system

Abstract

The system according to this embodiment aims to efficiently understand and remember the contents of a document. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, an evaluation unit, a provision unit, and a management unit. The analysis unit analyzes a document. The generation unit generates a quiz based on the content analyzed by the analysis unit. The evaluation unit evaluates the user's level of understanding based on the quiz generated by the generation unit. The provision unit provides a learning plan based on the level of understanding evaluated by the evaluation unit. The management unit manages the learning progress based on the learning plan provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] [[ID=XX]] [[ID=XX]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to completely understand and memorize the content of a document, and the learning efficiency is low.

[0005] The system according to the embodiment aims to efficiently understand and memorize the content of a document.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, an evaluation unit, a provision unit, and a management unit. The analysis unit analyzes a document. The generation unit generates a quiz based on the content analyzed by the analysis unit. The evaluation unit evaluates the user's level of understanding based on the quiz generated by the generation unit. The provision unit provides a learning plan based on the level of understanding evaluated by the evaluation unit. The management unit manages the learning progress based on the learning plan provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently understand and remember the contents of a document. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system in which AI reads all of a document and assists the user in understanding and memorizing its contents. This learning support system analyzes the document and extracts important points and keywords. Next, the learning support system automatically generates quizzes to check the user's level of understanding and adjusts the difficulty level according to the user's ability. For example, the learning support system analyzes the user's quiz answers, evaluates the level of understanding based on the correct answer rate and answer time, and provides feedback. The learning support system also learns the user's reading level and answer patterns and provides individually optimized learning plans and additional quizzes. Furthermore, the learning support system monitors learning progress, analyzes the level of understanding and memory retention over time, and provides visualized data. For example, the learning support system provides real-time feedback and recommends areas for improvement and additional learning resources. The learning support system is provided as an application that can be used on smartphones and personal computers, allowing learning anywhere. As a result, the learning support system can efficiently support the user's document comprehension and promote memory retention.

[0029] The learning support system according to this embodiment comprises an analysis unit, a generation unit, an evaluation unit, a provision unit, and a management unit. The analysis unit analyzes a document. The analysis unit analyzes the content of the document using, for example, natural language processing technology. The analysis unit can extract important points and keywords from the document. For example, the analysis unit extracts frequently occurring words and keywords in the document and identifies important information. The analysis unit can also analyze the structure of the document and understand the relationships between headings and paragraphs. The generation unit generates quizzes based on the content analyzed by the analysis unit. The generation unit automatically generates, for example, multiple-choice or written quizzes. The generation unit can adjust the difficulty level of the quizzes according to the user's ability. For example, the generation unit generates quizzes of appropriate difficulty based on the user's past quiz answer history. The generation unit can also adjust the level of detail of the quizzes based on the importance of the document. The evaluation unit evaluates the user's understanding based on the quizzes generated by the generation unit. The evaluation unit analyzes the user's quiz answers and evaluates the level of understanding based on the correct answer rate and answer time. The evaluation unit can analyze user response patterns to improve the accuracy of evaluations. For example, the evaluation unit analyzes the consistency of user responses and the types of incorrect answers to assess comprehension. The evaluation unit can also perform evaluations while considering user attribute information. The delivery unit provides learning plans based on the level of comprehension assessed by the evaluation unit. For example, the delivery unit learns the user's reading comprehension level and response patterns to provide individually optimized learning plans and additional quizzes. The delivery unit can select the optimal learning plan by referring to the user's past learning history. For example, the delivery unit suggests effective learning methods based on the user's past test results and study time. The delivery unit can also customize learning plans based on the user's current living situation. The management unit manages learning progress based on the learning plans provided by the delivery unit. For example, the management unit monitors learning progress and analyzes comprehension and memory retention levels over time. The management unit can visualize learning progress and provide feedback to the user. For example, the management unit displays learning progress as graphs and charts, providing it in a visually easy-to-understand format.Furthermore, the management department can provide real-time feedback and recommend areas for improvement and additional learning resources. This allows the learning support system according to the embodiment to efficiently assist users in understanding documents and promote memory retention.

[0030] The analysis unit analyzes the document. For example, it analyzes the content of the document using natural language processing techniques. Specifically, the analysis unit calculates the frequency of words and phrases within the document and extracts important points and keywords. This involves techniques such as morphological analysis, part-of-speech tagging, and dependency analysis. For instance, morphological analysis is used to segment words within the document, and part-of-speech tagging is performed to identify important words such as nouns and verbs. Furthermore, dependency analysis is used to analyze the document's structure and understand the relationships between headings and paragraphs. This allows for an understanding of the document's overall logical structure and information flow. The analysis unit can also generate a topic model of the document and identify the main topics within it. For example, using topic models such as LDA (Latent Dirichlet Allocation), it analyzes the topic distribution within the document and extracts keywords related to each topic. This allows for a deeper understanding of the document's content and efficient extraction of important information. Additionally, the analysis unit can perform sentiment analysis of the document to grasp its emotional tone and intent. For example, it can identify sentences with positive or negative emotions and evaluate the overall emotional tendency of the document. This allows us to understand not only the content of the document, but also the intentions and emotions behind it.

[0031] The generation unit generates quizzes based on the content analyzed by the analysis unit. The generation unit automatically generates multiple-choice and open-ended quizzes, for example. Specifically, the generation unit creates quiz questions based on keywords and key points provided by the analysis unit. For example, in the case of a multiple-choice quiz, it uses a sentence containing key keywords as the question and generates a correct answer option and several incorrect answer options. Incorrect answer options are often generated based on other relevant keywords and phrases within the document. In the case of an open-ended quiz, it creates questions that ask the user to summarize the document's content or to explain specific keywords. The generation unit can adjust the difficulty of the quiz according to the user's ability. For example, it analyzes the user's past quiz answer history, including correct answer rate and response time, to generate quizzes of appropriate difficulty. The generation unit can also adjust the level of detail in the quiz based on the importance of the document. For example, it creates detailed questions for quizzes on key points and simpler questions for quizzes on supplementary information. Furthermore, the generation unit can enhance the user's learning effect by randomly changing the quiz format and question order. This allows the generation unit to effectively evaluate the user's level of understanding and support their learning progress.

[0032] The evaluation unit assesses the user's understanding based on the quizzes generated by the generation unit. For example, the evaluation unit analyzes the user's quiz answers and evaluates understanding based on the correct answer rate and response time. Specifically, the evaluation unit collects user response data and calculates the correct answer rate. The correct answer rate indicates the percentage of questions the user answered correctly and serves as an indicator of understanding. Response time is also an important evaluation criterion; users who can answer accurately in a short time are judged to have a high level of understanding. The evaluation unit can analyze the user's response patterns to improve the accuracy of the evaluation. For example, if a user consistently answers incorrectly to a particular type of question, they are judged to have low understanding in that area. Furthermore, by analyzing the consistency of the user's answers and the types of incorrect answers, a more detailed evaluation of understanding becomes possible. In addition, the evaluation unit can also perform evaluations considering the user's attribute information. For example, it can set individually optimized evaluation criteria based on information such as the user's age, learning history, and interests. This allows the evaluation unit to perform highly accurate evaluations tailored to the individual characteristics of the user. Based on these evaluation results, the evaluation unit monitors the user's learning progress and changes in understanding and provides appropriate feedback. This allows users to understand their own learning progress and proceed with effective learning.

[0033] The service provider provides learning plans based on the level of understanding assessed by the evaluation team. For example, the service provider learns the user's reading comprehension level and answer patterns, and provides individually optimized learning plans and additional quizzes. Specifically, the service provider selects the optimal learning plan based on the user's past learning history and evaluation results. For example, if a user has a low level of understanding in a particular area, it provides additional learning resources and quizzes related to that area. The service provider can also customize learning plans based on the user's current lifestyle. For example, if a user is busy, it provides a plan that allows for effective learning in a short amount of time, and conversely, if a user has more time, it provides a more detailed learning plan. The service provider can also adjust the format and content of the learning plan according to the user's learning style and preferences. For example, it provides learning resources that make extensive use of diagrams and graphs for users who prefer visual learning, and learning resources that use audio and video for users who prefer auditory learning. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the learning plan. This allows the service provider to provide users with the optimal learning plan and support effective learning.

[0034] The Management Department manages learning progress based on the learning plans provided by the Delivery Department. For example, the Management Department monitors learning progress and analyzes comprehension and memory retention levels over time. Specifically, the Management Department collects user learning data and visualizes learning progress. For instance, it displays learning progress as graphs and charts, providing it in a visually easy-to-understand format. This allows users to grasp their learning status at a glance. The Management Department can also provide real-time feedback and recommend areas for improvement and additional learning resources. For example, if a user has a low level of understanding in a particular area, it can provide additional learning resources related to that area to support improvement. Furthermore, the Management Department can develop long-term learning plans based on the user's learning history and evaluation results. For example, it can provide a plan for users to progress through learning step-by-step towards their target exams or qualifications. This allows users to learn effectively and achieve their goals. The Management Department centrally manages user learning data and can collaborate with other systems and departments as needed. For example, it can store learning data on a cloud server, making it accessible to the Analysis Department and Delivery Department. This allows the management department to efficiently and effectively manage learning progress and improve the overall performance of the system.

[0035] The evaluation unit can analyze users' quiz responses and assess their level of understanding based on the correct answer rate and response time. For example, the evaluation unit can analyze users' quiz responses and calculate the correct answer rate. The evaluation unit can calculate the correct answer rate based on the ratio of correct answers to the total number of responses. The evaluation unit can also measure the time taken to answer each quiz and assess the level of understanding based on the response time. For example, the evaluation unit can calculate the average response time of users and assess their level of understanding. This allows the evaluation unit to accurately assess the user's level of understanding and provide appropriate feedback.

[0036] The service provider can learn the user's reading comprehension level and response patterns, and provide individually optimized learning plans and additional quizzes. For example, the service provider can assess the user's reading comprehension level and provide an appropriate learning plan. The service provider can assess the reading comprehension level based on the difficulty of the text and the results of comprehension tests. In addition, the service provider can analyze the user's response patterns and optimize the learning plan based on response trends and types of incorrect answers. For example, the service provider can analyze the user's response patterns and provide an individually optimized learning plan. This allows the service provider to provide the user with the most suitable learning plan and enhance learning effectiveness.

[0037] The management department can monitor learning progress, analyze comprehension and memory retention levels over time, and provide visualized data. For example, the management department can monitor learning progress and evaluate the user's comprehension level. The management department can evaluate comprehension based on the correct answer rate and response time. The management department can also evaluate the user's memory retention level and analyze it based on the results of retests and evaluations of long-term memory. For example, the management department can analyze the results of the user's retests and evaluate memory retention. This allows the management department to visualize learning progress and grasp the user's comprehension and memory retention level.

[0038] The management department can provide real-time feedback and recommend areas for improvement and additional learning resources. For example, the management department can improve user learning effectiveness by providing real-time feedback. The management department can provide real-time feedback based on the timing and delay of feedback delivery. Furthermore, the management department can adjust the content and method of feedback delivery, including pointing out correctness or incorrectness and suggesting areas for improvement. For example, the management department can point out correctness or incorrectness in user responses and suggest areas for improvement. This allows the management department to provide real-time feedback and improve learning effectiveness.

[0039] The management unit can be provided as an application usable on smartphones and personal computers, enabling learning anywhere. The management unit can be provided, for example, as a mobile app or web app. The management unit can provide an environment where users can learn regardless of location. For example, the management unit can provide learning plans using smartphones and personal computers, allowing users to study at home or on the go. This allows the management unit to provide an environment where users can learn regardless of location.

[0040] The analysis unit can prioritize analyzing the most important parts of a document based on its content. For example, it can prioritize analyzing headings and bolded sections to extract important information. It can also prioritize analyzing the beginning and conclusion sections of a document to grasp the overall main points. Furthermore, the analysis unit can analyze keywords and frequently occurring words within the document to extract important themes and topics. For example, it can evaluate importance based on keyword frequency and document structure, prioritizing the analysis of important information. As a result, the analysis unit can prioritize the analysis of important information and provide it efficiently.

[0041] The analysis unit can apply different analysis algorithms depending on the genre and theme of the document. For example, in the case of a scientific paper, the analysis unit can apply an algorithm that analyzes technical terms and mathematical formulas. In the case of a novel, the analysis unit can apply an algorithm that analyzes characters and the flow of the story. Furthermore, in the case of a business document, the analysis unit can apply an algorithm that analyzes data and graphs. For example, the analysis unit can select an appropriate analysis algorithm according to the genre and theme of the document to improve the accuracy of the analysis. In this way, the analysis unit can perform appropriate analysis according to the genre and theme of the document.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's past learning history when analyzing documents. For example, the analysis unit prioritizes analyzing relevant information based on what the user has learned in the past. The analysis unit can use parts of the user's past learning history that the user understands well as a reference for analysis. Furthermore, the analysis unit can also improve the accuracy of its analysis by analyzing the user's past learning history. For example, the analysis unit can improve the accuracy of its analysis based on the user's past test results and learning time. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past learning history.

[0043] The analysis unit can determine the priority of analysis based on the user's areas of interest when analyzing a document. For example, the analysis unit will prioritize analyzing parts related to themes that the user is interested in. The analysis unit can extract important information based on the user's areas of interest. Furthermore, the analysis unit can also determine the priority of analysis by considering the user's areas of interest. For example, the analysis unit can identify the user's areas of interest based on survey results or past learning content and determine the priority of analysis. This allows the analysis unit to determine the priority of analysis based on the user's areas of interest and provide information efficiently.

[0044] The generation unit can adjust the level of detail in quizzes based on the importance of the document during quiz generation. For example, it can create detailed quizzes on important points and simplify quizzes on less important parts. It can also create quizzes that cover the main points of the entire document. For example, it can evaluate importance based on keyword frequency and document structure and adjust the level of detail in the quiz. This allows the generation unit to adjust the level of detail in quizzes based on the importance of the document and provide efficient learning.

[0045] The generation unit can apply different quiz generation algorithms depending on the document category when generating quizzes. For example, in the case of a scientific paper, the generation unit can generate quizzes about technical terms and mathematical formulas. In the case of a novel, the generation unit can generate quizzes about characters and storylines. Furthermore, in the case of a business document, the generation unit can generate quizzes about data and graphs. For example, the generation unit can select an appropriate quiz generation algorithm according to the document category to improve the accuracy of the quizzes. As a result, the generation unit can generate appropriate quizzes according to the document category and provide efficient learning.

[0046] The generation unit can improve the accuracy of quizzes by referring to the user's past quiz answer history when generating quizzes. For example, the generation unit can re-present questions that the user has answered incorrectly in the past. The generation unit can also present quizzes of appropriate difficulty based on the user's past correct answer rate. Furthermore, the generation unit can improve the accuracy of quizzes by analyzing the user's past answer patterns. For example, the generation unit can improve the accuracy of quizzes based on the user's past quiz answer history. In this way, the generation unit can improve the accuracy of quizzes by referring to past quiz answer history.

[0047] The generation unit can adjust the order of quizzes based on the user's learning progress when generating quizzes. For example, the generation unit can adjust the order from basic questions to applied questions according to the user's learning progress. The generation unit can postpone more difficult questions based on the user's level of understanding. Furthermore, the generation unit can optimize the order of quizzes considering the user's learning progress. For example, the generation unit can adjust the order of quizzes based on the user's learning progress. In this way, the generation unit can adjust the order of quizzes based on learning progress and provide learning efficiently.

[0048] The evaluation unit can improve the accuracy of its evaluations by analyzing the user's response patterns during quiz responses. For example, the evaluation unit analyzes the user's response patterns and evaluates based on the correct answer rate. The evaluation unit can also improve the accuracy of its evaluations by considering the user's response time. Furthermore, the evaluation unit can improve the accuracy of its evaluations by analyzing the consistency of the user's responses. For example, the evaluation unit can improve the accuracy of its evaluations based on the user's response trends and types of incorrect answers. In this way, the evaluation unit can improve the accuracy of its evaluations by analyzing the user's response patterns.

[0049] The evaluation unit can perform evaluations while considering the user's attribute information when they answer quizzes. For example, the evaluation unit can adjust evaluation criteria by considering the user's age and gender. The evaluation unit can set evaluation criteria by considering the user's educational background and occupation. Furthermore, the evaluation unit can improve the accuracy of evaluations by considering the user's interests and concerns. For example, the evaluation unit can adjust evaluation criteria based on the user's attribute information to provide an appropriate evaluation. In this way, the evaluation unit can provide an appropriate evaluation by considering the user's attribute information.

[0050] The evaluation unit can perform quiz responses while considering the geographical distribution of users. For example, the evaluation unit can adjust evaluation criteria based on the user's place of residence. The evaluation unit can also set evaluation criteria considering the educational level of the user's region. Furthermore, the evaluation unit can improve the accuracy of evaluations by considering the culture and customs of the user's region. For example, the evaluation unit can adjust evaluation criteria based on the geographical distribution of users to provide appropriate evaluations. In this way, the evaluation unit can provide appropriate evaluations by considering the geographical distribution of users.

[0051] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature when users answer quizzes. For example, the evaluation unit can evaluate the user's answers by comparing them with relevant literature. The evaluation unit can set evaluation criteria based on the information in the relevant literature. Furthermore, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature. For example, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature such as academic papers and technical reports. In this way, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature.

[0052] The service provider can select the optimal learning plan by referring to the user's past learning history when providing a learning plan. For example, the service provider can propose the optimal learning plan based on the user's past learning history. The service provider can select effective learning methods from the user's past learning history. Furthermore, the service provider can analyze the user's past learning history and provide the optimal learning plan. For example, the service provider can select the optimal learning plan based on the user's past test results and study time. In this way, the service provider can provide the optimal learning plan by referring to the user's past learning history.

[0053] The service provider can customize learning plans based on the user's current lifestyle when providing them. For example, the service provider can customize the learning plan to match the user's daily rhythm. The service provider can adjust the learning plan considering the user's work and family circumstances. Furthermore, the service provider can optimize the learning plan considering the user's health condition. For example, the service provider can customize the learning plan based on the user's work schedule and family circumstances. This allows the service provider to customize the plan based on the user's current lifestyle and provide appropriate learning.

[0054] The service provider can select the optimal learning plan by considering the user's geographical location when providing a learning plan. For example, the service provider can propose the optimal learning plan based on the user's place of residence. The service provider can adjust the learning plan by considering the educational resources in the user's area. Furthermore, the service provider can optimize the learning plan by considering the culture and customs of the user's area. For example, the service provider can select the optimal learning plan based on the user's geographical location. In this way, the service provider can provide the optimal learning plan by considering geographical location.

[0055] The service provider can analyze the user's social media activity and propose a learning plan when providing one. For example, the service provider can propose a learning plan based on the user's interests and passions on social media. The service provider can adjust the learning plan considering the user's time spent on social media. Furthermore, the service provider can analyze the content of the user's social media interactions and provide the optimal learning plan. For example, the service provider can propose a learning plan based on the user's social media activity. In this way, the service provider can provide the optimal learning plan by analyzing social media activity.

[0056] The management department can select the optimal management method when managing learning progress by referring to the user's past learning history. For example, the management department can propose the optimal progress management method based on the user's past learning history. The management department can select an effective progress management method from the user's past learning history. Furthermore, the management department can analyze the user's past learning history and provide the optimal progress management method. For example, the management department can select the optimal progress management method based on the user's past test results and learning time. In this way, the management department can provide the optimal progress management method by referring to the past learning history.

[0057] The management department can customize the management methods based on the user's current lifestyle when managing learning progress. For example, the management department can customize progress management to match the user's daily rhythm. The management department can adjust progress management considering the user's work and family circumstances. Furthermore, the management department can optimize progress management considering the user's health condition. For example, the management department can customize progress management based on the user's work schedule and family circumstances. In this way, the management department can customize the management methods based on the user's current lifestyle and provide appropriate management.

[0058] The management department can select the optimal management method when managing learning progress, taking into account the user's geographical location. For example, the management department can propose the optimal progress management method based on the user's place of residence. The management department can adjust progress management considering the educational resources in the user's region. Furthermore, the management department can optimize progress management by considering the culture and customs of the user's region. For example, the management department can select the optimal progress management method based on the user's geographical location. In this way, the management department can provide the optimal progress management method by taking geographical location into consideration.

[0059] The management department can analyze users' social media activity and propose management methods when managing learning progress. For example, the management department can propose progress management methods based on users' interests and preferences on social media. The management department can adjust progress management considering the time users spend on social media. Furthermore, the management department can analyze the content of users' social media interactions and provide the optimal progress management method. For example, the management department can propose progress management methods based on users' social media activity. In this way, the management department can provide the optimal progress management method by analyzing social media activity.

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

[0061] The generation unit can set learning goals for the user and adjust the quiz content based on those goals. For example, a user aiming to pass a specific exam can be given many questions related to that exam. A user aiming to acquire a specific skill can be given practical questions related to that skill. Furthermore, users with long-term learning goals can be provided with quizzes that gradually increase in difficulty. In this way, the generation unit can adjust the quiz content according to the user's learning goals and provide effective learning support.

[0062] The evaluation unit can refer to the user's learning history and adjust the evaluation criteria based on past learning content. For example, it can relax the evaluation criteria for questions related to areas the user previously struggled with. Conversely, it can tighten the evaluation criteria for questions related to areas the user excels at. It can also assess the user's progress based on their past learning history and provide feedback that reflects that progress. In this way, the evaluation unit can adjust the evaluation criteria based on the user's learning history and provide an appropriate evaluation.

[0063] The service provider can adjust learning plans to suit the user's learning environment. For example, for users who have difficulty studying in a quiet environment, it can provide a learning plan that allows for short, focused study sessions. Conversely, for users who are in an environment where they can study for extended periods, it can provide a detailed learning plan. Furthermore, for users who study while on the go, it can provide a learning plan that can be used on a mobile device. In this way, the service provider can adjust learning plans according to the user's learning environment and provide effective learning support.

[0064] The management department can compare a user's learning progress with other users and provide relative evaluations. For example, if a user is behind other users with the same learning goals, additional learning resources can be provided. Conversely, if a user is progressing quickly, a more challenging learning plan can be offered. Relative evaluations can also be provided by comparing users in the same region or age group. This allows the management department to compare a user's learning progress with other users and provide relative evaluations.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis unit analyzes the document. The analysis unit uses natural language processing technology to analyze the content of the document and extract important points and keywords. For example, it extracts frequently occurring words and keywords within the document to identify important information. It can also analyze the structure of the document and understand the relationships between headings and paragraphs. Step 2: The generation unit generates quizzes based on the analysis performed by the analysis unit. The generation unit automatically generates multiple-choice and open-ended quizzes and adjusts the difficulty level according to the user's ability. For example, it can generate quizzes of appropriate difficulty based on the user's past quiz answer history. It can also adjust the level of detail of the quiz based on the importance of the document. Step 3: The evaluation unit assesses the user's understanding based on the quiz generated by the generation unit. The evaluation unit analyzes the user's quiz answers and evaluates the level of understanding based on the correct answer rate and response time. The evaluation unit analyzes the user's response patterns to improve the accuracy of the evaluation. For example, it analyzes the consistency of the user's answers and the types of incorrect answers to evaluate the level of understanding. It is also possible to perform the evaluation while considering the user's attribute information. Step 4: The delivery unit provides a learning plan based on the level of understanding assessed by the evaluation unit. The delivery unit learns the user's reading comprehension level and answer patterns, and provides an individually optimized learning plan and additional quizzes. It selects the optimal learning plan by referring to the user's past learning history. For example, it suggests effective learning methods based on the user's past test results and study time. It can also customize the learning plan based on the user's current lifestyle. Step 5: The management department manages learning progress based on the learning plan provided by the delivery department. The management department monitors learning progress and analyzes comprehension and memory retention levels for each period. They visualize learning progress and provide feedback to the user. For example, they display learning progress as graphs or charts, providing it in a visually easy-to-understand format. They can also provide real-time feedback and recommend areas for improvement or additional learning resources.

[0067] (Example of form 2) The learning support system according to an embodiment of the present invention is a system in which AI reads all of a document and assists the user in understanding and memorizing its contents. This learning support system analyzes the document and extracts important points and keywords. Next, the learning support system automatically generates quizzes to check the user's level of understanding and adjusts the difficulty level according to the user's ability. For example, the learning support system analyzes the user's quiz answers, evaluates the level of understanding based on the correct answer rate and answer time, and provides feedback. The learning support system also learns the user's reading level and answer patterns and provides individually optimized learning plans and additional quizzes. Furthermore, the learning support system monitors learning progress, analyzes the level of understanding and memory retention over time, and provides visualized data. For example, the learning support system provides real-time feedback and recommends areas for improvement and additional learning resources. The learning support system is provided as an application that can be used on smartphones and personal computers, allowing learning anywhere. As a result, the learning support system can efficiently support the user's document comprehension and promote memory retention.

[0068] The learning support system according to this embodiment comprises an analysis unit, a generation unit, an evaluation unit, a provision unit, and a management unit. The analysis unit analyzes a document. The analysis unit analyzes the content of the document using, for example, natural language processing technology. The analysis unit can extract important points and keywords from the document. For example, the analysis unit extracts frequently occurring words and keywords in the document and identifies important information. The analysis unit can also analyze the structure of the document and understand the relationships between headings and paragraphs. The generation unit generates quizzes based on the content analyzed by the analysis unit. The generation unit automatically generates, for example, multiple-choice or written quizzes. The generation unit can adjust the difficulty level of the quizzes according to the user's ability. For example, the generation unit generates quizzes of appropriate difficulty based on the user's past quiz answer history. The generation unit can also adjust the level of detail of the quizzes based on the importance of the document. The evaluation unit evaluates the user's understanding based on the quizzes generated by the generation unit. The evaluation unit analyzes the user's quiz answers and evaluates the level of understanding based on the correct answer rate and answer time. The evaluation unit can analyze user response patterns to improve the accuracy of evaluations. For example, the evaluation unit analyzes the consistency of user responses and the types of incorrect answers to assess comprehension. The evaluation unit can also perform evaluations while considering user attribute information. The delivery unit provides learning plans based on the level of comprehension assessed by the evaluation unit. For example, the delivery unit learns the user's reading comprehension level and response patterns to provide individually optimized learning plans and additional quizzes. The delivery unit can select the optimal learning plan by referring to the user's past learning history. For example, the delivery unit suggests effective learning methods based on the user's past test results and study time. The delivery unit can also customize learning plans based on the user's current living situation. The management unit manages learning progress based on the learning plans provided by the delivery unit. For example, the management unit monitors learning progress and analyzes comprehension and memory retention levels over time. The management unit can visualize learning progress and provide feedback to the user. For example, the management unit displays learning progress as graphs and charts, providing it in a visually easy-to-understand format.Furthermore, the management department can provide real-time feedback and recommend areas for improvement and additional learning resources. This allows the learning support system according to the embodiment to efficiently assist users in understanding documents and promote memory retention.

[0069] The analysis unit analyzes the document. For example, it analyzes the content of the document using natural language processing techniques. Specifically, the analysis unit calculates the frequency of words and phrases within the document and extracts important points and keywords. This involves techniques such as morphological analysis, part-of-speech tagging, and dependency analysis. For instance, morphological analysis is used to segment words within the document, and part-of-speech tagging is performed to identify important words such as nouns and verbs. Furthermore, dependency analysis is used to analyze the document's structure and understand the relationships between headings and paragraphs. This allows for an understanding of the document's overall logical structure and information flow. The analysis unit can also generate a topic model of the document and identify the main topics within it. For example, using topic models such as LDA (Latent Dirichlet Allocation), it analyzes the topic distribution within the document and extracts keywords related to each topic. This allows for a deeper understanding of the document's content and efficient extraction of important information. Additionally, the analysis unit can perform sentiment analysis of the document to grasp its emotional tone and intent. For example, it can identify sentences with positive or negative emotions and evaluate the overall emotional tendency of the document. This allows us to understand not only the content of the document, but also the intentions and emotions behind it.

[0070] The generation unit generates quizzes based on the content analyzed by the analysis unit. The generation unit automatically generates multiple-choice and open-ended quizzes, for example. Specifically, the generation unit creates quiz questions based on keywords and key points provided by the analysis unit. For example, in the case of a multiple-choice quiz, it uses a sentence containing key keywords as the question and generates a correct answer option and several incorrect answer options. Incorrect answer options are often generated based on other relevant keywords and phrases within the document. In the case of an open-ended quiz, it creates questions that ask the user to summarize the document's content or to explain specific keywords. The generation unit can adjust the difficulty of the quiz according to the user's ability. For example, it analyzes the user's past quiz answer history, including correct answer rate and response time, to generate quizzes of appropriate difficulty. The generation unit can also adjust the level of detail in the quiz based on the importance of the document. For example, it creates detailed questions for quizzes on key points and simpler questions for quizzes on supplementary information. Furthermore, the generation unit can enhance the user's learning effect by randomly changing the quiz format and question order. This allows the generation unit to effectively evaluate the user's level of understanding and support their learning progress.

[0071] The evaluation unit assesses the user's understanding based on the quizzes generated by the generation unit. For example, the evaluation unit analyzes the user's quiz answers and evaluates understanding based on the correct answer rate and response time. Specifically, the evaluation unit collects user response data and calculates the correct answer rate. The correct answer rate indicates the percentage of questions the user answered correctly and serves as an indicator of understanding. Response time is also an important evaluation criterion; users who can answer accurately in a short time are judged to have a high level of understanding. The evaluation unit can analyze the user's response patterns to improve the accuracy of the evaluation. For example, if a user consistently answers incorrectly to a particular type of question, they are judged to have low understanding in that area. Furthermore, by analyzing the consistency of the user's answers and the types of incorrect answers, a more detailed evaluation of understanding becomes possible. In addition, the evaluation unit can also perform evaluations considering the user's attribute information. For example, it can set individually optimized evaluation criteria based on information such as the user's age, learning history, and interests. This allows the evaluation unit to perform highly accurate evaluations tailored to the individual characteristics of the user. Based on these evaluation results, the evaluation unit monitors the user's learning progress and changes in understanding and provides appropriate feedback. This allows users to understand their own learning progress and proceed with effective learning.

[0072] The service provider provides learning plans based on the level of understanding assessed by the evaluation team. For example, the service provider learns the user's reading comprehension level and answer patterns, and provides individually optimized learning plans and additional quizzes. Specifically, the service provider selects the optimal learning plan based on the user's past learning history and evaluation results. For example, if a user has a low level of understanding in a particular area, it provides additional learning resources and quizzes related to that area. The service provider can also customize learning plans based on the user's current lifestyle. For example, if a user is busy, it provides a plan that allows for effective learning in a short amount of time, and conversely, if a user has more time, it provides a more detailed learning plan. The service provider can also adjust the format and content of the learning plan according to the user's learning style and preferences. For example, it provides learning resources that make extensive use of diagrams and graphs for users who prefer visual learning, and learning resources that use audio and video for users who prefer auditory learning. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the learning plan. This allows the service provider to provide users with the optimal learning plan and support effective learning.

[0073] The Management Department manages learning progress based on the learning plans provided by the Delivery Department. For example, the Management Department monitors learning progress and analyzes comprehension and memory retention levels over time. Specifically, the Management Department collects user learning data and visualizes learning progress. For instance, it displays learning progress as graphs and charts, providing it in a visually easy-to-understand format. This allows users to grasp their learning status at a glance. The Management Department can also provide real-time feedback and recommend areas for improvement and additional learning resources. For example, if a user has a low level of understanding in a particular area, it can provide additional learning resources related to that area to support improvement. Furthermore, the Management Department can develop long-term learning plans based on the user's learning history and evaluation results. For example, it can provide a plan for users to progress through learning step-by-step towards their target exams or qualifications. This allows users to learn effectively and achieve their goals. The Management Department centrally manages user learning data and can collaborate with other systems and departments as needed. For example, it can store learning data on a cloud server, making it accessible to the Analysis Department and Delivery Department. This allows the management department to efficiently and effectively manage learning progress and improve the overall performance of the system.

[0074] The evaluation unit can analyze users' quiz responses and assess their level of understanding based on the correct answer rate and response time. For example, the evaluation unit can analyze users' quiz responses and calculate the correct answer rate. The evaluation unit can calculate the correct answer rate based on the ratio of correct answers to the total number of responses. The evaluation unit can also measure the time taken to answer each quiz and assess the level of understanding based on the response time. For example, the evaluation unit can calculate the average response time of users and assess their level of understanding. This allows the evaluation unit to accurately assess the user's level of understanding and provide appropriate feedback.

[0075] The service provider can learn the user's reading comprehension level and response patterns, and provide individually optimized learning plans and additional quizzes. For example, the service provider can assess the user's reading comprehension level and provide an appropriate learning plan. The service provider can assess the reading comprehension level based on the difficulty of the text and the results of comprehension tests. In addition, the service provider can analyze the user's response patterns and optimize the learning plan based on response trends and types of incorrect answers. For example, the service provider can analyze the user's response patterns and provide an individually optimized learning plan. This allows the service provider to provide the user with the most suitable learning plan and enhance learning effectiveness.

[0076] The management department can monitor learning progress, analyze comprehension and memory retention levels over time, and provide visualized data. For example, the management department can monitor learning progress and evaluate the user's comprehension level. The management department can evaluate comprehension based on the correct answer rate and response time. The management department can also evaluate the user's memory retention level and analyze it based on the results of retests and evaluations of long-term memory. For example, the management department can analyze the results of the user's retests and evaluate memory retention. This allows the management department to visualize learning progress and grasp the user's comprehension and memory retention level.

[0077] The management department can provide real-time feedback and recommend areas for improvement and additional learning resources. For example, the management department can improve user learning effectiveness by providing real-time feedback. The management department can provide real-time feedback based on the timing and delay of feedback delivery. Furthermore, the management department can adjust the content and method of feedback delivery, including pointing out correctness or incorrectness and suggesting areas for improvement. For example, the management department can point out correctness or incorrectness in user responses and suggest areas for improvement. This allows the management department to provide real-time feedback and improve learning effectiveness.

[0078] The management unit can be provided as an application usable on smartphones and personal computers, enabling learning anywhere. The management unit can be provided, for example, as a mobile app or web app. The management unit can provide an environment where users can learn regardless of location. For example, the management unit can provide learning plans using smartphones and personal computers, allowing users to study at home or on the go. This allows the management unit to provide an environment where users can learn regardless of location.

[0079] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can shallow the depth of the analysis and extract only the important points. If the user is relaxed, the analysis unit can perform a detailed analysis and extract even fine details. Furthermore, if the user is focused, the analysis unit can deepen the depth of the analysis and include relevant background information in the analysis. For example, the analysis unit can analyze the user's facial expressions and voice to estimate their emotions. This allows the analysis unit to adjust the depth of the analysis according to the user's emotions and provide appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The analysis unit can prioritize analyzing the most important parts of a document based on its content. For example, it can prioritize analyzing headings and bolded sections to extract important information. It can also prioritize analyzing the beginning and conclusion sections of a document to grasp the overall main points. Furthermore, the analysis unit can analyze keywords and frequently occurring words within the document to extract important themes and topics. For example, it can evaluate importance based on keyword frequency and document structure, prioritizing the analysis of important information. As a result, the analysis unit can prioritize the analysis of important information and provide it efficiently.

[0081] The analysis unit can apply different analysis algorithms depending on the genre and theme of the document. For example, in the case of a scientific paper, the analysis unit can apply an algorithm that analyzes technical terms and mathematical formulas. In the case of a novel, the analysis unit can apply an algorithm that analyzes characters and the flow of the story. Furthermore, in the case of a business document, the analysis unit can apply an algorithm that analyzes data and graphs. For example, the analysis unit can select an appropriate analysis algorithm according to the genre and theme of the document to improve the accuracy of the analysis. In this way, the analysis unit can perform appropriate analysis according to the genre and theme of the document.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit analyzes the user's facial expressions and voice to estimate their emotions. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, thereby improving visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can improve the accuracy of its analysis by referring to the user's past learning history when analyzing documents. For example, the analysis unit prioritizes analyzing relevant information based on what the user has learned in the past. The analysis unit can use parts of the user's past learning history that the user understands well as a reference for analysis. Furthermore, the analysis unit can also improve the accuracy of its analysis by analyzing the user's past learning history. For example, the analysis unit can improve the accuracy of its analysis based on the user's past test results and learning time. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past learning history.

[0084] The analysis unit can determine the priority of analysis based on the user's areas of interest when analyzing a document. For example, the analysis unit will prioritize analyzing parts related to themes that the user is interested in. The analysis unit can extract important information based on the user's areas of interest. Furthermore, the analysis unit can also determine the priority of analysis by considering the user's areas of interest. For example, the analysis unit can identify the user's areas of interest based on survey results or past learning content and determine the priority of analysis. This allows the analysis unit to determine the priority of analysis based on the user's areas of interest and provide information efficiently.

[0085] The generation unit can estimate the user's emotions and adjust the difficulty of the quiz based on the estimated emotions. For example, if the user is stressed, the generation unit can present an easy quiz. If the user is relaxed, the generation unit can present a more difficult quiz. Furthermore, if the user is focused, the generation unit can present a quiz of appropriate difficulty. For example, the generation unit can analyze the user's facial expressions and voice to estimate their emotions. This allows the generation unit to adjust the quiz difficulty according to the user's emotions and provide appropriate learning. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The generation unit can adjust the level of detail in quizzes based on the importance of the document during quiz generation. For example, it can create detailed quizzes on important points and simplify quizzes on less important parts. It can also create quizzes that cover the main points of the entire document. For example, it can evaluate importance based on keyword frequency and document structure and adjust the level of detail in the quiz. This allows the generation unit to adjust the level of detail in quizzes based on the importance of the document and provide efficient learning.

[0087] The generation unit can apply different quiz generation algorithms depending on the document category when generating quizzes. For example, in the case of a scientific paper, the generation unit can generate quizzes about technical terms and mathematical formulas. In the case of a novel, the generation unit can generate quizzes about characters and storylines. Furthermore, in the case of a business document, the generation unit can generate quizzes about data and graphs. For example, the generation unit can select an appropriate quiz generation algorithm according to the document category to improve the accuracy of the quizzes. As a result, the generation unit can generate appropriate quizzes according to the document category and provide efficient learning.

[0088] The generation unit can estimate the user's emotions and adjust the quiz format based on the estimated emotions. For example, if the user is nervous, the generation unit can present a multiple-choice quiz. If the user is relaxed, it can present a written quiz. Furthermore, if the user is in a hurry, it can present a quiz that can be answered quickly. For example, the generation unit can analyze the user's facial expressions and voice to estimate their emotions. This allows the generation unit to adjust the quiz format according to the user's emotions and provide appropriate learning. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The generation unit can improve the accuracy of quizzes by referring to the user's past quiz answer history when generating quizzes. For example, the generation unit can re-present questions that the user has answered incorrectly in the past. The generation unit can also present quizzes of appropriate difficulty based on the user's past correct answer rate. Furthermore, the generation unit can improve the accuracy of quizzes by analyzing the user's past answer patterns. For example, the generation unit can improve the accuracy of quizzes based on the user's past quiz answer history. In this way, the generation unit can improve the accuracy of quizzes by referring to past quiz answer history.

[0090] The generation unit can adjust the order of quizzes based on the user's learning progress when generating quizzes. For example, the generation unit can adjust the order from basic questions to applied questions according to the user's learning progress. The generation unit can postpone more difficult questions based on the user's level of understanding. Furthermore, the generation unit can optimize the order of quizzes considering the user's learning progress. For example, the generation unit can adjust the order of quizzes based on the user's learning progress. In this way, the generation unit can adjust the order of quizzes based on learning progress and provide learning efficiently.

[0091] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, the evaluation unit may relax the evaluation criteria if the user is stressed. If the user is relaxed, the evaluation unit may apply strict evaluation criteria. The evaluation unit may also set moderate evaluation criteria if the user is focused. For example, the evaluation unit may analyze the user's facial expressions and voice to estimate their emotions. This allows the evaluation unit to adjust the evaluation criteria according to the user's emotions and provide an appropriate evaluation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The evaluation unit can improve the accuracy of its evaluations by analyzing the user's response patterns during quiz responses. For example, the evaluation unit analyzes the user's response patterns and evaluates based on the correct answer rate. The evaluation unit can also improve the accuracy of its evaluations by considering the user's response time. Furthermore, the evaluation unit can improve the accuracy of its evaluations by analyzing the consistency of the user's responses. For example, the evaluation unit can improve the accuracy of its evaluations based on the user's response trends and types of incorrect answers. In this way, the evaluation unit can improve the accuracy of its evaluations by analyzing the user's response patterns.

[0093] The evaluation unit can perform evaluations while considering the user's attribute information when they answer quizzes. For example, the evaluation unit can adjust evaluation criteria by considering the user's age and gender. The evaluation unit can set evaluation criteria by considering the user's educational background and occupation. Furthermore, the evaluation unit can improve the accuracy of evaluations by considering the user's interests and concerns. For example, the evaluation unit can adjust evaluation criteria based on the user's attribute information to provide an appropriate evaluation. In this way, the evaluation unit can provide an appropriate evaluation by considering the user's attribute information.

[0094] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, the evaluation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise display method. For example, the evaluation unit analyzes the user's facial expressions and voice to estimate emotions. This allows the evaluation unit to adjust the display method of the evaluation results according to the user's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The evaluation unit can perform quiz responses while considering the geographical distribution of users. For example, the evaluation unit can adjust evaluation criteria based on the user's place of residence. The evaluation unit can also set evaluation criteria considering the educational level of the user's region. Furthermore, the evaluation unit can improve the accuracy of evaluations by considering the culture and customs of the user's region. For example, the evaluation unit can adjust evaluation criteria based on the geographical distribution of users to provide appropriate evaluations. In this way, the evaluation unit can provide appropriate evaluations by considering the geographical distribution of users.

[0096] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature when users answer quizzes. For example, the evaluation unit can evaluate the user's answers by comparing them with relevant literature. The evaluation unit can set evaluation criteria based on the information in the relevant literature. Furthermore, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature. For example, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature such as academic papers and technical reports. In this way, the evaluation unit can improve the accuracy of its evaluations by referring to relevant literature.

[0097] The system can estimate the user's emotions and adjust the content of the learning plan based on those emotions. For example, if the user is stressed, the system can reduce the content of the learning plan. If the user is relaxed, the system can enrich the content of the learning plan. The system can also optimize the content of the learning plan if the user is focused. For example, the system can analyze the user's facial expressions and voice to estimate their emotions. This allows the system to adjust the content of the learning plan according to the user's emotions and provide appropriate learning. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The service provider can select the optimal learning plan by referring to the user's past learning history when providing a learning plan. For example, the service provider can propose the optimal learning plan based on the user's past learning history. The service provider can select effective learning methods from the user's past learning history. Furthermore, the service provider can analyze the user's past learning history and provide the optimal learning plan. For example, the service provider can select the optimal learning plan based on the user's past test results and study time. In this way, the service provider can provide the optimal learning plan by referring to the user's past learning history.

[0099] The service provider can customize learning plans based on the user's current lifestyle when providing them. For example, the service provider can customize the learning plan to match the user's daily rhythm. The service provider can adjust the learning plan considering the user's work and family circumstances. Furthermore, the service provider can optimize the learning plan considering the user's health condition. For example, the service provider can customize the learning plan based on the user's work schedule and family circumstances. This allows the service provider to customize the plan based on the user's current lifestyle and provide appropriate learning.

[0100] The system can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is stressed, the system will prioritize relaxing content. If the user is relaxed, the system will prioritize more challenging content. Furthermore, if the user is focused, the system will prioritize important content. For instance, the system can analyze the user's facial expressions and voice to estimate their emotions. This allows the system to prioritize learning plans according to the user's emotions and provide appropriate learning. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The service provider can select the optimal learning plan by considering the user's geographical location when providing a learning plan. For example, the service provider can propose the optimal learning plan based on the user's place of residence. The service provider can adjust the learning plan by considering the educational resources in the user's area. Furthermore, the service provider can optimize the learning plan by considering the culture and customs of the user's area. For example, the service provider can select the optimal learning plan based on the user's geographical location. In this way, the service provider can provide the optimal learning plan by considering geographical location.

[0102] The service provider can analyze the user's social media activity and propose a learning plan when providing one. For example, the service provider can propose a learning plan based on the user's interests and passions on social media. The service provider can adjust the learning plan considering the user's time spent on social media. Furthermore, the service provider can analyze the content of the user's social media interactions and provide the optimal learning plan. For example, the service provider can propose a learning plan based on the user's social media activity. In this way, the service provider can provide the optimal learning plan by analyzing social media activity.

[0103] The management unit can estimate the user's emotions and adjust the learning progress management method based on the estimated emotions. For example, the management unit can ease progress management if the user is stressed, perform detailed progress management if the user is relaxed, and perform strict progress management if the user is focused. For example, the management unit can analyze the user's facial expressions and voice to estimate emotions. This allows the management unit to adjust the learning progress management method according to the user's emotions and provide appropriate management. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The management department can select the optimal management method when managing learning progress by referring to the user's past learning history. For example, the management department can propose the optimal progress management method based on the user's past learning history. The management department can select an effective progress management method from the user's past learning history. Furthermore, the management department can analyze the user's past learning history and provide the optimal progress management method. For example, the management department can select the optimal progress management method based on the user's past test results and learning time. In this way, the management department can provide the optimal progress management method by referring to the past learning history.

[0105] The management department can customize the management methods based on the user's current lifestyle when managing learning progress. For example, the management department can customize progress management to match the user's daily rhythm. The management department can adjust progress management considering the user's work and family circumstances. Furthermore, the management department can optimize progress management considering the user's health condition. For example, the management department can customize progress management based on the user's work schedule and family circumstances. In this way, the management department can customize the management methods based on the user's current lifestyle and provide appropriate management.

[0106] The management unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated emotions. For example, if the user is nervous, the management unit can provide a simple and highly visible display method. If the user is relaxed, the management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a concise display method. For example, the management unit can analyze the user's facial expressions and voice to estimate their emotions. This allows the management unit to adjust the display method of learning progress according to the user's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The management department can select the optimal management method when managing learning progress, taking into account the user's geographical location. For example, the management department can propose the optimal progress management method based on the user's place of residence. The management department can adjust progress management considering the educational resources in the user's region. Furthermore, the management department can optimize progress management by considering the culture and customs of the user's region. For example, the management department can select the optimal progress management method based on the user's geographical location. In this way, the management department can provide the optimal progress management method by taking geographical location into consideration.

[0108] The management department can analyze users' social media activity and propose management methods when managing learning progress. For example, the management department can propose progress management methods based on users' interests and preferences on social media. The management department can adjust progress management considering the time users spend on social media. Furthermore, the management department can analyze the content of users' social media interactions and provide the optimal progress management method. For example, the management department can propose progress management methods based on users' social media activity. In this way, the management department can provide the optimal progress management method by analyzing social media activity.

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

[0110] The analysis unit can estimate the user's learning style and adjust the analysis method based on that estimated style. For example, visual learners can be provided with analysis results that make extensive use of diagrams and graphs. Auditory learners can have additional audio explanations. Furthermore, practical learners can be provided with analysis results that include concrete examples and practice problems. In this way, the analysis unit can adjust the analysis method according to the user's learning style and provide more effective learning support.

[0111] The generation unit can set learning goals for the user and adjust the quiz content based on those goals. For example, a user aiming to pass a specific exam can be given many questions related to that exam. A user aiming to acquire a specific skill can be given practical questions related to that skill. Furthermore, users with long-term learning goals can be provided with quizzes that gradually increase in difficulty. In this way, the generation unit can adjust the quiz content according to the user's learning goals and provide effective learning support.

[0112] The evaluation unit can refer to the user's learning history and adjust the evaluation criteria based on past learning content. For example, it can relax the evaluation criteria for questions related to areas the user previously struggled with. Conversely, it can tighten the evaluation criteria for questions related to areas the user excels at. It can also assess the user's progress based on their past learning history and provide feedback that reflects that progress. In this way, the evaluation unit can adjust the evaluation criteria based on the user's learning history and provide an appropriate evaluation.

[0113] The service provider can adjust learning plans to suit the user's learning environment. For example, for users who have difficulty studying in a quiet environment, it can provide a learning plan that allows for short, focused study sessions. Conversely, for users who are in an environment where they can study for extended periods, it can provide a detailed learning plan. Furthermore, for users who study while on the go, it can provide a learning plan that can be used on a mobile device. In this way, the service provider can adjust learning plans according to the user's learning environment and provide effective learning support.

[0114] The management department can compare a user's learning progress with other users and provide relative evaluations. For example, if a user is behind other users with the same learning goals, additional learning resources can be provided. Conversely, if a user is progressing quickly, a more challenging learning plan can be offered. Relative evaluations can also be provided by comparing users in the same region or age group. This allows the management department to compare a user's learning progress with other users and provide relative evaluations.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that focuses on the essentials. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions and improve visibility.

[0116] The generation unit can estimate the user's emotions and adjust the difficulty of the quiz based on those emotions. For example, if the user is stressed, it can present an easy quiz. If the user is relaxed, it can present a more difficult quiz. Furthermore, if the user is focused, it can present a quiz of appropriate difficulty. In this way, the generation unit can adjust the quiz difficulty according to the user's emotions and provide appropriate learning.

[0117] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is stressed, the evaluation criteria can be relaxed. If the user is relaxed, stricter evaluation criteria can be applied. Furthermore, if the user is focused, moderate evaluation criteria can be set. This allows the evaluation unit to adjust the evaluation criteria according to the user's emotions and provide an appropriate evaluation.

[0118] The system can estimate the user's emotions and adjust the content of the learning plan based on those emotions. For example, if the user is stressed, the content of the learning plan can be reduced. If the user is relaxed, the content of the learning plan can be enhanced. Furthermore, if the user is focused, the content of the learning plan can be optimized. In this way, the system can adjust the content of the learning plan according to the user's emotions and provide appropriate learning.

[0119] The management department can estimate the user's emotions and adjust the learning progress management method based on those estimates. For example, if the user is stressed, progress management can be relaxed. If the user is relaxed, detailed progress management can be implemented. Conversely, if the user is focused, strict progress management can be implemented. In this way, the management department can adjust the learning progress management method according to the user's emotions and provide appropriate management.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The analysis unit analyzes the document. The analysis unit uses natural language processing technology to analyze the content of the document and extract important points and keywords. For example, it extracts frequently occurring words and keywords within the document to identify important information. It can also analyze the structure of the document and understand the relationships between headings and paragraphs. Step 2: The generation unit generates quizzes based on the analysis performed by the analysis unit. The generation unit automatically generates multiple-choice and open-ended quizzes and adjusts the difficulty level according to the user's ability. For example, it can generate quizzes of appropriate difficulty based on the user's past quiz answer history. It can also adjust the level of detail of the quiz based on the importance of the document. Step 3: The evaluation unit assesses the user's understanding based on the quiz generated by the generation unit. The evaluation unit analyzes the user's quiz answers and evaluates the level of understanding based on the correct answer rate and response time. The evaluation unit analyzes the user's response patterns to improve the accuracy of the evaluation. For example, it analyzes the consistency of the user's answers and the types of incorrect answers to evaluate the level of understanding. It is also possible to perform the evaluation while considering the user's attribute information. Step 4: The delivery unit provides a learning plan based on the level of understanding assessed by the evaluation unit. The delivery unit learns the user's reading comprehension level and answer patterns, and provides an individually optimized learning plan and additional quizzes. It selects the optimal learning plan by referring to the user's past learning history. For example, it suggests effective learning methods based on the user's past test results and study time. It can also customize the learning plan based on the user's current lifestyle. Step 5: The management department manages learning progress based on the learning plan provided by the delivery department. The management department monitors learning progress and analyzes comprehension and memory retention levels for each period. They visualize learning progress and provide feedback to the user. For example, they display learning progress as graphs or charts, providing it in a visually easy-to-understand format. They can also provide real-time feedback and recommend areas for improvement or additional learning resources.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the analysis unit, generation unit, evaluation unit, provision unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the content of a document and extracts important points and keywords. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a quiz based on the analyzed content. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's quiz answers and evaluates the level of understanding. The provision unit is implemented by the control unit 46A of the smart device 14 and provides individually optimized learning plans and additional quizzes. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors learning progress and analyzes the level of understanding and memory retention. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the analysis unit, generation unit, evaluation unit, provision unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214, which analyzes the content of a document and extracts important points and keywords. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a quiz based on the analyzed content. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's quiz answers and evaluates their level of understanding. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides individually optimized learning plans and additional quizzes. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors learning progress and analyzes the level of understanding and memory retention. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the analysis unit, generation unit, evaluation unit, provision unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314, which analyzes the content of a document and extracts important points and keywords. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a quiz based on the analyzed content. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's quiz answers and evaluates their level of understanding. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides individually optimized learning plans and additional quizzes. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors learning progress and analyzes the level of understanding and memory retention. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the analysis unit, generation unit, evaluation unit, provision unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414, which analyzes the content of a document and extracts important points and keywords. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates a quiz based on the analyzed content. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's quiz answers and evaluates their level of understanding. The provision unit is implemented by the control unit 46A of the robot 414, which provides individually optimized learning plans and additional quizzes. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, which monitors learning progress and analyzes the level of understanding and memory retention. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0175] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) The analysis unit analyzes the document, A generation unit that generates a quiz based on the content analyzed by the analysis unit, An evaluation unit that evaluates the user's level of understanding based on the quiz generated by the generation unit, A provisioning unit that provides a learning plan based on the level of understanding evaluated by the evaluation unit, The system includes a management unit that manages learning progress based on the learning plan provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The evaluation unit, The system analyzes users' quiz responses and evaluates their level of understanding based on their accuracy rate and response time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It learns the user's reading comprehension level and answer patterns, and provides individually optimized learning plans and additional quizzes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, It monitors learning progress, analyzes comprehension and memory retention levels over time, and provides visualized data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Provide real-time feedback and recommend areas for improvement and additional learning resources. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, It will be provided as an application usable on smartphones and PCs, making learning possible anywhere. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the depth of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Based on the document's content, prioritize analyzing the most important parts. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Depending on the genre and theme of the document, different analysis algorithms are applied. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing documents, the system improves the accuracy of the analysis by referencing the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing documents, the analysis prioritization is determined based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the quiz difficulty based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating quizzes, adjust the level of detail based on the importance of the document. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating quizzes, different quiz generation algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and adjusts the quiz format based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating quizzes, the system improves quiz accuracy by referencing the user's past quiz answer history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating quizzes, adjust the order of the quizzes based on the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When users answer quizzes, we analyze their answer patterns to improve the accuracy of our evaluations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When answering the quiz, evaluation will be performed taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When answering the quiz, the evaluation will take into account the geographical distribution of the users. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When answering quiz questions, refer to relevant literature to improve the accuracy of your evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a learning plan, the system selects the most suitable plan by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a learning plan, customize the plan based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing a learning plan, the system selects the most suitable plan by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing a learning plan, we analyze the user's social media activity and propose a plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, It estimates the user's emotions and adjusts the learning progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, When managing learning progress, the system selects the optimal management method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When managing learning progress, customize the management method based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, When managing learning progress, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, When managing learning progress, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the document, A generation unit that generates a quiz based on the content analyzed by the analysis unit, An evaluation unit that evaluates the user's level of understanding based on the quiz generated by the generation unit, A provisioning unit that provides a learning plan based on the level of understanding evaluated by the evaluation unit, The system includes a management unit that manages learning progress based on the learning plan provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The evaluation unit described above, The system analyzes users' quiz responses and evaluates their level of understanding based on their accuracy rate and response time. The system according to feature 1.

3. The aforementioned supply unit is, It learns the user's reading comprehension level and answer patterns, and provides individually optimized learning plans and additional quizzes. The system according to feature 1.

4. The aforementioned management department, It monitors learning progress, analyzes comprehension and memory retention levels over time, and provides visualized data. The system according to feature 1.

5. The aforementioned management department, Provide real-time feedback and recommend areas for improvement and additional learning resources. The system according to feature 1.

6. The aforementioned management department, It will be provided as an application usable on smartphones and PCs, making learning possible anywhere. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the depth of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, Based on the document's content, prioritize analyzing the most important parts. The system according to feature 1.

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