cooperating with other learners.

[0054] The practice assistance system can set individual learning goals based on the user's learning data and provide a function to visualize the progress of those goals. For example, if a user sets a goal of "mastering a specific subject by the end of the month," the generative AI can monitor the progress and display the progress in graphs and charts. It can also provide advice and reminders to help users achieve their goals. This allows users to work effectively toward their learning goals.

[0055] The practice assistance system can provide a function to simulate what the user has learned in an actual exam environment. For example, the generative AI can provide a simulation in which the user solves problems in the same format as the actual exam. It can also set an exam time and allow the user to practice solving problems within the time limit. Furthermore, it can provide feedback on the user's weaknesses and areas for improvement based on the simulation results. This allows the user to make practical preparations for the actual exam.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The book content learning unit learns the contents of the book. For example, the generation AI inputs text data from a book for exam preparation and understands and analyzes its contents. The generation AI can also comprehensively grasp the contents of the book. Step 2: The question generation unit generates questions in a question-and-answer format based on the content learned by the book content learning unit. For example, the generation AI receives a prompt such as "Please provide questions for the IT Passport Examination" and generates appropriate questions. The generation AI can also select appropriate question content and present them to the user. Step 3: The correctness judgment unit analyzes the user's answers to the questions generated by the question generation unit and judges whether they are correct or incorrect. For example, if a user answers "confidentiality, integrity, and availability" to the question "What are the basic concepts of information security?", the generation AI judges that answer to be correct. The generation AI can also analyze the user's answers quickly and accurately. Step 4: The explanation output unit outputs an explanation based on the results of the judgment by the accuracy judgment unit. For example, it outputs an explanation such as, "Confidentiality, integrity, and availability are the three major elements of information security, and refer to keeping information secret, maintaining the accuracy of information, and making information available when needed, respectively." The generative AI can also provide an explanation that includes the background of the problem and related knowledge.

[0058] (Example 2) The practice support system according to an embodiment of the present invention is a system that aims to provide practice support for popular exams in the final stages. This system uses a generation AI to study the contents of a book, pose questions in a question-and-answer format, and output correct / incorrect answers and explanations. This allows the practice support system to enable users to efficiently prepare for exams.

[0059] The exercise assistance system according to the embodiment includes a book content learning unit, a question generation unit, a correct / incorrect judgment unit, and an explanation output unit. The book content learning unit learns the content of the book. For example, the generation AI inputs text data from a book for exam preparation and understands and analyzes the content. The generation AI can also comprehensively grasp the content of the book. The question generation unit generates questions in a question-and-answer format based on the content learned by the book content learning unit. For example, the generation AI receives a prompt such as, "Please give me a question for the IT Passport exam," and generates an appropriate question. The generation AI can also appropriately select the content of the question and present it to the user. The correct / incorrect judgment unit analyzes the user's answer to the question generated by the question generation unit and judges whether it is correct or incorrect. For example, if the user answers "confidentiality, integrity, and availability" to the question, "What are the basic concepts of information security?", the generation AI judges the answer to be correct. The generation AI can also quickly and accurately analyze the user's answer. The explanation output unit outputs an explanation based on the result determined by the correct / incorrect judgment unit. For example, the system outputs an explanation such as, "Confidentiality, integrity, and availability are the three major elements of information security, respectively referring to keeping information secret, maintaining the accuracy of information, and making information available when needed." The generation AI can also provide explanations that include the background of the questions and related knowledge. This allows the practice assistance system according to the embodiment to efficiently prepare for exams. For example, in the run-up to the IT Passport Exam or Bookkeeping Exam, users can confirm and deepen their understanding through practice questions using the generation AI. Furthermore, the explanations allow users to understand the causes of incorrect answers and study effectively for the next exam.

[0060] The book content learning unit can learn not only the contents of books, but also data from past exam questions and mock exams. For example, when the book content learning unit has the generation AI learn the contents of a book, it also simultaneously inputs data from past exam questions and mock exams. For example, adding data from past questions for the IT Passport exam makes it easier to understand exam question trends. The generation AI can also analyze data from past exam questions and mock exams to identify important points. This allows it to build more comprehensive knowledge.

[0061] The book content learning unit allows the generation AI to automatically extract important keywords and concepts and highlight them when studying the contents of a book. For example, the book content learning unit allows the generation AI to analyze the contents of a book and automatically extract important keywords and concepts. For example, it highlights important terms such as "information security" and "network basics." In addition, by highlighting important keywords and concepts, the generation AI can enable the user to grasp important information at a glance. This allows the user to grasp important information at a glance.

[0062] The book content learning unit uses the emotion estimation function to analyze the user's emotional response to the book content, and can prioritize learning of content that is likely to interest the user. The book content learning unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time when reading the book content. For example, it prioritizes learning of parts that the user is interested in. In addition, the generation AI analyzes the user's emotional response and prioritizes learning of content that is likely to interest the user. This makes it possible to prioritize learning of content that is likely to interest the user.

[0063] The book content learning unit can learn not only the content of a book, but also the content of related video lectures and online courses, and integrate multimedia information. For example, the book content learning unit has the generation AI learn the content of related video lectures in addition to the content of a book. For example, by adding video lectures for the IT Passport exam, visual information is also integrated. The generation AI can also learn the content of online courses and integrate multimedia information. This enables more diversified learning.

[0064] The book content learning unit can compare the contents of different test preparation books and automatically extract similarities and differences. For example, the book content learning unit has the generation AI learn the contents of different test preparation books and automatically extract similarities and differences. For example, it compares multiple books for the IT Passport exam and identifies important points. The generation AI can also compare the contents of different test preparation books and extract similarities and differences, allowing it to build more comprehensive knowledge. This allows it to build more comprehensive knowledge.

[0065] The book content learning unit allows the generation AI to automatically search for related real-world examples and case studies when studying the content of a book and add them to the study content. For example, when the generation AI studies the content of a book, the book content learning unit automatically searches for related real-world examples and adds them to the study content. For example, adding the latest security examples related to the content of the IT Passport Examination. The generation AI can also build more practical knowledge by searching for case studies and adding them to the study content. This allows the generation AI to build more practical knowledge.

[0066] The question generation unit can analyze the user's past answer history and prioritize questions in areas where the user is weak or where the user frequently gets the questions wrong. For example, the question generation unit uses a generation AI to analyze the user's past answer history and identify areas where the user is weak. For example, it prioritizes questions where the user frequently gets the questions wrong. Furthermore, the generation AI can analyze the user's past answer history and prioritize questions in areas where the user is weak or where the user frequently gets the questions wrong, allowing the user to focus on learning their weak points. This allows the user to focus on learning their weak points.

[0067] The question generation unit can automatically adjust the difficulty of the questions and present questions of an appropriate level according to the user's learning progress. For example, the question generation unit uses a generation AI to analyze the user's learning progress and automatically present questions of an appropriate level of difficulty. For example, basic questions are presented to beginners, and applied questions are presented to advanced learners. The generation AI can also automatically adjust the difficulty of the questions and present questions of an appropriate level according to the user's learning progress. This makes it possible to provide appropriate questions according to the user's learning progress.

[0068] The question generation unit can use the emotion estimation function to avoid questions that cause stress to the user and present questions that the user can answer in a relaxed manner. The question generation unit, for example, uses the emotion estimation function to avoid questions that cause stress to the user. For example, it prioritizes questions that the user can answer in a relaxed manner. In addition, the generation AI can analyze the user's emotional reactions, avoid questions that cause stress, and present questions that the user can answer in a relaxed manner. This allows the user to study in a relaxed manner.

[0069] The question generation unit can provide a variety of question formats by generating multiple choice and written questions in addition to question-and-answer format. For example, the question generation unit generates multiple choice questions for the IT Passport Examination and provides them to the user. The generation AI can also generate written questions and provide a variety of question formats, allowing the user to deal with a variety of question formats.

[0070] The question generation unit can combine questions from different test subjects or fields to generate questions that test comprehensive knowledge. The question generation unit, for example, combines questions from different test subjects to generate questions. For example, it combines questions from the IT Passport exam and the bookkeeping exam to generate questions that test comprehensive knowledge. The generation AI can also combine questions from different fields to generate questions that test comprehensive knowledge. This makes it possible to evaluate the user's comprehensive knowledge.

[0071] The question generation unit automatically generates related diagrams and graphs, making it possible to present questions that are visually easy to understand. For example, the question generation unit uses a generation AI to automatically generate diagrams and graphs related to the question. For example, it generates related diagrams for questions about network topology in the IT Passport exam. In addition, the generation AI presents questions that are visually easy to understand, making it easier for users to understand the questions. This makes it possible to provide questions that are visually easy for users to understand.

[0072] When analyzing a user's answers, the correctness determination unit can also perform a detailed analysis of the reasons for partial correct answers and incorrect answers and provide feedback. For example, the correctness determination unit allows the generation AI to analyze a user's answers and identify partial correct answers. For example, if a user provides a partially correct answer to a question in the IT Passport exam, that part is provided as feedback. The generation AI can also perform a detailed analysis of the reasons for incorrect answers and provide feedback to the user. This makes it clear which part the user made a mistake in.

[0073] When determining whether a question is correct or incorrect, the correctness determination unit allows the generation AI to suggest related reference literature and additional learning resources. For example, the correctness determination unit allows the generation AI to suggest related reference literature based on the results of the correctness determination. For example, the generation AI may suggest related books and papers for questions on the IT Passport Examination. Furthermore, by suggesting additional learning resources, the generation AI can provide reference literature and learning resources to help the user deepen their understanding. This makes it possible to provide reference literature and learning resources to help the user deepen their understanding.

[0074] The correct / incorrect determination unit can use the emotion estimation function to analyze the emotional response of the user when they give an incorrect answer and display an encouraging message to maintain their motivation. The correct / incorrect determination unit, for example, uses the emotion estimation function to analyze the emotional response of the user when they give an incorrect answer in real time. For example, an encouraging message is displayed if the user feels depressed. The generation AI can also analyze the user's emotional response and display an encouraging message to maintain their motivation. This helps maintain the user's motivation.

[0075] The correct / incorrect judgment unit can compare the result of the correct / incorrect judgment with other users and provide a relative evaluation. For example, the generation AI compares the result of the correct / incorrect judgment with the results of other users and provides a relative evaluation. For example, the generation AI displays the correct answer rate of other users for questions on the IT Passport exam. In addition, the generation AI compares the result of the correct / incorrect judgment with other users and provides a relative evaluation, allowing the user to understand their own position. This allows the user to understand their own position.

[0076] The correctness determination unit can provide a dashboard that visualizes the user's learning progress based on the results of the correctness determination. For example, the generation AI provides a dashboard that visualizes the user's learning progress based on the results of the correctness determination. For example, the generation AI displays the progress of the IT Passport exam in a graph. Furthermore, by providing a dashboard that visualizes the user's learning progress, the generation AI allows the user to grasp the learning progress at a glance. This allows the user to grasp the learning progress at a glance.

[0077] The correctness judgment unit allows the generation AI to automatically present similar questions to prevent the user from making the same mistake again. For example, the generation AI analyzes the user's incorrect answers and automatically presents similar questions. For example, it presents a different question that tests the same concept as a question in the IT Passport exam. The generation AI can also present similar questions to prevent the user from making the same mistake again. This prevents the user from making the same mistake again.

[0078] When outputting an explanation, the explanation output unit generates related videos and animations, making it possible to provide explanations that are visually easy to understand. For example, when the generation AI outputs an explanation, the explanation output unit automatically generates related videos. For example, related videos are displayed for explanations of the IT Passport exam. The generation AI can also generate animations to provide explanations that are visually easy to understand. This makes it possible to provide explanations that are visually easy for users to understand.

[0079] The explanation output unit can customize the content of the explanation according to the user's level of understanding and provide explanations for beginners and advanced users. For example, the generation AI analyzes the user's level of understanding and customizes the explanations for beginners and advanced users. For example, for the explanations of the IT Passport exam, basic explanations are provided to beginners and detailed explanations are provided to advanced users. Furthermore, by customizing the explanations according to the user's level of understanding, the generation AI can provide explanations that suit the user's level of understanding. This makes it possible to provide explanations that suit the user's level of understanding.

[0080] The explanation output unit can use the emotion estimation function to analyze the emotional reaction of the user when reading the explanation and provide additional explanation to enhance understanding. The explanation output unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when reading the explanation in real time. For example, it provides additional explanation if the user does not understand. In addition, the generation AI can analyze the user's emotional reaction and provide additional explanation to enhance understanding. This makes it possible to provide additional explanation to enhance the user's understanding.

[0081] The explanation output unit can add a function to share the content of the explanation with other users and receive community-based feedback. The explanation output unit adds a function to share the explanation output by the generation AI with other users, for example, sharing explanations for the IT Passport exam and receiving feedback from other users. Furthermore, the generation AI can improve the quality of its explanations by receiving community-based feedback. This allows it to receive feedback to improve the quality of its explanations.

[0082] The explanation output unit can automatically translate the content of the explanation into different languages, making it possible to accommodate international users. For example, the explanation output unit adds a function to automatically translate the explanation output by the generation AI into different languages. For example, it translates explanations for the IT Passport exam into English and Chinese. In addition, the generation AI can automatically translate into different languages, making it possible to accommodate international users. This makes it possible to accommodate international users.

[0083] The explanation output unit allows the generating AI to automatically add relevant real-world examples and case studies to the explanation, promoting practical understanding. For example, when the generating AI outputs an explanation, the explanation output unit automatically adds relevant real-world examples. For example, it adds the latest security examples to the explanation for the IT Passport exam. In addition, the generating AI can add case studies to the explanation, allowing the user to deepen their practical understanding. This allows the user to deepen their practical understanding.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The practice assistance system can provide customized learning plans according to the user's learning style. For example, for users who prefer visual learning, it can provide learning materials that make extensive use of charts and graphs. For users who prefer auditory learning, it can provide learning materials in the form of audio commentary or podcasts. Furthermore, the generative AI can monitor the user's learning progress and adjust the learning plan as needed. This allows users to study efficiently in the way that best suits them.

[0086] The practice assistance system can provide individualized feedback based on the user's learning history. For example, if a user repeatedly makes mistakes on problems in a particular area, it can provide additional learning materials and practice problems specific to that area. The generative AI can also analyze the user's learning history and provide encouraging messages and advice based on their progress. This allows users to understand their own learning situation and study effectively.

[0087] The exercise assistance system allows users to input questions while studying, and the generative AI can provide answers in real time. For example, if a user inputs "I'd like to know more about this concept," the generative AI will provide related information and additional explanations. The generative AI can also automatically generate an FAQ section that lists frequently asked questions based on the user's question history. This allows users to quickly resolve their questions and progress smoothly through their studies.

[0088] The practice assistance system uses emotion estimation to monitor the user's emotional state while studying and suggests taking a break at the appropriate time. For example, if the user is feeling tired or stressed, the generative AI will suggest, "Let's take a short break." It can also provide short meditation or stretching videos to help users relax. This allows users to take appropriate breaks while maintaining their concentration and continuing their studies.

[0089] The exercise support system can provide a function that allows users to share what they have learned with other users and learn collaboratively. For example, users can share their study notes and explanations with other users and exchange opinions and hold discussions. The generative AI can also monitor the progress of collaborative learning and provide advice to improve the learning effectiveness of the entire group. This allows users to gain a deeper understanding while cooperating with other learners.

[0090] The practice assistance system can use its emotion estimation function to provide support to reduce the anxiety and stress that users feel about specific problems. For example, when a user faces a difficult problem, the AI ​​generation system can display an encouraging message such as, "This problem is a little difficult, but try to tackle it calmly." It can also play music or environmental sounds to help users relax. This allows users to continue learning with peace of mind.

[0091] The practice assistance system can set individual learning goals based on the user's learning data and provide a function to visualize the progress of those goals. For example, if a user sets a goal of "mastering a specific subject by the end of the month," the generative AI can monitor the progress and display the progress in graphs and charts. It can also provide advice and reminders to help users achieve their goals. This allows users to work effectively toward their learning goals.

[0092] The practice assistance system can use its emotion estimation function to analyze fluctuations in the user's motivation while studying and provide ideas to help them maintain their motivation. For example, if a user is losing interest in studying, the generative AI can display an encouraging message such as, "You've done well so far! You're almost there!" It can also provide small rewards and badges according to the user's progress. This allows the user to continue studying while maintaining their motivation.

[0093] The practice assistance system can provide a function to simulate what the user has learned in an actual exam environment. For example, the generative AI can provide a simulation in which the user solves problems in the same format as the actual exam. It can also set an exam time and allow the user to practice solving problems within the time limit. Furthermore, it can provide feedback on the user's weaknesses and areas for improvement based on the simulation results. This allows the user to make practical preparations for the actual exam.

[0094] The practice assistance system uses its emotion estimation function to analyze the sense of accomplishment and satisfaction that users feel while learning, and can provide positive feedback. For example, when a user solves a difficult problem, the generation AI displays a positive message such as, "Great! Solving this problem is a great step forward." It can also record the user's learning results and generate a report that gives them a sense of accomplishment. This allows users to realize the results of their learning and increases their motivation.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The book content learning unit learns the contents of the book. For example, the generation AI inputs text data from a book for exam preparation and understands and analyzes its contents. The generation AI can also comprehensively grasp the contents of the book. Step 2: The question generation unit generates questions in a question-and-answer format based on the content learned by the book content learning unit. For example, the generation AI receives a prompt such as "Please provide questions for the IT Passport Examination" and generates appropriate questions. The generation AI can also select appropriate question content and present them to the user. Step 3: The correctness judgment unit analyzes the user's answers to the questions generated by the question generation unit and judges whether they are correct or incorrect. For example, if a user answers "confidentiality, integrity, and availability" to the question "What are the basic concepts of information security?", the generation AI judges that answer to be correct. The generation AI can also analyze the user's answers quickly and accurately. Step 4: The explanation output unit outputs an explanation based on the results of the judgment by the accuracy judgment unit. For example, it outputs an explanation such as, "Confidentiality, integrity, and availability are the three major elements of information security, and refer to keeping information secret, maintaining the accuracy of information, and making information available when needed, respectively." The generative AI can also provide an explanation that includes the background of the problem and related knowledge.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 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.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0114] The data processing system 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a book content learning unit for learning the contents of a book; a question generation unit that generates questions in a question-and-answer format based on the content learned by the book content learning unit; a correctness determination unit that analyzes the user's answers to the questions generated by the question generation unit and determines whether the answers are correct; a comment output unit that outputs a comment based on the result determined by the correctness determination unit. A system characterized by:

2. The book content learning unit When studying the contents of a book, generative AI automatically extracts and highlights important keywords and concepts.

2. The system of claim 1.

3. The book content learning unit Integrate multimedia information by studying not only the content of books but also the content of related video lectures and online courses.

2. The system of claim 1.

4. The question generator Analyze the user's past answer history and prioritize questions in areas where the user is weak or frequently gets the questions wrong.

2. The system of claim 1.

5. The correctness determination unit When analyzing the user's answers, the reasons for partial correct answers and incorrect answers are also analyzed in detail, and feedback is provided.

2. The system of claim 1.

6. The explanation output unit When outputting explanations, related videos and animations are generated to provide visually easy-to-understand explanations.

2. The system of claim 1.

7. The book content learning unit Analyzes the user's emotional response to the content of the book and prioritizes learning content that is likely to interest the user.

2. The system of claim 1.

8. The question generator To avoid questions that cause stress to the user and to present questions that the user can answer in a relaxed manner 2. The system of claim 1.

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